Toxifying Language and the Genocidal Process in Rwanda

University of Nevada, Reno

“One would think Satan has invaded the place”1: Toxifying Language and the

Genocidal Process in Rwanda

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Political Science

Holly Marie Scala

Dr. Robert Ostergard/Thesis Advisor

May, 2020

1 This quote is taken from an April 2, 1994 RTLM transcript.

Abstract This thesis seeks to specify empirical differences between two types of rhetoric thought to contribute to the onset of genocide: dehumanization and toxification. It utilizes radio transcripts from the Rwandan Genocide to test two propositions: that toxification and dehumanization are empirically distinguishable, and that toxification contributes to the onset and/or intensification of killings in a genocidal context. Results indicate that there are empirically demonstrable differences between dehumanization and toxification, but toxification does not contribute to the onset or intensification of genocide. Instead, the

Rwandan case indicates toxification may be utilized as an attempt to motivate latent perpetrators to participate and justify the actions of those already participating in the genocide, as well as to attempt to maintain power in the face of perceived loss. This thesis contributes to the literature on dehumanization and the uses of language in genocide.

Dedication For the victims of the Rwandan Genocide, known and unknown. I will persist in shedding light upon the tragedies that unfolded in Rwanda and elsewhere, and I will implore those around me to learn about genocide and mass atrocity. Your suffering shall not be in vain.

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Acknowledgements I wish to express my deepest gratitude to the individuals and institutions who helped me complete this project. These include my committee chair and advisor, Dr. Robert Ostergard, who was extremely helpful and patient with me during this process, and the other members of my committee, Dr. Ian Hartshorn, who was vital in the early stages of developing a topic and question, Dr. Amy Pason, and Dr. Leah Windsor, my external committee members who provided me with ample support and feedback. I am also grateful for the Director of Graduate Studies in the Political Science department, Dr. Xiaoyu Pu, the rest of the Political Science department staff and faculty, and the members of my cohort, who were always curious about and supportive of this project. Thank you to Michael Graf and Augustin Mutemberezi. Michael connected me with Augustin Mutemberezi, who generously translated my Kinyarwanda results. I am also indebted to Dr. Steven Wilson, who speedily converted my thousands of pages of transcripts into text files, Dr. Matthew Krain, who provided me with thoughtful feedback and encouragement at the International Studies Association, West Conference, and to the Genocide Archive of Rwanda, the Montreal Institute for Genocide and Human Rights Studies, and the University of Texas at Austin for making RTLM transcripts available for use. Thank you to Max for supporting me throughout the entire process. I could not have completed this without your love and encouragement.

Table of Contents Toxifying Language and the Genocidal Process in Rwanda ...... 1 Existing Genocide Literature ...... 2 Defining Genocide ...... 2 Predictors and Early Warnings ...... 3 Ideology and Language ...... 15 Dehumanization and the Social Psychology of Perpetrators ...... 19 A Call for Further Specification: Toxification ...... 23 Toxification Merits Further Research ...... 26 Research Design ...... 27 Data and Analysis Methodology ...... 28 Case Selection: Rwanda 1994...... 29 Why Rwanda? ...... 29 Background on Rwanda: A History of Discrimination ...... 32 Continued Discrimination, Armed Conflict, and Destabilization ...... 32 Early Triggering Factors...... 33 Instability and Exclusionary Ideology ...... 36 Final Triggering Incident and Openings in Political Opportunity Structures ...... 37 Data Collection ...... 38 Data Analysis ...... 40 Results of Linguistic Analysis of RTLM Transcripts...... 45 Assigning Topic Names to Latent Topic Models ...... 48 English Topic Models ...... 49 Urgent Threat Construction ...... 49 Tutsis as Oppressors ...... 49 Political Communication ...... 50 Instillation of Fear ...... 50 Equation of Enemy with Tutsi Identity ...... 51 Toxic to Ideal ...... 52 Toxic to Self ...... 53 Critique of Government Institutions ...... 54 Virtueltalk as Legitimation ...... 54 Construction of a Dangerous and Ubiquitous Enemy ...... 55 Kinyarwanda Topic Models ...... 55 English Transcript Results ...... 61 Dehumanizing and Toxifying Language ...... 61 Power Language ...... 66 Anger Language ...... 68 v

Causal Language ...... 70 Urgent Threat Construction ...... 72 Tutsis as Oppressors ...... 73 Instillation of Fear ...... 75 Equation of Enemy with Tutsi Identity ...... 76 Toxic to Ideal ...... 78 Toxic to Self ...... 80 Critique of Government Institutions ...... 82 Virtueltalk as Legitimation ...... 84 Construction of Dangerous and Ubiquitous Enemy ...... 86 Kinyarwanda Topics ...... 87 Conflict News ...... 87 Toxic to Ideal ...... 88 Tutsis as Oppressors ...... 89 Breadth of Threat ...... 90 Locations Under Threat ...... 91 State of the Conflict ...... 92 Enemy Infiltration ...... 93 Scope of the Conflict ...... 94 Rwandan Identity ...... 95 Identity Dichotomy: Us versus Them ...... 96 Discussion ...... 97 Key Findings ...... 97 Rhetoric Preceding and During the Early Phase of Genocide ...... 100 Rhetoric During the Deadliest Phase of Genocide ...... 102 Rhetoric During Later Stages of Genocide...... 103 Implications for Theory of Toxification ...... 105 Should We Disregard Toxification? ...... 108 Limitations of the Study...... 108 Conclusion ...... 109 Appendices ...... 120

List of Tables Table 1: Theoretical Differences Between Toxification and Dehumanization ______25 Table 2: Project Propositions ______28 Table 3: English Transcript Topics and Topic Words ______47 Table 4: Kinyarwanda Transcripts Topics and Topic Words (From Translations) ____ 48 Table 5: Descriptive Statistics for English Transcript Topics and Constructs ______60 Table 6: Descriptive Statistics for Kinyarwanda Transcript Constructs ______61

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List of Figures

Figure 1: Indicators for the United Nation’s Tenth Risk Factor (Genocide-specific) ...... 11 Figure 2: Kinyarwanda Topic: Tutsis as Oppressors ...... 57 Figure 3: English Topic: Tutsis as Oppressors ...... 57 Figure 4: Kinyarwanda Topic: Rwandan Identity...... 58 Figure 5: Kinyarwanda Topic: Identity Dichotomy ...... 58 Figure 6: English Topic: Toxic to Self ...... 59 Figure 7: English Topic: Virtueltalk as Legitimation ...... 59 Figure 8: Combined Results from Theory-based Secondary Dictionary ...... 63 Figure 9: Results from Theory-based Secondary Dictionary for “Toxification” ...... 63 Figure 10: Toxifying Language: Pre/Early Genocide...... 64 Figure 11: Toxifying Language: Mid/Late Genocide ...... 64 Figure 12: Results from Theory-based Secondary Dictionary for “Dehumanization” ...... 65 Figure 13: Dehumanizing Language: Pre/Early Genocide ...... 65 Figure 14: Dehumanizing Language: Mid/Late Genocide ...... 66 Figure 15: Internal Dictionary Results for “Power” ...... 67 Figure 16: Internal Dictionary Results for “Power”: Pre/Early Genocide ...... 67 Figure 17: Internal Dictionary Results for “Power”: Mid/Late Genocide...... 68 Figure 18: Internal Dictionary Results for “Anger” ...... 69 Figure 19: Internal Dictionary Results for “Anger”: Pre/Early Genocide ...... 69 Figure 20: Internal Dictionary Results for “Anger”: Mid/Late Genocide ...... 70 Figure 21: Internal Dictionary Results for “Causal ...... 71 Figure 22: Internal Dictionary Results for “Causal: Pre/Early Genocide ...... 71 Figure 23: Internal Dictionary Results for “Causal: Mid/Late Genocide ...... 72 Figure 24: Urgent Threat Construction: Full Data Range ...... 72 Figure 25: Urgent Threat Construction: Pre/Early Phase ...... 73 Figure 26: Urgent Threat Construction: Mid/Late Phase ...... 73 Figure 27: Tutsis as Oppressors: Full Data Range ...... 74 Figure 28: Tutsis as Oppressors: Full Data Range: Pre/Early Phase ...... 74 Figure 29: Tutsis as Oppressors: Full Data Range: Mid/Late Phase ...... 75 Figure 30: Instillation of Fear: Full Data Range ...... 75 Figure 31: Instillation of Fear: Pre/Early Phase ...... 76 Figure 32: Instillation of Fear: Mid/Late Phase ...... 76 Figure 33: Equation of Enemy with Tutsi Identity: Full Data Range ...... 77 Figure 34: Equation of Enemy with Tutsi Identity: Pre/Early Phase...... 77 Figure 35: Equation of Enemy with Tutsi Identity: Mid/Late Phase ...... 78 Figure 36: Toxic to Ideal: Full Data Range ...... 79 Figure 37: Toxic to Ideal: Pre/Early Phase ...... 79 Figure 38: Toxic to Ideal: Mid/Late Phase ...... 80 Figure 39: Toxic to Idea: Final Two Months ...... 80 Figure 40: Toxic to Self: Full Data Range ...... 81 Figure 41: Toxic to Self: Pre/Early Phase ...... 81 Figure 42: Toxic to Self: Mid/Late Phase ...... 82 Figure 43: Toxic to Self: Final Two Months ...... 82 Figure 44: Critique of Government Institutions: Full Data Range...... 83 Figure 45: Critique of Government Institutions: Pre/Early Phase ...... 83 Figure 46: Critique of Government Institutions: Mid/Late Phase ...... 84 viii

Figure 47: Virtueltalk as Legitimation: Full Data Range ...... 84 Figure 48: Virtueltalk as Legitimation: Pre/Early Phase ...... 85 Figure 49: Virtueltalk as Legitimation: Mid/Late Phase ...... 85 Figure 50: Virtueltalk: Final Two Months ...... 86 Figure 51: Construction of Dangerous and Ubiquitous Enemy: Full Data Range ...... 86 Figure 52: Construction of Dangerous and Ubiquitous Enemy: Pre/Early Phase ...... 87 Figure 53: Construction of Dangerous and Ubiquitous Enemy: Mid/Late Phase ...... 87 Figure 54: Conflict News: Full Data Range...... 88 Figure 55: Toxic to Ideal: Full Data Range ...... 89 Figure 56: Tutsis as Oppressors: Full Data Range ...... 90 Figure 57: Breadth of Threat: Full Data Range ...... 91 Figure 58: Locations Under Threat: Full Data Range ...... 92 Figure 59: State of Conflict: Full Data Range ...... 93 Figure 60: Enemy Infiltration: Full Data Range...... 94 Figure 61: Scope of Conflict: Full Data Range ...... 95 Figure 62: Rwandan Identity: Full Data Range ...... 95 Figure 63: Identity Dichotomy: Full Data Range ...... 96

Toxifying Language and the Genocidal Process in Rwanda

Modern genocide continues to perplex and engage scholars seeking to understand a seemingly irrational phenomenon: one group of a species destroying, or attempting to destroy, another group of the same species. Literature on genocide has evolved since the coining of the term in 1948, and continues to expand into a variety of disciplines, and empirical approaches. In this project, I seek to understand the subtleties of language used during the period preceding and during a genocide. Reacting to a term coined in 2015,

“toxification”, which is both distinct from dehumanization and purportedly serves as a more precise indicator of an approaching genocide, I ask the following question: does toxification follow dehumanization as a further stage of the genocidal process? Utilizing the Rwandan Genocide of 1994 as an applied case study, I test two propositions through computational linguistic analyses and qualitative examination of a corpus of radio transcripts from Rwanda:

P1: Toxification and dehumanization are empirically distinguishable phenomena.

P2: Toxification may contribute to the onset and/or intensification of killing in a genocidal context.

While I find support for the first proposition, I find that toxification fails to indicate an impending genocide in Rwanda, and instead increases significantly following the most severe waves of killing. This project contributes to the literature on genocide by illuminating how the subtleties of language prior to and during genocide affect audiences’ propensity to participate in violence.

Existing Genocide Literature

The following provides an overview of fundamental genocide scholarship including definitional arguments, warning signs and predictors, and the role of language and ideology.

Defining Genocide

The naming of genocide has yet to reach its hundredth birthday, and the international community’s handling of the 1948 United Nations Convention on the Prevention and

Punishment of the Crime of Genocide is still, at times, awkward and confused. The UN

Convention serves several primary purposes. First, it deems genocide a crime under international law, and specifies that genocide can be committed during times of war or peace. Second, it defined the bounds of the punishable act, which are predicated upon the notion of the perpetrating group’s intent to destroy, in whole or in part, a “national, ethnical, racial or religious group” by means of

● Killing members of a group;

● Causing serious bodily or mental harm to members of the group;

● Deliberately inflicting on the group conditions of life calculated to bring about its

physical destruction in whole or in part;

● Imposing measures intended to prevent births within a group;

● Forcibly transferring children of the group to another group

The third purpose of the Convention specifies the acts which are punishable under international law (Jones, 2011, p. 13). This definition has spawned many controversies due to ambiguity in much of the language, such as “serious bodily or mental harm” 3

(Jones 2011, p. 14). Others have taken issue with the requirement of proof of “intent to destroy”, which can interfere with prosecuting the crime.

Genocide is often redefined in any scholarly article across disciplines due to these concerns. As some scholars point out, there “is no general agreement as to a non-legal definition of genocide” (Hulsizer & Woolf, 2005, p. 101). Barbara Harff (2003), for example, contends that the UN’s definition lacks inclusion of political groups in its list of possible targeted groups, and therefore, includes such groups in her definition, leading her to coin the term “politicide”. Other scholars point to the vagueness of non-lethal actions that comprise legal genocide in the genocide convention. Ulfelder and Valentino

(2008), mention that punishable actions such as “causing serious bodily or mental harm” or “forcibly transferring children of the group to another” are seemingly difficult to identify, especially when coupled with the necessity of identifiable “intent” from the perpetrators (p. 2). Such difficulties have led scholars to utilize other terms for their own research, such as Harff’s “politicide”, or Ulfelder, Valentino and the Political Instability

Task Force’s “mass killings”, which they contend encompasses most instances of recognized genocides. For the purposes of this project, the United Nations legal definition will be used, despite its limitations.

Predictors and Early Warnings

The phenomenon of genocide has been studied across disciplines including Social

Psychology, Anthropology, Political Science, and Philosophy. Social Psychology’s contribution to the genocide literature primarily centers on discussion of collective pathological narcissism, greed, fear, and humiliation (Jones 2011, chap. 10). 4

Anthropological perspectives generally focus on ethnicity and ethnic relations, modernity as a predictor of genocide, imperialism, nationalism, or genocide as a culmination of a

“continuum of violence”, a term that Nancy-Scheper Hughes and Philippe Bourgois coined (Jones 2011, chap. 11; Scheper-Hughes & Bourgois, 2004). International

Relations and Political Science dispositions are often rooted in conceptions of power, the changing nature of war, or proof of the futility of prohibition regimes (Jones 2011, chap.

12). Although many of the predictors discussed below belong to the Political Science literature, the discussion of dehumanization is more interdisciplinary in nature, given that it entails processes that are studied across political science, sociology, psychology, and anthropology.

Genocide literature in political science regarding causality has evolved from what

Scott Straus termed the “First Generation” of scholarship, which primarily utilizes the

Nazi Holocaust as a classic case to develop theoretical insights (Straus, 2012, p. 546).

These insights center on concepts of scapegoating, intergroup hatred, totalitarian leaders, and power concentration. Given that scholars have been exposed to various other genocides compared to those operating within the “First Generation” framework, both due to the coining of the term “genocide” and subsequent reconceptualization of previous genocides through the new theoretical and legal framework, the literature has evolved to account for the variation across genocides and attempt to identify patterns in the phenomena that occur preceding genocide. The evolution of the literature has resulted in theories geared towards historical, political, and ideological patterns, the interrelated dynamics between genocide and war, and strategic/rational choice approaches to perpetrators’ decision to commit genocide amongst other strategies of control ( Chirot & 5

McCauley, 2006; Hulsizer & Woolf, 2005; Straus, 2012, pp. 546–551). It should be noted, however, that genocide is a respectively rare phenomenon, one that does not easily lend itself definitively causal language. Thus, genocide scholarship has exhibited a trend towards recognizing precipitating factors and indicators of its impending occurrence, in lieu of probing for and asserting causality.

One primary account in the new wave of scholarship asserts that the primary predictor of impending genocide is civil war (Krain, 1997). Krain contends that events leading to openings in the political opportunity structure, or opportunities for challenges to the status quo to manifest, are important signals of the onset of genocide. Such openings include civil wars, interstate wars, extraconstitutional changes, and decolonization (Krain, 1997, p. 355). The “window of opportunity” for the onset of genocide widens if at least two of these variables exists in tandem. For example, a state that recently gained independence from its colonial power and then experienced a civil war would exhibit a wider window of opportunity for mass violence to manifest than a state that had only recently gained independence. Krain’s argument echoes Hulsizer and

Woolf, who propose a similar situational risk factor, a “destabilizing crisis”, which can consist of economic, political, or detrimental remnants of a war (Hulsizer & Woolf, 2005, p. 104). While they acknowledge that some regions, states, or groups of individuals are living in perpetual hardship, they remind readers that destabilizing crises are those that

“result in unanticipated difficult life conditions in populations unaccustomed to such difficulty”, and these are the conditions that make states ripe for genocide (Hulsizer &

Woolf, 2005, p. 105). 6

Barbara Harff’s seminal studies commissioned by the Clinton Administration to examine the variables that predict genocide mostly align with Krain’s claims, with a few exceptions. Her seven variables for genocide prediction include a risk for future instability (regime instability or internal war), state-led discrimination, occurrence of previous genocides, ethnically polarized political elite, exclusionary ideology, autocratic characteristics, and lack of trade openness (Harff, 2010). In her initial paper presenting the predicting variables, she seconds Krain’s assertion that disruptions or openings to political opportunities increase the likelihood of a regime’s choice to carry out mass killings (Harff 2003, p. 62). However, Harff imputes much more importance on the dynamics of ethnic division and ideology than Krain. Whereas it may come across as though Krain understates the importance of ethnicity and Harff overstates it, it is possible their respective emphasis resulted from nuanced operationalization and measurement differences. For example, Krain concluded that ethnic fractionalization is not an important precondition for genocide, a variable captured by the amount of asymmetry between ethnic groups in a state2 (Krain, 1997, p. 341). Harff, on the other hand, examines how ethnically polarized the political elites are. She operationalizes this variable by identifying whether the ethnic majority or minority dominates the political system, and whether or not that domination is contested (Harff does not explicitly state how she measures “contestation”) (Harff, 2010, p. 521). Thus, the acceptance of

2 Krain operationalizes this variable by squaring the proportion of the entire population each ethnic group makes up, adding those squared proportions together, then subtracting it from 1. High scores indicate that many ethnic groups make up relatively equal proportions of the population, while low scores indicate a majority ethnicity with only a small minority. 7

“ethnicity” (fractionalization or polarization) as a significant predictor of genocide depends on which variable the scholar chooses to accept.

Similarly, Krain determines that “power concentration”, the “degree to which power resides in the institutions of the government” is an unimportant predictor of the onset of genocide (Krain, 1997, p. 347). However, Harff’s measure of “elite ethnic polarization” might provide for a more nuanced variable in terms of identifying where the majority of power within a state lies. While shedding light onto how centralized power is in a pre-genocidal context, it seems more intuitive to identify in whose hands the power exists in terms of ethnic identification. Hulsizer and Woolf support a variant of this view as well, contending that authoritarian leaders with high power concentration are the secondary (following destabilizing crises) situational factor that make a state ripe for genocide (Hulsizer & Woolf, 2005, p. 104). Thus, authoritarian leaders losing power concentration are more likely to commit genocide than those who have a strong grasp on power, an explanation that illuminates both structural and rational choice reasons for genocide onset (Ulfelder & Valentino, 2008, p. 17) .

Ulfelder and Valentino (2008) propose a set of predictors that combine aspects of both Krain and Harff, with a couple additions as well. They contend that governments are more likely to perpetrate mass killings when they:

● Are fighting insurgencies or other violent threats to their power (attempted coups,

etc.)

● Have exhibited a history of discriminatory behavior

● Exhibit propensities to misuse public goods or wealth 8

● Have had a recent high turnover in leadership

● Are less economically integrated into the world economy, and

● Have a recent history of nonviolent contestation of the current regime or political

status quo (Ulfelder and Valentino 2008, pp. 14–17).

Their findings indicate a rationalist view of mass killings, wherein governments purposefully use violence against civilians as a tactic against political challengers, and as an instrument to respond to perceived or actual threats to regime stability (Ulfelder &

Valentino, 2008, pp. 17-18). The significant variables in their model suggest that rulers are more likely to resort to mass violence amidst armed conflicts or major protests, phenomena which indicate a threat to regime stability. Additionally, rulers are more likely to commit mass killings when they lack the capacity and political security necessary to respond by other means, or if have demonstrated a history of exclusion or discrimination (Ulfelder & Valentino, 2008, p. 16). One major limitation in their structural model is that their it is designed to test the risk factors controlling for the onset of civil war or an adverse regime change within the previous five years, which may obscure dynamics related to their control variables. Additionally, their model assumes that there is not currently a state-sponsored mass killing (Ulfelder & Valentino, 2008, p.

20). It also does not apply to countries that are currently stable, or to states that are already involved in mass killings. However, for states that are already involved in mass killings, these risk factors should have been taken into consideration at an earlier stage, and the presence of mass killings indicates a failure to recognize the onset of genocide or mass killings. 9

Similar to Ulfelder and Valentino, Harff takes into account whether or not that power is contested, which has proven to be an essential indication of dissatisfaction concentrated in a select group of people, rather than a select group of government institutions or the populace overall (Harff, 2003). Thus, these scholars overlap in their analysis of genocide predictors in terms of political opportunity structures being opened or disrupted (civil war, high leader turnover, internal war or regime instability). Harff,

Ulfelder and Valentino agree on two other predictors, including lack of trade openness and discriminatory history. While Harff omits discussion of misuse of public goods or wealth, her variable “current regime type”, which she measures on a scale of “autocratic to democratic” by referencing the well-known Polity index, could contain latent information regarding trends in how autocratic and democratic nations provide public goods and use state money.

Risk factors in the existing scholarship have made impressions on institutions such as the United Nations, something that is evident upon examination of their Framework of

Analysis for Atrocity Crimes report (2018, pp. 9-19). The UN proposes the following risk factors for atrocity crimes:

● Situations of armed conflict or instability

● A record of serious violations of international human rights and humanitarian law

● Weak state structures

● Motives or incentives (apparent aims or reasons that may justify violence against

protected groups)

● Capacity to commit atrocities 10

● Lack of mitigating factors

● Preparatory action, and

● Triggering factors (circumstances that may worsen current conditions, such as

incitement of hate propaganda, sudden deployment of state forces, etc.).

Because this document addresses atrocity crimes beyond genocide, including crimes against humanity and war crimes, the framework posits two additional risk factors specific to genocide: intergroup tensions or discrimination against protected groups, and signs of intent to destroy in whole or in part a protected group (United Nations, 2018, pp.

18-19). The UN framework contains a comprehensive set of indicators for each risk factor. The indicator that the present project will focus on is 10.1 of the genocide-specific risk factors (signs of intent to destroy in whole or in part a protected group) (see Figure

1). This indicator includes “official documents, political manifests, media records, or any other documentation through which a direct intent, or incitement, to target a protected group is revealed, or can be inferred in a way that the implicit message could reasonably lead to acts of destruction against that group” (United Nations, 2018).

Figure 1: Indicators for the United Nation’s Tenth Risk Factor (Genocide-specific)

Gregory Stanton’s 2004 article decrying the failure of the international community to recognize the warning signs of genocide led to his proposition of eight “stages” of genocide, which he expanded to ten stages in 2016. He claims that genocide, from start to finish progresses through the following stages (Stanton, 2016):

● Classification of groups in a culture

● Symbolization of those classifications and attribution of symbols as distinguishing

characteristics of groups (the Jewish yellow star during the Nazi Holocaust)

● Discrimination or denial of rights and power to out-groups

● Dehumanization of the out-groups through depersonalizing behavior and rhetoric 12

● Organization of the forces intending to carry out the genocide (organizing state

militias, arms purchasing)

● Polarization of groups by extremists to elicit motivation to harm or kill

● Preparation in a more official manner, such as euphemistically defining the goal

of extermination (“Final Solution”, “purification”)

● Persecution in a more official manner (making death lists, segregation into

ghettoes, etc.)

● Extermination of the target group

● Denial or attempted erasure of evidence

Last, and in a similar vein to Stanton, Woolf and Hulsizer propose a 7-stage model that predictably leads to genocide or mass violence. They remind their reader of the messiness of such a process, and caveat their model with a warning that some stages may overlap or blend (Hulsizer & Woolf, 2005, p. 114). Their model roughly follows these seven stages:

• Normative stereotypes from the in-group towards the out-group develop. Acts of

violence or discrimination may occur, which, depending on the cultural

acceptance of violence, may or may not be condoned and accepted.

• Groups or cultures proceed further down the path of violence. Members of out-

group experience institutional discrimination, denial of services, exclusion from

social groups or organizations, and stunted social mobility. Negative insignia is

associated with the group. 13

• Out-group loses basic civil rights, including the right to vote, or legal citizenship.

Dehumanizing rhetoric disseminated by leaders and propaganda increases,

especially that which portrays the out-group as threatening.

• Increased isolation of out-group, either by means of ghettos, deportation, or

forced expulsion of a group from a region. Employment of euphemistic language

to assuage the cognitive dissonance of the in-group or observers.

• Deprivation of basic human rights, like education, access to food or water, etc. On

a psychological level, moral exclusion quickens, which renders it acceptable for

the out-group to receive such treatment for the perpetrators or bystanders.

• Members of out-group face impending genocide. Impunity signals to perpetrators

that they can proceed.

• Denial that genocide happened.

Evidently, these stages parallel some of Stanton’s stages, particularly the final stage: denial.

Whereas Harff, Krain, Valentino and Ulfelder, and the United Nations’ risk factors center primarily on structural, state-specific characteristics, Stanton’s, Hulsizer’s and

Woolf’s stages encapsulate several of the more social-psychological phenomena that are essential in the manifestation of genocide. Furthermore, several of the ten stages illuminate the codependency of social-psychological phenomena with structural characteristics. Specifically, the “dehumanization” stage is crucial for predictors such as exclusionary ideology (Harff, 2003), state-led discrimination (Harff, 2003), or a record of human rights violations (United Nations, 2018). This is due in part to the dependency of 14 moral disengagement on part of the perpetrators and bystanders from the out-group.

Stanton, however, in his conception of dehumanization, misses some of its nuance, such as rhetoric aiming to construct the enemy as unjust, threatening, or polluting (Stanton,

2016, p. 1). He mentions dehumanizing strategies, such as calling people animal names or likening them to disease, but the context within which these tactics are used, and the ideological implications of such tactics are underspecified.

While the dehumanization stage is not necessarily more important than any other stage, it is a stage that is ripe for intervention, given its temporal place in the genocidal process. For example, during the crucial stage of espousing dehumanizing rhetoric or information, there are ample opportunities to sabotage the effect that such communication has. These include both abstract and tangible strategies such as delegitimizing the authority or trustworthiness of those leading dehumanizing campaigns, encouraging a variety of providers of information so as to prevent an information monopoly, or encouraging skepticism of the valorization of violence (Virtuetalk) and the idea that violence against an out-group is inevitable and just (Maynard & Benesch, 2016). More practically, international actors can attempt to jam radios and inciteful news outlets, prohibit genocidal leaders from international travel, or freeze their foreign assets

(Stanton, 2016, p. 1). Thus, further research into dehumanization, specifically, is essential to recognizing the onset of genocide as well as sophisticating understanding of intervention opportunities.

Ideology and Language

While some scholars may take for granted the assumption that language can reveal goals, intentions, and mechanisms of persuasion, it is not obvious to all why a genocide scholar would want to examine concrete evidence of rhetoric from a genocidal context. This choice indicates one of the intersections of communications studies and international studies. From a theoretical aspect, many scholars have advanced the argument that language is a primary means of persuasion (Bourdieu, 1979; Charteris-

Black, 2005; Donohue, 2012; Dowell, Kaltner, Windsor, & Windsor, 2017; Haslam,

Loughnan, & Sun, 2011; Merry, 2016; Newman, 1959; Savage, 2007; Steuter & Wills,

2009; Stollznow, 2013; Straus, 2007). More absolutely, rhetoric and persuasion are inseparable due to the rhetorical mechanisms that help shape how an audience views the world around them (Charteris-Black 2005, 8). As an example, Charteris-Black cites the importance of metaphor as a rhetorical mechanism, noting the ability of metaphor to

“reconstruct” causal relationships in an argument (2005, pp. 10-11).

It is possible that neither resources nor advantageous political situations suffice to instigate social movements or the collective action that accompanies them (Williams &

Pfeiffer, 2017) 3. Rather, cognitive frames are necessary due to their heuristic ability to aid people in their understanding of objects, events, and situations (Williams & Pfeiffer,

2017, p. 75). The venue and manner wherein these cognitive frames are constructed is also important. Maynard points to the variety of possible communication pathways,

3 Pfeiffer and Williams propose a sociological definition of genocide, which they see as a state-sponsored social movement with an aim to destroy a specific group (Williams & Pfeiffer, 2017, p. 74).

16 including everyday social interactions, media-run propaganda programs, short-term calls for violence, for example, from a single speech or text, and escalatory radio and TV broadcasts in his call for more focused attention on the subtleties of communication

(Maynard, 2014, p. 827). This project will speak to the nuance of escalatory radio propaganda.

Maynard also connects language and rhetoric to what he terms “epistemic authorities”, individuals whom people trust to provide them with political knowledge, pointing to the tendency of humans operating under bounded rationality to seek influence and information by those they view as trustworthy (Maynard, 2014, p. 827). In the

Rwandan case, for example, this may be in the form of RTLM broadcasters. He critiques genocide scholars for being insensitive to the descriptive and semantic details of language, which he contends help to create frames and ideological meanings that contribute to justificatory ideology (Maynard 2014, p. 825). Maynard also argues that

“saturation” of specific messages and recurring lexicons increase the probability that audiences will internalize a message (Maynard 2014, pp. 828-831). That is, when epistemic authorities disseminate a message so constantly that it becomes common parlance (i.e. Tutsis as “cockroaches”), the substantive issues decrease in importance and the construction of the “enemy” becomes the most important facet of the message. This is consistent with Donohue’s Identity Trap, a phenomenon that occurs when groups get caught in a rhetorical chasm wherein “linguistic conventions combine to establish a social context” that encourages dichotomous identity construction between groups, eventually leading to the rationalization of conflict escalation (Donohue 2012, p. 13). He notes, 17

“language creates a context that moves people to action”, and thus warrants observation, especially in a pre-genocidal context” (Donohue, 2012, p. 28).

Other scholarship highlighting the importance of language in its connection to violent behavior are more empirical. Stollznow’s analysis of linguistic and behavioral representations of dehumanization illuminates that dehumanizing metaphors and epithets are common in intergroup conflict situations, and that they serve a “licensing” purpose to mistreat an outgroup (Stollznow, 2013, p. 194). Euphemisms contribute to violence intensification by morally excluding the target group and obscuring the meaning of calls to action, and even violent actions themselves (Hulsizer & Woolf, 2005, p. 107). Thus, empirical support of language’s role in motivating and legitimating violence or violent dispositions towards an out-group is not lacking (Buckels & Trapnell, 2013; Luna, 2018;

Savage, 2013; Stollznow, 2013). Buckels and Trapnell assert that disgust, which, in their experiment, is communicated through images, helps to both derogate the out-group and moralize the in-group, thereby normalizing aggressive or violent behavior towards the out-group (Buckels & Trapnell, 2013, p. 771). This is due to the intimate connection between disgust and the animal/human dichotomy. Disgust is often triggered by images of garbage, corpses, death, sickness, sex, aggression, bodily waste, and hunger. Thus, the associated emotions triggered by these images facilitates the moral exclusion of the target from the perpetrator’s moral sphere (Buckels & Trapnell, 2013, p. 772).

Consideration of ideology and the language that helps shapes it is crucial to narrowing the explanatory scope of risk factors and warnings signs for genocide down from the structural level to the group and individual level. As Benesch and Maynard 18 explain in “Dangerous Speech and Dangerous Ideology”, “the balance between the availability and power of ideological justifications and restraints in a particular context determines the overall ideological impetus towards violence - and this is crucial for thinking about how to counter such justifications" (2016, p. 75). Similarly, Savage notes that “the language that we use to describe action shapes our understanding of the meaning of that action. There is a world of difference between the murder of a human being and the excision of a cancerous growth, between ‘massacre’ and ‘cleansing’” (Savage, 2007, pp. 424–425). Similar to Savage, Pfeiffer and Williams assert that frames transform deviant behavior into acceptable, and even expected, behavior (Williams & Pfeiffer,

2017, p. 76).

Studying speech and ideology in tandem is crucial, as they are mutually constitutive: ideologies are both constructed through speech, and also provide a blueprint for speech meant to further a certain ideology (Maynard & Benesch, 2016, p. 74). Maynard and

Benesch propose a dual model of ideological study, one that includes both context and content, two essential interpretive considerations in the current project. Four features of context make speech more influential:

1) the authority, charisma, or power of the speaker

2) the different characteristics of the audience that make it more or less prone to

internalize the speaker’s message

3) the vitriol of socio-historical context

4) the means of dissemination in addition to whether or not there is an informational

monopoly in a society (Maynard & Benesch, 2016, pp. 77–78). 19

In terms of content, Benesch and Maynard suggest six powerful attributes that promote justification of violence or killing. These are dehumanization, guilt attribution, threat construction, destruction of alternative courses of action, Virtueltalk (valorization of fighting and militancy), and future bias (ensuring that actions will positively bias perpetrator group in the future) (Maynard & Benesch, 2016, pp. 80–85). Several of these characteristics, such as threat construction and destruction of alternative courses of action, overlap with Savage’s examination of biopolitical discourse. He is especially interested in the ways in which metaphors pertaining to hygiene, purity, and contamination contribute to the many processes of dehumanization, which is more concretized in Neilsen’s “toxification” (explained subsequently) (Savage, 2007, p. 404).

Dehumanization and the Social Psychology of Perpetrators

Scholarship on dehumanization frequently found across disciplines including

Social Psychology, Neuroscience, Sociology, Political Science and International

Relations. Social Psychology scholarship on dehumanization often focuses on the types of dehumanization as well as the psychological mechanisms that undergird it. Regardless of the discipline, most scholarship on dehumanization propose that it offers great utility for those planning, committing, or observing genocide. As Savage opines,

“dehumanization is a necessary element in genocide in the modern era in that it is a strategy that resolves the problems that genocidal action poses for perpetrators”, usually problems akin to cognitive dissonance (2013, p. 142). While some have critiqued this argument due to its inherent assumption that humans do not enjoy killing, or that they 20 have a cognitive problem with it, it is still an important and common consideration found in genocide scholarship, and thus, merits attention (Lang, 2010; Savage, 2013).

Psychology’s treatment of dehumanization often surrounds the concept of moral disengagement, wherein “normal moral restraints on violence are weakened” (Haslam,

2006, p. 254). A decade prior to this article, “moral disengagement” appeared in the

Social Psychology literature in Bandura, Barbaranelli, Caprara, and Pastorelli’s seminal study (1996). While these scholars conducted a laboratory experiment, which compromises external validity, their foundational study helped spearhead future research into moral disengagement. Their findings indicate that there are two primary mechanisms at work during moral disengagement: conversion of harmful acts to moral ones by euphemistic or valorizing language, and vilification of targets or victims by blaming them for harm or by dehumanizing them (Bandura et al. 1996, p. 369). In genocidal contexts, this occurs on both an individual level and a societal or group-level. On an individual level, perpetrators or passive bystanders of genocide can be free self-condemnation due to the exclusion of the out-group from their perceived moral sphere. On a group level, moral disengagement occurs primarily due to the association of superior values with one’s own in-group (infrahumanization), and the denial of these, or any positive, values from the out-group (Haslam, 2006, p. 255).

Psychology’s work on dehumanization also focuses on specification and further categorization of this process. For example, a commonly accepted distinction within dehumanization literature in general is that of mechanistic and animalistic dehumanization (Haslam, 2006). This distinction stems from Haslam’s proposition of 21

“two senses of humanness”, defined by two sets of human features: those that distinguish humans from other types of animals (“Uniquely Human”, or “UH”), and those that represent the “core” of human nature (“Human Nature” or “HN”) (Haslam, 2006, p. 256).

Haslam contends that when individuals portray out-groups as amoral, irrational, or childlike, they are engaging in “animalistic dehumanization”, because they are denying the out-group traits that render them unique from other animals (denying their UH characteristics). Alternatively, when individuals portray out-groups as shallow, rigid, cold, or lacking agency, they are engaging in “mechanistic dehumanization”, because they are denying individuals characteristics that represent characteristics that are generally deemed desirable and inherent to human nature (denying their HN characteristics) (Haslam, 2006, pp. 256–257). Such constructions of out-groups help to legitimate violence against the group, lest the perpetrators or observers experience internal qualms or social pressure regarding their actions. However, Haslam may have taken for granted the in-group/out-group dichotomy, prompting a critique that the “moral obligation” to refrain or partake in violence is more complex than an “in” versus “out” dichotomy (Savage, 2013, p. 143). Savage claims that the construction of an out-group is better conceived of as a continuum, or a degree of shared values versus lack of values.

Rather, the relation between dehumanization’s “motivating” and “legitimating” functions is constructed by the “presence or absence of a narrative of threat” (Savage, 2013, p.

142). The current project tangentially argues in Savage’s favor by highlighting different stages of dehumanization, including the newly coined “toxification”, which this project proposes may indicate a turning point in the genocidal process. This problematizing 22 resides outside of the scope of psychology and will be discussed further in the resulting paper.

An essential, comprehensive account of dehumanization’s role in genocide in the

International Relations literature relates the “background concept” of dehumanization to the role of ideology (Maynard & Benesch, 2016). This connection is essential due to the presence of “ideology” in the list of genocide predictors previously discussed. Maynard asserts that there are three causal pathways ideology can take in terms of genocide. First, it can help generate desire and motivation to commit violence. Second, it can contribute to the creation of legitimizing dispositions, which in turn, make the committing of violence tolerable. Last, it can operate retroactively to rationalize past acts of violence.

He conjoins these three causal pathways together into what he terms “ideological justification” (Maynard, 2014, p. 829). Maynard then suggests a list of six mechanisms that contribute to the success of justificatory ideology. Dehumanization is the first mechanism on this list, demonstrating its necessity to motivate, legitimize, and rationalize violence (Maynard 2014, p. 830).

While dehumanization is inarguably an essential component of pre-genocidal contexts, some scholars have pointed out its insufficiency in predicting genocide. Haslam points out that dehumanization arises in a variety of domains, including scholarship on gender and pornography, medicine, disability, technology, and education (Haslam 2006, pp. 252–54). Thus, while it is a necessary component of genocide, it is also a phenomenon that occurs in countless non-genocidal contexts. Despite this limitation, there is almost unanimous agreement that dehumanization occurs as a stage in the process 23 of genocide, and thus warrants further study and specification. Additionally, one should not view this as a limitation, but as further reason to examine specific types or extensions of dehumanization, and distinguish them from the kind that is found “everyday” or in non-genocidal contexts. Formalizing further nuances within the literature on dehumanization as it relates to genocide will allow scholars and policymakers to be more sensitive to the early predictors of genocide.

A Call for Further Specification: Toxification

The perceived necessity for a distinct concept from dehumanization stems from

Rhiannon Neilsen’s proposition that dehumanization is an insufficient warning sign for genocide due to its presence in non-genocidal contexts in addition to genocidal contexts, as Haslam highlights. Neilsen claims that her coined term, “toxification”, has the potential to serve as a more precise indicator of genocide because toxifying rhetoric implies the necessity to kill, not just the permission to kill (Neilsen, 2015, p. 83).

Toxifying language, in contrast to dehumanizing language, elicits urgency in action because, unless the in-group submits to destruction or harm from the target group, the target group must be destroyed. This proposition is consistent with an idea in Maynard and Benesch’s 2016 article, which attributes higher influential power on audiences to speech that eliminates alternatives to violence against a target group, thereby necessitating destruction of the future victims (Maynard & Benesch, 2016), as well as

Savage’s contention that biopolitical discourse communicates higher threat levels from the out-group (2007). 24

Neilsen defines “toxification” as the “cognitive perception of the target group as fundamentally lethal to the furtherance of the perpetrators’ survival and society”

(Neilsen, 2015, p. 86). This conception of subjects as malignant leads to perpetrators acting under the belief that the target group must be destroyed, or their own group will be destroyed or harmed by the target group. She contrasts this with dehumanization, whereby rhetoric relies on the exclusion of the subjects from the perpetrator’s “sphere of moral standing” by stripping subjects of their identity as “human”. She still asserts, however, that toxification includes dehumanization, a theoretical claim that she fails to develop more clearly (Neilsen & Williams, 2016, p. 7). The two strains of toxification embedded in her definition are toxic-to-the-ideal (threatening to the in-group’s society, ideology, or idea of a utopian world) and toxic-to-the-self (dangerous to oneself as an individual, emphasizing kill or be killed logic) (Neilsen & Williams, 2016, p. 5). See

Table 1 for basic theoretical distinctions between toxification and dehumanization, per

Neilsen’s 2015 and 2016 articles as well as Luna’s 2018 operationalization of dehumanization4.

Neilsen’s call for a novel concept rests on the claim that existing early warning frameworks for genocide “fail to distinguish between an allowable action, and a requisite action” (Neilsen, 2015, p. 86). The ability to recognize rhetoric urging for necessary and imminent action may provide external actors with a more sensitive warning sign for genocide. Neilsen admits that the concept in abstract is not absent from genocide studies but calls for formalization and operationalization of the concept, and testing of possible

4 Table 1 provides initial theoretical distinctions from Neilsen and Luna, which I will propose to expand and specify in the discussion section. 25 hypotheses embedded therein (Neilsen, 2015, p. 83). Indeed, the unnamed concept is present in most scholarship on genocide, especially in discussion of dehumanization

(Chirot & McCauley, 2006; Desforges, 1999; Donohue, 2012; Kelman, 2018; Power,

2002b; Savage, 2007, 2013; Straus, 2007). Closely examining language is of utmost importance because the ideology bolstered or constructed by dehumanizing language serves three main functions relating to genocide: it legitimizes, justifies, and motivates action (Savage, 2007, p. 405). The current project seeks to identify specific indicators of toxification in order to identify what constitutes toxification instead of dehumanization

(even though the two concepts heavily overlap). Its abstract nature will ideally become more concrete, and the processual nature of rhetoric across time will begin to take more specific shape. Thus, while toxification, similar to dehumanization, may not be a necessary and sufficient predictor or cause of genocide, it merits further examination, and more importantly, empirical distinction using real-time data as a case of genocide unfolds, so that scholars can continue to develop it as a concept and identifiable indicator in the genocidal process.

Table 1: Theoretical Differences Between Toxification and Dehumanization

Dehumanization Toxification

• Violent action is allowable • Violent action is required

• Comparison to animals or machines • Comparison to cancer, disease, and

• Does not always entail urgency or harm-causing or lethal elements

immanent calls to action • Entails urgency in action and zero-sum

rhetoric (kill or be killed) 26

• Signals aggressors’ control over the • Appeal to audience’s fear and survival

victim instincts as opposed to mere moral

• Includes identity hierarchies and exclusion of out-group

dichotomies • Less benign, more definitive language

• More benign

• Exists in non-genocidal contexts

Toxification Merits Further Research

The proposed study aims to contribute to Neilsen’s concept of toxification by operationalizing it through content analysis of a single case study in order to demonstrate its purported empirical distinction from dehumanization. Despite the fact that Neilsen proposed her concept several years ago (in 2015), there is a deficiency of testing and application of it. One study was conducted involving interviews with ex-Khmer Rouge cadres probing for their attribution of toxification as a motivating and legitimizing factor for their acts during the Cambodian Genocide (Neilsen & Williams, 2016). Neilsen and

Williams interviewed the ex-cadres with a set of questions intended to identify whether toxifying discourse legitimized and/or motivated their killings during the Cambodian

Genocide, per Neilsen’s use of Maynard’s justificatory ideology in the formulation of her theory (Maynard 2014, p. 829). This study was problematic for several reasons. First,

Neilsen and Williams expected the interviewees to self-report why they killed people.

Second, they conducted the interviews more than 30 years after the actions were committed, which raises concerns about the accuracy of memory of a tumultuous time in 27 one’s life. The post-hoc approach also fails to capture the temporal nature of toxification inherent to Neilsen’s theory, which argues that toxification occurs prior to genocidal onset, not as a post-hoc justification. Last, it was unclear how Neilsen and Williams operationalized “toxification”. Therefore, the interview questions as well as the coding of the answers are ambiguous and nontransparent.

While Neilsen did provide two examples of toxification in her initial article, she claims that her examples operationalize the nascent concept (Neilsen, 2015, p. 87).

Examples, however, do not operationalize a concept. Rather, the formulation of indicators, observations that one measures, helps to operationalize concepts (Gerring,

1999). The present project addresses, at minimum, the third concern: operationalization.

It will look for the preponderance of evidence of whether toxification is a more precise indicator of genocide by examining whether toxification contributes to the onset or intensification of killing. Implicit in this probe is the formulation of indicators of toxification as well as initial measurements of the indicators garnered from the case study data at hand. Formulating indicators provides scholars with a more tangible avenue to apply and probe for toxification in the period leading up to and during genocide.

Research Design

This project utilizes the genocide in Rwanda to seek to formalize toxification and demonstrate its empirical distinction from dehumanization. It is a single case study that aims to provide scholars an example of the application of the concept as well as a test into the provided propositions (see Table 2). While single case studies are limited in generalizability, they provide a fruitful starting point for testing embryonic theory 28

(George & Bennett, 2005). The material for analysis in this project is a sample of digitized radio transcripts from the Genocide Archive of Rwanda, University of Texas’s

Rwandan Genocide Collection, and the Montreal Institute of Genocide and Human

Rights Studies. Through computational linguistic analysis of the aforementioned corpus, the outcome will serve to either support or fail to support the two propositions. I expect to see a shift from dehumanizing rhetoric to toxifying rhetoric directly preceding the onset of genocide on April 6, as well as preceding intense cycles of killing throughout the genocide.

Table 2: Project Propositions

P1: Toxification and dehumanization are empirically distinguishable phenomena.

P2: Toxification contributes to the onset and/or intensification of killing in a genocidal context.

Data and Analysis Methodology

Given the focus on language and rhetoric, I analyze transcripts from Rwanda’s primary extremist media outlet associated with the 1994 genocide: Radio Télévision

Libre des Mille Collines, subsequently referred to as RTLM. The RTLM began broadcasting on Rwandan radio in July 1993, nine months before the beginning of the genocide. Its early messages regarded the perceived failing of the Arusha Accords and general political discontent with the failure to implement transitional institutions ordered by the Arusha Accords (Roozen & Shulman, 2014). In the months leading up to the genocide, messaging shifted from general political discontent to more nationalistic 29 sentiments on part of Hutu extremists. Broadcasts began to characterize Tutsis and the

RPF as a security and political threat to Hutu power, intensifying even more after the assassination of Rwandan President Juvénal Habyarimana and Burundian President

Cyprien Ntaryamira on April 6, 1994 (Roozen & Shulman, 2014).

The computational linguistic analysis of the aforementioned corpus illuminates measurable patterns in rhetoric and saliency of topics, which uncovers two things: 1) whether there is an empirically identifiable difference between dehumanization and toxification, and 2) if there is a difference, whether movement between the two types of language are associated with the onset or intensification of killings. More specifically, the presence or absence of rhetorical shifts provide evidence supporting or failing to support the project’s propositions: that toxification contributes to the onset and/or intensification of killing in a genocidal context.

Case Selection: Rwanda 1994

Why Rwanda?

The decision to examine the genocide in Rwanda stems from several reasons.

First, there is almost universal consensus that the intensity, prevalence, and prominence of rhetoric, especially media rhetoric, fueled the genocide in Rwanda. Many genocide scholars attribute the efficiency and ruthlessness of the genocide to media, specifically the prolific RTLM (Desforges, 1999; Donohue, 2012; Kimani, 2007; Power, 2002;

Roozen & Shulman, 2014; Stanton, 2004; Yanagizawa-drott, 2014). Both the small geographic size and relatively efficient radio communication infrastructure, among a host of structural and ideological factors such as history of discrimination, exclusionary 30 ideology, recent (or in this case, concurrent) civil war, and a polarized political elite, contributed to the swift mass mobilization against a perceived threat, the RPF or Tutsis more broadly (Mann, 2005, p. 431). This disposition was institutionally supported by the

International Criminal Tribunal on Rwanda, which determined the guilty verdict of several print and radio journalists for inciting genocide (Straus, 2007, p. 610). While the importance of media and radio is not a unanimous view within genocide scholarship

(Straus, 2019), most scholars at least allude to the salience of inciteful rhetoric communicated through the Hutu publication Kangura and the RTLM5.

Existing empirical work also supports the notion that the broadcasts did, indeed, lead to higher levels of violence during the genocide (Yanagizawa-drott 2014; Kimani

2007). However, most studies examine geo-spatial distribution of radios or radio access in relation to individual participation in genocide, glossing over the mechanisms operating within rhetoric. This project contributes to the literature on the relationship between hate radio and genocidal killings by probing deeper into the subtle qualities of language instead of the distribution of radios across the country. In short, Rwanda possesses intrinsic importance given the widespread foundational research on the importance of the radio in catalyzing and fueling the genocide (Gerring, 2017, p. 42).

A second reason Rwanda is a suitable case for this study is because Neilsen

(2015) utilized this case in her seminar article defining toxification, specifically to define the “toxic to self” strain of toxification. However, despite the fact that temporality is

5 Straus concluded that the sole reason for the onset of the Rwandan Genocide could not be solely attributed to hate radio, but also acknowledged that the radio emboldened hardliners and bolstered the effects of face-to-face perpetrator mobilization (Straus, 2007, p. 631).

31 crucial to her hypotheses regarding when toxification occurs, she merely offers second- hand examples of words or phrases from the Rwandan Genocide out of the context of time. Thus, examining how language unfolds in real time in a case that Neilsen used to coin her nascent term allows me to conduct a diagnostic test of the very case that was used to initially demonstrate toxification’s utility, but with a more precise, time-sensitive approach (Gerring, 2017, p. 98).

Finally, the contemporary nature of the Rwandan Genocide affords me with two advantages for utilizing this case. First, it offers my findings increased external validity due to the almost universal nature of verbal and print media throughout the world. Had I chosen a case that occurred closer to the coining of the word genocide in 1948, I would have examined more obscure, possibly elite-centric documents that were not intended for mass consumption. Given the profile of participants in the genocide, average Rwandan citizens who were not historically mobilized to fight, it is crucial to utilize data that appeals to this profile, not to a small group of elites or militants. Furthermore, because only 66% of Rwandans were literate, radio was known as the most prolific medium through which Rwandans got consumed their news (Desforges, 1999, p. 58). By exploring mass media in the Rwandan case, my findings will be more valid for other cases of genocide where mass media is considered to have played a role in intensifying the conflict (i.e. Bosnia or Myanmar). Second, the Rwandan case appeals to the convenience of widely accessible, digitized data that is easily convertible to text files, providing for a humble yet significant sample size.

Background on Rwanda: A History of Discrimination

The genocide in Rwanda occurred very quickly, in just 3 months, from April to

July 1994 (Jones, 2011). The victims were primarily of the Tutsi ethnic group, although many moderate Hutus were also killed during the genocide. It is crucial to dispel pseudo- theories of resurging primitive rivalries between two African tribes as an impetus for

Hutu/Tutsi conflict. In fact, Hutus, Tutsis, and the smallest group in Rwanda, the Twa, had mostly peacefully coexisted in the space that now makes up Rwanda before

European colonization (Mann, 2005, p. 432). Despite coexistence in a clan model system where Tutsis governed the central core and Hutus governed independent polities, physical and occupational heuristic distinctions eventually emerged to distinguish Hutus from

Tutsis more overtly. For example, Hutus began to be associated with shorter, stockier physical appearances indicative of a Bantu lineage, and crop growing subsistence occupations. Tutsis, on the other hand, were associated with appearing taller and thinner indicative of Hamitic lineage, and cattle-herding occupations. These distinctions eventually morphed into Tutsi association with a “ruling” role over the Hutus, who became associated with “the ruled” (Mann, 2005, p. 432). This was the foundation upon which Belgian colonizers during the 1920’s through early 1960’s deepened and formalized what may have previously served as mere heuristic distinctions between the two primary groups in Rwanda.

Continued Discrimination, Armed Conflict, and Destabilization

Most scholars trace the roots of the genocide to the deep ethnic divisions derivative of German and Belgian colonial legacy, through which Tutsis enjoyed 33 exceptional privilege in education and bureaucracy and ethnicity was officially externalized through the institution of mandatory identity cards (Jones, 2011, p. 350,

Mann, 2005, p. 433). Capitalizing on the existing divides, Belgian favoritism of Tutsis widened the power gap between the two groups, increasing resentment and decreasing the likelihood of future power sharing. More concretely, restrictions on educational, political, and legal access eliminated the social mobility that Hutus had previously enjoyed (Mann, 2005, p. 433). When the Tutsi population began to spearhead anti- colonial movements following World War II, Belgian colonizers began to shift their partiality towards the Hutus, whom they saw as more complicit with their own regime and preferences (Jones, 2011, p. 350). This shift, as well as increased bouts of violence, led to the fleeing of many Tutsis to neighboring Zaire, Tanzania, and Uganda, where many Tutsis organized and conducted rebel attacks into Rwanda, a movement that culminated in the 1987 formation of the Rwanda Patriotic Front (RPF), and three years later, an invasion into Rwanda from Uganda in October 1990. Although this invasion ended in defeat, it signaled the beginning of resurging RPF operations into Rwanda that intensified over the next three years, which in turn, increased French military support of the Hutu government (Mann, 2005, p. 439).

Early Triggering Factors

Most scholars agree that the rapid increase in quantity and intensity of ideological hate propaganda occurred following the resurgence of the RPF (Desforges, 1999; Jones,

2011c; Power, 2002b; Roozen & Shulman, 2014; Yanagizawa-drott, 2014). For example, the radical Kangura magazine, which was sponsored heavily by Habyarimana’s own 34 party, the MRND, emerged following the October 1990 RPF attack (Desforges, 1999, p.

58). Among a host of visual and written propaganda, one seminal piece of publication was the “Hutu 10 Commandments”. The 10 Commandments instructed Hutus not to sleep with Tutsis, lest they accept their own identity as traitorous, to stop having mercy on the Tutsis, and attempt to take control of every strategic point of power (Mann, 2005, p. 443, Power, 2002, p. 338). Similarly, after the national radio, Radio Rwanda, was forced to tame its pro-Habyarimana propaganda and promise to disseminate more non- partisan content, Hutu hardliners created the RTLM, culminating in the first broadcasts in

July 1993 (Desforges, 1999, p. 59). However, this apparent break-up between the newly party-agnostic Radio Rwanda and openly partisan RTLM was not as clean as it appeared.

Many broadcasters on the RTLM merely joined from their previous posts at Radio

Rwanda, including editor-in-chief, Gaspard Gahigi. The RTLM also allegedly coopted a significant amount of equipment from Radio Rwanda, and was even afforded three hours of airtime on the same frequency as Radio Rwanda when they were not broadcasting.

Aside from the obvious cross-fertilization between the two radio stations, the founders and hosts on the RTLM were intimately involved with the MRND party,

Habyarimana’s northern Hutu-dominated party. Many founders and supporters had even deeper personal connections to Habyarimana. For example, Félicien Kabuga was the father of Habyarimana’s son’s wife, and he was one of the station’s primary financial supporters (Desforges, 1999, p. 59). In addition to holding obvious allegiance to

Habyarimana’s regime and his MRND party, RTLM pundits also came from directly from several government ministries or the MRND party organization specifically. These individuals included Augustin Ngirabatware, the Minister of Planning, André Ntagerura, 35 the Minister of Telecommunications, Pasteur Musabe, who directed the Banque

Continetale Africaine, Kantano Habimana, previously employed by the party organ of the

MRND, and Gaspard Gahigi, an MRND central committee member (Desforges, 1999, p.

59).

While the MRND was disproportionately represented among founders, supporters, and broadcasters of the RTLM, the CDR party, a staunchly anti-Tutsi party, was also represented and they collaborated with the MRND cohort despite their disposition that the MRND party was too willing to share power with the RPF

(Desforges, 1999, p. 59). Thus, the voices on the radio were not mere radio personalities.

They were operatives of a well-oiled propaganda machine that began with espousing their party-centric grievances and increasingly focused on attributing blame, identifying a cruel enemy, and calling on others to act in the fight against them. This signals the

“Identity Trap” phenomenon, wherein substantive issues characterize initial airing of grievances, and then rhetoric morphs to increasingly trap opposing sides into a primarily identity-based framing of grievances (Donohue, 2012). Given the nature of both the

MRND’s and CDR’s anti-Tutsi stances, the motivation to put Tutsis at the center of grievances remained relatively stable throughout the genocide. However, the manner through which broadcasters framed their enemy became more severe over time in accordance with their goals to mobilize and motivate listeners to participate in violence.

External developments, including the arming and organizing of militias, facilitated the unravelling of any semblance of professional, trustworthy media. 36

In 1992, Hutu Power radicals organized a secret military organization called

“Amasasu”, which functioned both as a death squad and as a pressure group urging for more radical military policy in Habyarimana’s government (Mann, 2005, p. 445).

Likewise, the youth wing of the MRND advanced from their traditional roles protecting party meetings and helping to reform rural labor patterns, into a paramilitary group, famously known as the “Interahamwe”, meaning “those who stand together” (Mann,

2005, p. 445). Organization and movement of weapons intensified, with groups like the

“Zero Network” smuggling firearms to Interahamwe and soldiers who were off duty, which was accompanied by the emergence of lists of targets for elimination (Mann, 2005, p. 447).

Instability and Exclusionary Ideology

President Habyarimana’s regime, which was born out of the military coup that overthrew the previous president, Grégoire Kayibanda, did not alleviate the increasingly hostile and deadly tensions between Tutsis and Hutus, and also between moderate and extremist Hutus, a dynamic rooted in the previous power dynamic under Kayibanda’s regime (Mann, 2005, p. 436). The incentive of international development funds to aid

Rwanda’s hurting economy brought together the RPF and Habyarimana’s party, the

MRND, to the negotiating table in Arusha, Tanzania in 1992 (Mann, 2005, p. 439). The

1993 Arusha Accords, which aimed to mediate the civil conflict and guarantee political involvement for the RPF in national politics, failed to prevent the onset of genocide just eight months after they were signed. The regime failed to not only institute interim governmental structures that would be more inclusive of the RPF and exclusive of Hutu 37

Power parties, but the Structural Adjustment Program that came after the accords ravaged family-level economic well-being, thereby increasing resentment towards the regime

(Desforges, 1999, p. 94). This discontent manifested itself in the increasingly hostile party dynamics between the National Revolutionary Movement for Democracy (MRND),

Habyarimana’s own moderate-leaning party, and the Coalition for the Defense of the

Republic (CDR), which attracted and represented radical Hutu Power constituents (Mann,

2005, p. 440). Hutu leaders espoused their exclusionary ideology as early as 1992 through speeches paralleling Tutsis with the Khmer Rouge, naming them the “Black

Khmer”, calling for Tutsi exclusion from the Rwandan Armed Forces, and urging for their complete elimination of Tutsis under the guise of pre-emptive self-defense against a volatile enemy (Power, 2002, p. 339-340).

Final Triggering Incident and Openings in Political Opportunity Structures

The primary catalyzing incident occurred on April 6th, 1994, when moderate Hutu

President Habyarimana’s aircraft was shot down upon returning to Kigali from diplomatic talks in Tanzania. On board and killed in the aircraft was also Burundi’s president Cyprien Ntaryamira and several Hutu Power members (Mann, 2005, p. 450).

The unknown nature6 of the culprit of Habyarimana’s assassination was a topic that all sides of the conflict coopted to fuel out-group resentment. An undisputed result of the assassination is the power vacuum that emerged after Habyarimana’s assassination, vulnerable to cooptation by fractionalized radical Hutus, moderate Hutus, the military,

6 In the early 2000’s, unofficial reports surfaced from alleged RPF officers’ confessions that the attack was ordered by Paul Kagame. However, he ardently denies this claim (Mann, 2005, p. 450). 38 and RPF troops. Hutu hardliner Colonel Théoneste Bagosora, who served as the director of the army staff under Habyarimana, moved to occupy this absent leader role militarily, quickly arranging for the assassination of Agathe Uwilingiyimana, the titular, moderate

Prime Minister of the short-lived interim government, along with a swath of other moderate Hutus (Power, 2002, pp. 330–332). Following these killings, RPF soldiers stationed in Kigali also ramped up attacks against the remainder of the Hutu regime, cloaking the violence in a confusing overlap of civil war and genocide (Power, 2002, p.

333). By July 4th, 1994, between 500,000 and 1 million people7 had been killed (Jones,

2011, p. 346).

Data Collection

Most RTLM transcripts, which were collected and translated for the International

Criminal Tribunal on Rwanda in the few years following the end of the genocide, are hosted by three primary repositories: the Genocide Archive of Rwanda, the Concordia

University’s Montreal Institute for Genocide and Human Rights Studies, and the

University of Texas at Austin digital library collection. These repositories contain transcripts in the original Kinyarwanda, which break into occasional original French sections, French translations of Kinyarwanda transcripts, and English translations, which were often translated from French.

Initially, I acquired 104 English transcript files from all three repositories. The overlap in distinct English transcripts from each source resulted in a sample size of 87 distinct transcripts. After combining distinct transcripts from single days, the sample size

7 This paper utilizes Stam and Davenport’s aggregated estimates of deaths. See “Data Collection”. 39 decreased to 56 English translated transcripts for 56 days. The transcripts range from

November 24, 1993 to July 3rd, 1994. I cleaned text conversions of PDF documents by correcting misspelled words, removing additional punctuation, excising unofficial discourse8. Before analyzing the text files, I removed five stop words to improve analysis validity9.

The original number of separate Kinyarwanda transcript files was 388. After cleaning text file conversions of PDF files10, combining multiple transcripts from a single day into one text file, and removing any incorrectly labeled files (files that were not actually in Kinyarwanda), my corpus included 139 unique transcripts for single days beginning on October 20, 1993 and ending on July 3, 1994. I conducted one round of stop word removal from the Kinyarwanda transcripts prior to their analysis. To partially compensate for the possibility that additional stop words remained, I removed the names of the speaker when they only served to identify who was speaking (ex: “Gaspard

Gahigi:”). I also removed portions of transcripts that broke into French, given that the data analysis tool cannot process multiple languages simultaneously.

To examine the relationship between shifts in language and deaths per day, I utilized Stam and Davenport’s Reported and Estimated Start Dates and Lethality dataset,

8 Unofficial discourse includes document details, translator agreements, external summaries or analysis, and translator notes within the document, such as “speaker laughs”. 9 These words are “is”, “the”, “and”, “a”, and “an”. These are five of the most common stop words in English, thereby justifying their removal. 10 Due to the number of pages in the corpus, I did not conduct a word-by-word comparison of PDF-to-text files for each transcript. Instead, I removed common sources of noise, such as document details, easily identifiable translator notes, translator agreements, and external summaries, which were often written in French and therefore easily identifiable. 40 which aggregates estimated death counts from five different sources using statistical modeling (Davenport & Stam, 2014).

Data Analysis

To achieve a systematic analysis of RTLM transcripts, I utilized two computational linguistic tools: Linguistic Inquiry and Word Count (LIWC) (Pennebaker,

Booth, Boyd, & Francis, 2015) and Topic Modeling Tool (Newman & Balagopalan,

2018). Initial plans included a tertiary data analysis11 tool that would illuminate grammatical characteristics in the transcripts. However, concerns about disconnect between translated documents’ grammar led to the exclusion of the third tool in lieu of using LIWC, Topic Modeling, and triangulation of the resulting analyses through qualitative examination of selections in the transcripts. I was only able to utilize LIWC for English translated documents, as there is currently no Kinyarwanda calibration for the tool.

The central component of LIWC is word count. LIWC allows the researcher to examine common dimensions of a text by sorting words and word stems into many different categories, including typical linguistic categories, such as prepositions and pronouns, value-oriented categories, such as positive and negative, and even content categories, such as “death”, “social”, or “money” (Windsor, Dowell, & Graesser, 2014, p.

456). LIWC’s internal dictionary is expertly validated, and the processes for its composition include word generation from a corpus of hundreds of thousands of texts to draw from, panel judging, psychometric evaluation, and refinement from a team of

11 Coh-Metrix 41 experts across several disciplines (Pennebaker, Boyd, Jordan, & Blackburn, 2015). When

LIWC reads text files, it examines each “target word”, which is any word that LIWC can read and analyze, and, if the target word belongs to any of LIWC’s internal sub- dictionaries, the target word is sorted into one or several categories12 (Pennebaker, Boyd, et al., 2015, p. 2). Thus, these dictionaries are essentially “off the shelf”, in that the software user does not subjectively assign words into the 90 categories that LIWC contains in its internal dictionary. However, users also have the ability to load alternative dictionaries for LIWC to systemically sort words. There are several open access dictionaries online, and users can also create their own, which, given the youth of toxification as a concept, I opted to do.

Using the literature on dehumanization operationalization in tandem with

Neilsen’s 2015 and 2016 articles, I developed a theory-driven dictionary in addition to

LIWC’s internal dictionary to distinguish between dehumanization and toxification (see

Appendix 1). The generation of the 81-word dictionary is comparable in length to the number of words in several of LIWC’s internal dictionary. The input for “toxification” in the dictionary is based on the two foundational articles proposing the concept of toxification as well as driving theory and literature that influenced the development of the concept (Maynard & Benesch, 2016; Neilsen, 2015; Neilsen & Williams, 2016; Savage,

2007). “Dehumanization” words were selected based on the aforementioned articles, which work to delineate the two concepts, as well as literature on the application and

12 For example, the word “laughed” might be sorted into the following three categories: positive emotion, overall affect verbs, and past language (Pennebaker, Boyd, et al., 2015, p. 2). 42 operationalization of dehumanization (Donohue, 2012; Haslam, 2006; Haslam et al.,

2011; Luna, 2018; Stollznow, 2013).

The Topic Modeling Tool serves as a natural complement to LIWC’s categorization. I utilized this tool for both the English translated corpus and the

Kinyarwanda corpus. Topic models consist of algorithms that “discover” salient themes in a corpus, regardless of the order of words, paragraphs, or documents (Blei, 2012). This method identifies words and assigns them to labels, “such that words that often show up in the same document are more likely to receive the same label” (Enderle, 2017), which I then interpret and name. In addition to the Topic Modeling Tool’s agnosticism towards language (it is possible to model widgets, for example), using this method of analysis

“may yield connections between and within documents that are not obvious to the naked eye” (Blei & Lafferty, 2006, p. 17). Topic Modeling outputs four primary pieces of information:

1) The resulting topics along with a ranked list of documents that most heavily feature the topics

2) The resulting topics along with a list of a specified number of words per topic13

3) A list of documents with a ranked list of the most salient topics, the inverse of the first piece of information

4) A table with proportions of topics represented in each document

13 I selected 20 words per topic and 10 total topics, which are the default settings for the software. 43

Results from the analysis of the Kinyarwanda sample were translated by Augustin

Mutemberezi, a professional translator and native Kinyarwanda speaker, into English.

The process of naming each topic involves a series of steps. First, I brainstormed cohesive names for topics with just the topic words that the tool offered me, out of context of the documents that contain each topic. While this was an important starting point for me to generate immediate connotations for each word, I sought to triangulate my initial reactions to groups of words by looking more closely at where topic words were identified in the documents. Following the initial brainstorm round, I culled the five most prominent documents for each topic (information piece number 1 above) and searched the documents for each topic word. After identifying where the Topic Modeling

Tool was uncovering this hidden structure in each document, I contextualized the topic words based on the full sentences that surrounded each word. The most important component of this stage in the process is looking for instances where many words sorted into a topic occur together, thereby uncovering the structure even more precisely. For example, for the “Critique of Government Institutions” topic, which was still simply

“Topic 0” at this stage, I searched the term, “institutions”, one of the words sorted into this topic by the Topic Modeling Tool. In a March 23rd transcript, this illuminated a dynamic and political grievance that I would not have been able to sense without going back into the data. One set of sentences identified in this search contained five topic words (underlined in example) and more importantly, insight into how the words worked together to form a topic. They read, “delegates condemned the scheming tyranny which the RPF leaders of some political parties were known for and which involved favouring selfish interests to the detriment of their parties and people. This conduct is contrary to 44 democratic ideals and impedes the installation of transitional institutions” (March, 23,

1994). Thus, contextualizing topic words both individually and as a group of words is essential to understanding the full breadth of the topic and obtaining the most appropriate topic name.

Following the analysis of English and Kinyarwanda transcripts through LIWC and Topic Modeling Tool, I aligned data for unique transcript dates with Stam and

Davenport’s deaths-per-day data to visualize patterns in language relative to deaths per day14. The alignment between unique transcript days and deaths-per-day was not perfect, as I did not have a transcript for each day of the genocide where Stam and Davenport had data for deaths-per-day. Furthermore, transcript data begins in October 1993, six months preceding the onset of killings. The pre-genocide transcripts are, however, crucial to this project given the proposition that toxification may lead to the onset of killings in a genocidal context. Variation in available data during the genocide, between deaths-per- day and transcript dates, poses a limitation to future projects aiming to more precisely correlate the two variables or utilize more regression-like methodology.

14 Per Stam and Davenport’s explicit recommendations, I utilized the five-source aggregated data. 45

Results of Linguistic Analysis of RTLM Transcripts

Does toxification lead to the onset or intensification of killing in a genocidal context? Combined results from linguistic analysis through LIWC and Topic Modeling indicate that the relationship is not as straightforward as the propositions suggest. While there are clear shifts in linguistic patterns, including spikes and decreases in linguistic dimensions, they do not represent a clear relationship to the onset or intensification of killings.

All LIWC constructs and Topic Modeling results15 are percentages of total words that the construct or topic makes up in a document (i.e. a score of 2.23 for toxification indicates that 2.23% of all words in the transcript fall into the toxification category)16.

Spaces in graphs indicate missing data due to transcript unavailability.

I will first present the ten topics extracted from the Topic Modeling Tool with respective words for the topics in both English and Kinyarwanda17. I will then present scores for the highest, lowest, and average score of constructs and topics for both the 56

English transcripts and the 139 Kinyarwanda transcripts. Next, I will discuss the relationship between the English and Kinyarwanda topics. I will then display the most important constructs in relation to the number of deaths documented per day. To adequately capture relationships between language constructs and deaths per day, I will

15 All LIWC results will heretofore be referred to as “constructs”, while Topic Models will be referred to as “topics”. 16 Words can also fall into multiple categories. For example, “I” is sorted into “personal pronouns” as well as “I” language. 17 A professional translator translated results from the Kinyarwanda transcripts. Thus, the results from the Kinyarwanda transcripts will be in English. Please see Appendix 2 for the original output from the Kinyarwanda transcripts. 46 display 3-4 graphs per construct: the full range of data (from October

(Kinyarwanda)/November (English) 1993-July 1994), the phase leading up to the genocide and the first month of it (March-April 1994), the middle and late phases of the genocide (April-July 1994), and for a select few graphs indicating more granular patterns late in the genocide, the final two month phase (May 1st – July 4th, 1994). I excluded several constructs from the English transcript analysis due to the relative small percentage of the total word make-up, though they are reported in the descriptive statistics table below. These constructs were “I”, “We”, “You”, “They”, “positive emotion”, “negative emotion”, “anxiety”, “sad”, “difference”, “risk”, “past”, “present”,

“future”, and “time”. Many of the words sorted into these respective constructs appear in the transcripts more saliently in several topics. Thus, I do not omit essential information.

I also omit the English topic Political Communication, given short lengths of transcripts that resulted in spikes in the topic’s spikes.

Table 3: English Transcript Topics and Topic Words

Construction Equation of Urgent Critique of Virtueltalk Tutsis as Political Instillation of of Enemy Toxic to Toxic to Dangerous Threat Government as Oppressors Communication Fear with Tutsi Ideal Self and Construction Institutions Legitimation Identity Ubiquitous Enemy Case cockroach Democracy People Coup Country Killed Minister Armed Guns

minister Hutu Commune Kigali Army Peace General Institutions Fighting Army

UNAMIR Tutsi File Emmanuel Soldiers Arusha Army Republic Soldiers War

parties Hutus Court Person Killings Minister War Parties Kill Fight

Radio Tutsis Case Persons Power Government Soldiers Country Killed Enemy

told Ethnic Law Student Government Negotiations Fighting National Forces Inkotanyi

meeting Inkotanyi prosecution Radio Majority Population People Party People Inyenzi

people People listening Announcement Transitional Parties Rwandan Political Rwanda Time

wife Civilians radio asks Tutsis Power Dallaire Government Children Population

yesterday Man discovered Commune Hutu Rwandan Rwandans Transitional Kagame Country

day Called news Party Hutus People Rwanda Members Kigali Government

RPF Find Mutura18 Political Burundi Kigali Inkotanyi Prime Town Commune

CDR Power public CDR West Rwanda French Broad City Communes

house Airport July MRND Institutions Rwandans Inyenzi Assembly Country Place

place Time late Security Democracy War News Problems Inkotanyi People

attack Problem bodies Kill People Army Radio Situation Radio Security

killed Stay death Hotel Rwanda Soldiers Talking Radio Situation Give

problem Wanted Burundi march Problem Inkotanyi Told People Day Order

work Back aids news Back RPF Yesterday MRND White Back

pr19 things Million announcements Fact radio Country List Good pr

18 Mutura is a commune in the northwestern Gisenyi province of Rwanda. 19 “Pr” also appears in “Construction of Dangerous and Ubiquitous Enemy”, though it likely that the result is due to party names or noise in the data. 48

Table 4: Kinyarwanda Transcripts Topics and Topic Words (From Translations)

Identity Locations Conflict Toxic to Tutsis as Breadth State of the Enemy Scope of Rwandan Dichotomy: under News Ideal Oppressors of Threat Conflict Infiltration Conflict Identity Us versus Threat Them People Minister Inkotanyi Burundi Inkotanyi Rwanda Now Inkotanyi Rwanda Cockroaches

Now Country Rwanda People People Inkotanyi Party Commune People Inkotanyi

Radio FPR20 Cockroaches Ndadaye21 Things People Say Residents Country Army

Because Army Tutsi RTLM Rwanda Country People People Kigali People

Rwanda Words People Radio Kigali Kigali Twagiramungu22 Government French

Kigali Especially someone FPR Minister Residents Country

government Republic Government Republic Rwanda

Hutu residents government Truth

FPR

president

Assigning Topic Names to Latent Topic Models

There are two main reasons why my assignment of topic names to each model is

appropriate. First, the cleaning process of the 56 English transcripts entailed a close

examination of each individual document. Second, the importance and meaning of the

words in each model are most significant in the context of the words they most often

appear with. Thus, my post-hoc examination of relevant transcripts for each topic in

addition to my familiarity with the English transcripts from the cleaning process increase

the appropriateness of my assigned topic models.

20 FPR is the Kinyarwanda acronym for RPF. 21 Ndadaye was the president of Burundi. Tutsi army officers assassinated him in October 1993, an event that is often referred to in RTLM transcripts. 22 This refers to Faustin Twagiramungu, the head of the MDR at the time. He was frequented targeted by the RTLM and other extremist media outlets as being a Tutsi in disguise due to his push for cooperation with the RPF. 49

English Topic Models

Urgent Threat Construction

A significant degree of the urgency in threat appears when broadcasters discuss the whereabouts and plans that the RPF is making. As early as November 1993, broadcasters note that “now that people have been killed and there is no doubt that it is

RPF who killed them” (November 23, 1993). Prominent in transcripts scoring high on this topic are discussions of secret meetings, attack plans, and the speed, cruelty, and efficiency with which the RPF murders innocent victims, such as “after killing him, the

Inyenzi took out his intestines and replaced them with stones” (April 3, 1994). The urgency component derives primarily from broadcasters claiming that the “Tutsi RPF extremists are bent on overthrowing the government using arms and are planning to launch a major attack before Easter 1994” (April 3, 1994), or referencing consecutive days that the enemy is allegedly planning attacks.

Tutsis as Oppressors

This topic highlights Mann’s argument that a crucial component of the Hutu/Tutsi divide centered on competing identities of Hutus as the working class proletariat, and

Tutsis as the imperialistic oppressor (Mann, 2005, p. 431). Transcripts ranking high in this topic highlight rhetoric supporting this argument, with charges of Tutsis wanting to abolish political parties (March 18, 1994) or “enjoying a certain level of well-being”

(December 12, 1993). Allegations towards Tutsis also include wanting to “launch an ethnic war” (March 18, 1994) or being “intent on monopolizing power” (December 12,

1993). Broadcasters frequently reference history by saying things like “the Inkotanyi 50 bombshelled us…they chased us out of our property and compelled us to live as a loss on wastelands” (January 6, 1994). These passages frame Inkotanyi, and often Tutsis more generally, as taking something from Hutus and attempting to take power from the majority of the population.

Political Communication

I excluded political communication from in-depth analysis because its higher scores are attributable merely to short transcripts. Thus, spikes in this language do not reveal significant linguistic patterns. This topic centers on discussion of transitional government institutions and government problems more generally, often referencing the assassination of Ndadaye in Burundi in October 1993 and subsequent violence in the

Mutura commune. In December 1993, broadcasters mourn the “death of innocent peasants who were slaughtered like lambs in Mutura commune” (December 1, 1993), which also appeared in a transcript two days later when broadcasters discuss the public response to the violence. RTLM hosts discuss in great detail Ndadaye’s funeral proceedings, which accounts for the words “death”, “democracy” (Ndadaye’s father’s speech focused on his son’s death in the name of democracy), and “public”.

Instillation of Fear

Transcripts exemplary of this topic include detailed descriptions of enemy transgressions and planned attacks, commune and cellule-level reports of feelings of insecurity, and admonitions to keep radios nearby and listen often. On March 16th, broadcasters warn, “they want to kill important people in this country and wipe them out, especially Hutus” (March 16, 1994). Among goals to terrorize and exterminate Hutus, 51 broadcasters report from the Sake commune that “Inkotanyi have finished to infiltrate

Sake commune in Kibungo at such point that they had started recruiting within their front” (March 16, 1994). The most frequent context wherein this model’s words appear is the lack of security. Broadcasters lament, “there are also other many people for whom security is undermined”, and that things are so scary in Kigali “one would think satan has invaded the place” (April 2, 1994). Rhetoric emphasizes the importance of RTLM’s

“urgent news”, and therefore, they warn, “your radios should not go far from you”

(March 31, 1994). This language also functions to instill in listeners that the RTLM are epistemic authorities, disinhibiting listeners to seek alternative information or news which might be more benign or objective (Maynard & Benesch, 2016).

Equation of Enemy with Tutsi Identity

This topic highlights the explicit and implicit equation of “enemy” with the Tutsi identity. Rhetoric charges Tutsis as “trying to be the only masters in charge” (March 23,

1994), and continuing to refer to the Ndadaye assassination and Tutsi transgressions in

Burundi to serve as a lesson to the dangers of the Tutsis in Rwanda. The significance of military-like words such as “coup”, “army”, and “soldiers” suggests that speakers’ equation of all Tutsis with a military threat serves to legitimize and concretize the threat.

An ethnically identifiable enemy seeking to overthrow a government is much more threatening than an enemy wherein only military factions pose a threat. Words such as

“coup”, especially used to point to the Tutsi coup attempt in Burundi the previous

October signal that Tutsis persist as enemies both abroad and at home. For example, on

March 23, 1994 broadcasters alert listeners that “Tutsis are still bloodthirsty. In fact, they 52 are used to shedding blood and they continue to do so. Today, as they are still planning a coup, it means they still want to shed blood, this time around, on a large scale. The thirst for power and blood for which the Tutsis of Burundi are known has just resurfaced”

(March 23, 1994). Broadcasters expand the “enemy” charge to the entire Tutsi race by quoting sayings such as, “a leopard cannot change its spots”, and drawing parallels between the “putchist Burundian army” (March 23, 194), or Ugandan President

Museveni’s soldiers, “fighting alongside the Inyenzi in our country” (June 10, 1994).

More simply, hosts on the RTLM frequently combine the word “Inkotanyi”, the term that

RPF soldiers, almost exclusively Tutsi, used to refer to themselves, with the word

“Inyenzi”, meaning “cockroach”. One exemplary passage decries the RPF as “a group of

Tutsi extremists who call themselves Inkotanyi but who are really Inyenzi” (April 22,

1994).

Toxic to Ideal

A vein of toxification, toxic to an ideal, entails a conception of the perpetrator group as “toxic to the furtherance of human civilization, the perpetrators’ ideational reality or utopia, or the body politic” (Neilsen, 2015, p. 87). The “ideals” defining this vein of toxification in the Rwandan context are peace, democracy, and Hutus, as the majority, in power. Broadcasters framed Tutsis as toxic to the ideal by charging them with intentionally preventing transitional governmental institutions from being set up

(March 18, 1994) and resuming war in Rwanda, “thereby nullifying the Arusha Peace

Accord” (April 22, 1994). Speakers make frequent claims that the “RPF has indeed the intention to take over power by force of arms”, which broadcasters note is even more 53 crucial of a transgression because the RPF is “coming to take power from leaders representing the people who make up a majority of the population” (April 22, 1994).

Language instills the fear that the ideal, or the Hutu group as listeners know it, will not survive, even if individual Hutus survive and continue to outnumber Tutsis (Chirot &

McCauley, 2006, p. 62).

Toxic to Self

The second vein of toxification is toxic to self, which centers on a “kill before being killed zero-sum logic” (Neilsen, 2015, p. 87). While this can be subtly communicated through notions of filthy, threatening, or disease-ridden animals, such as rats or cockroaches, it is often communicated in RTLM transcripts through explicit reports of enemy transgressions. There are two primary purposes of such communication.

First, it signals to listeners that they could become the next victim. Second, it saturates the audience propaganda aiming to instill a sense of threat in the audience, often playing on historical or geographically contiguous conflict. Broadcasters warn that “Inyenzi

Inkotanyi killed people with small hoes” (June 9, 1994), that the only people “they would not kill [are] one[s] who would be their accomplice in killing” (June 9, 1994), and that

“they want to reduce the number of Hutus – maybe to exterminate them” (June 21, 1994).

RTLM hosts saturate their programs with the word “kill” to communicate to listeners that no Hutu is safe: “they killed Hutu adults, young men, teenagers, children…They killed women, old women, pregnant women and babies. They said that since they had the good luck of being Tutsis, they had to kill the Hutu gorillas” (June 18, 1994). Saturated 54 communication about the universal physical vulnerability of Hutus is a pinnacle component of this strand of toxification.

Critique of Government Institutions

This topic is the most benign of the ten topics. It focuses on general frustrations with temporary Prime Minister Agathe Uwilingiyimana, failure to install transitional government institutions, which most speakers do not even look upon positively, and security problems. Speakers still attribute blame for these frustrations to RPF, both as a party and military force, though it is not often inciteful. Grievances center on the

“tyranny which RPF leaders of some political parties were known for” (March 23, 1994) and the “political maneuvers blocking the instillation of the transition institutions”

(March 18, 1994).

Virtueltalk as Legitimation

Virtueltalk consists of rhetoric that valorizes violence, assigns to it righteous characteristics, and exploits elements of individual psychology by “directing and regulating individuals’ moral emotions, by reconstructing violence as admirable”

(Maynard & Benesch, 2016, p. 84). One week into the genocide, RTLM hosts made universal calls to action, such as, “all Hutus and the entire population should support the

Rwandan Armed Forces” (April 15, 1994), and a month later, “those who have guns should immediately go to these Inkotanyi before they listen to Radio RTLM and flee.

Stand near this place and encircle them and kill them because they are there” (May 16,

1994). Virtueltalk serves to legitimate violence towards victims by diminishing feelings of guilt. Broadcasters remind listeners that it was the “Inyenzi Inkotanyi who shot down 55 that aircraft and killed our father”, and that “no respectable Rwandan is feeling guilty for that action because guerilla fighters are killers” (May 17, 1994).

Construction of a Dangerous and Ubiquitous Enemy

Ubiquity and constant threat serve to create urgency and further legitimize calls to action against the victim. This often includes words connoting secrecy, infiltration, and trickery. For instance, speakers warn that there are people who attend secret meetings at night and “make attack plans targeting certain people, or design plans to facilitate the

Inkotanyi infiltration” (March 10, 1994). They frequently assert that “they will take over the country little by little”, that they “only want to wipe out people in order to rule

Rwanda, to enslave people and colonize it” (March 16, 1994), rendering it necessary that every citizen “should encircle the enemy and fight him” (May 15, 1994). This language fosters an enemy who can exist anywhere, as “there may be an enemy among refugees; they may be afraid that among these refugees there may be people who work for the enemy or the enemy himself” (June 4, 1994).

Kinyarwanda Topic Models

Whereas I had the benefit of contextualizing topic model words in the English transcripts, I was not able to replicate this process for the translated models resulting from the Kinyarwanda transcripts. Thus, I assigned topics from the Kinyarwanda transcripts the same name as the English topics if they shared several words with English topics. This resulted in only two overlapping topics: Toxic to Ideal, which shares

“minister”, “country”, “army”, “government”, “Kigali”, and “RPF” with the English

Toxic to Ideal, and Tutsis as Oppressors, which shares “cockroach”, “Tutsi”, “Hutu”, 56

“people”, and “Inkotanyi” with the English Tutsis as Oppressors. For the remaining eight topics, I aimed to balance specificity and generality. Thus, I strove for the most specific topic name while recognizing the limitations of being less attuned to the context of the groups of topic words. There were several stop words or non-fluencies that I excluded from the topics (see Appendix 2 for original Kinyarwanda results).

In terms of overlapping visual patterns, comparison of both language’s toxic to ideal do not match. Oddly, the Kinyarwanda Tutsis as Oppressors topic pattern inversely reflects its English counterpart. Kinyarwanda patterns for Tutsis as Oppressors include spikes in mid-late June, whereas English transcripts include four major spikes beginning in December 1993 and ending on April 6, 1994 (see Figures 2-3). Kinyarwanda’s Identity

Dichotomy and Rwandan Identity topics slightly approximates Toxic to Self and

Virtueltalk as Legitimation topics from the English transcripts, all demonstrating heavy tails towards the end of the genocide (see Figures 4-7).

Figure 2: Kinyarwanda Topic: Tutsis as Oppressors

Tutsis as Oppressors (Kinyarwanda) 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000

0 0%

6/1/94 6/8/94 1/5/94 2/2/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 7/6/94

5/18/94 5/25/94 6/15/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/11/94 6/22/94 6/29/94

10/20/93 10/27/93 11/10/93 11/17/93 11/24/93 12/15/93 12/22/93 12/29/93

Generalizing Enemy Identity Number of Deaths

Figure 3: English Topic: Tutsis as Oppressors

Tutsis as Oppressors (English) 80,000 90% 70,000 80% 60,000 70% 50,000 60% 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Tutsis as Oppressors Number of Deaths

Figure 4: Kinyarwanda Topic: Rwandan Identity

Rwandan Identity (Kinyarwanda) 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Rwandan Identity Number of Deaths

Figure 5: Kinyarwanda Topic: Identity Dichotomy

Identity Dichotomy (Kinyarwanda) 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Identity Dichotomoy Number of Deaths

Figure 6: English Topic: Toxic to Self

Toxic to Self (English) 80,000 70% 70,000 60% 60,000 50% 50,000 40% 40,000 30% 30,000 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Toxic to Self Number of Deaths

Figure 7: English Topic: Virtueltalk as Legitimation

Virtueltalk as Legitimation (English) 80,000 90% 70,000 80% 60,000 70% 50,000 60% 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Virtueltalk as Legitimation Number of Deaths

The divergence in pattern approximation is likely due to some of this study’s limitations. First, although I removed one round of stop words from Kinyarwanda transcripts, translated results of the topic model output indicate that there were likely may stop words still interfering with the sensitivity of the analysis. Second, my inability to contextualize the translated topic words inhibits the accuracy of the topic model names 60 that I assigned to them, as discussed previously. Finally, the asymmetry in sample size may disrupt the patterns displayed between the English and Kinyarwanda transcripts.

Despite these limitations, the cursory use of Kinyarwanda transcripts and its respective issues serves to signal the importance of conducting further studies with original transcripts and Kinyarwanda-fluent researchers.

The following tables contain descriptive statistics for English constructs and topics and Kinyarwanda topics. Following the comprehensive graphs, I will present three graphs for each prominent LIWC construct and then each topic, first for the English transcripts and then for the Kinyarwanda transcripts23. I only present examples from

English transcript documents for language and readability reasons.

Table 5: Descriptive Statistics for English Transcript Topics and Constructs

Construct Minimum Maximum Average Urgent Threat Construction 0.02% 73.50% 6.92% Tutsis as Oppressors 0.03% 81.50% 9.41% Political Communication 0.01% 63.71% 4.86% Instillation of Fear 0.00% 45.13% 3.42% Equation of Enemy with Tutsi Identity 0.00% 61.13% 3.93% Toxic to Ideal 0.69% 75.61% 16.37% Toxic to Self 0.02% 61.64% 12.19% Critique of Government Institutions 0.02% 80.38% 10.43% Virtueltalk as Legitimation 0.05% 80.07% 20.38% Construction of Dangerous and 0.14% 86.53% 12.09% Ubiquitous Enemy "I" 0.47% 3.14% 1.48% "We" 0.33% 6.69% 2.25% "You" 0.00% 5.70% 1.69% "They" 0.52% 8.62% 3.18% Positive Emotion 0.66% 4.53% 2.37% Negative Emotion 0.78% 4.35% 2.63%

23 Because I am unable to examine Kinyarwanda transcripts more closely to identify reasons for language patterns, I will only display the full range of data in lieu of the full range plus the broken-down phase data. 61

Anxiety 0.00% 0.80% 0.26% Anger 0.14% 3.21% 1.40% Sad 0.00% 0.65% 0.22% Causal 1.04% 3.71% 2.06% Difference 1.83% 6.94% 3.59% Power 0.57% 6.82% 3.91% Risk 0.00% 3.26% 1.04% Past 0.96% 7.62% 4.70% Present 3.80% 14.75% 9.89% Future Focus 0.00% 3.21% 1.60% Time 1.39% 5.77% 4.06% Death 0.00% 2.87% 0.87% Toxification 0.00% 3.82% 0.82% Dehumanization 0.00% 1.62% 0.38%

Table 6: Descriptive Statistics for Kinyarwanda Transcript Constructs

Construct Minimum Maximum Average Conflict News 0.07% 97.69% 21.95% Toxic to Ideal 0.00% 93.39% 3.14% Tutsis as 0.01% 98.37% 10.72% Oppressors Breadth of Conflict 0.00% 79.50% 9.86% Locations 0.00% 85.80% 10.14% State of the Conflict 0.00% 97.95% 7.17% Enemy Lies 0.01% 69.98% 14.50% Scope of Conflict 0.00% 73.15% 7.20% Rwandan Identity 0.00% 98.45% 8.24% Identity Dichotomy 0.00% 77.75% 7.03%

English Transcript Results

Dehumanizing and Toxifying Language

The two dimensions analyzed through the additional

“Toxification/Dehumanization” dictionary demonstrate increases near the end of the genocide, in late June and early July 1994 (see Figures 1-3). The tallest red spike, 62 indicating 67,432 deaths across the state on April 15, 1994, represents the maximum estimated death count on a single day. The peak measure of toxification (3.82%) occurs on June 25, 1994, and the peak measure of dehumanization (1.62%) on June 14, 1994.

The spike in toxification on June 25th is due to high instances of the word “must”. For example, one passage from the passage reads:

In my view, if French come to help country to restore peace, peace must come from among us. In order for peace to be restored as Mr. Jean Kambanda once said, rightly so - you must know our adversaries, Inkotanyi. (June 25th, 1994)24. The second highest score for toxification occurred on the same day as dehumanization’s peak, on June 14th. LIWC identified several words in both the

“dehumanization” and “toxification” categories, but “toxification” still scored higher at

2.24% compared to “dehumanization’s score of 1.62%. The most commonly occurring words in this analysis were “inyenzi”25 (coded as both dehumanization and toxification),

“enemy” (dehumanization and toxification), “remove” (toxification only), “must”

(toxification), and “kill” (toxification only). One passage from this transcript reads:

…methods of execution used by Inyenzi-Inkotanyi...they kill in cruel manner ...they mutilate body... remove certain organs such as heart, liver stomach. This what I was telling you yesterday, that Inyenzi-Inkotanyi could be eating human flesh ...no, there no doubt about that anyway, since...what do they do with all organs they remove from bodies? They do eat human flesh... Inyenzi-Inkotanyi eat human beings.... so much so that we have little hope of finding any remains.... (June 14, 1994)

Figures 8-14 illustrate these results further.

24 The context of the high occurrence of “must” does not accurately capture the urgency implicit in the theory of toxification. See “Limitations” section. 25 Meaning “cockroach” in Kinyarwanda. 63

Figure 8: Combined Results from Theory-based Secondary Dictionary

Toxifying and Dehumanizing Language 80,000 5 60,000 4 3 40,000 2 20,000 1

0 0

6/1/94 1/5/94 2/2/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/8/94 7/6/94

12/8/93 6/22/94 12/1/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Toxification as % of Total Words Dehumanization as % of Total Words Number of Deaths

Figure 9: Results from Theory-based Secondary Dictionary for “Toxification”

Toxifying Language 80,000 4.50 70,000 4.00 60,000 3.50 50,000 3.00 2.50 40,000 2.00 30,000 1.50 20,000 1.00 10,000 0.50

0 0.00

3/2/94 4/6/94 1/5/94 2/2/94 2/9/94 3/9/94 5/4/94 6/1/94 6/8/94 7/6/94

1/26/94 5/11/94 6/15/94 12/1/93 12/8/93 1/12/94 1/19/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Toxification as % of Total Words Number of Deaths

Figure 10: Toxifying Language: Pre/Early Genocide

Toxifying Language (Pre-Early Genocide) 80,000 1.20 70,000 1.00 60,000 0.80 50,000 40,000 0.60 30,000 0.40 20,000 0.20 10,000 0 0.00 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Toxification as % of Total Words Number of Deaths

Figure 11: Toxifying Language: Mid/Late Genocide

Toxifying Language (Mid-Late Genocide) 80,000 4.50 70,000 4.00 60,000 3.50 50,000 3.00 2.50 40,000 2.00 30,000 1.50 20,000 1.00 10,000 0.50 0 0.00

Toxification as % of Total Words Number of Deaths

Figure 12: Results from Theory-based Secondary Dictionary for “Dehumanization”

Dehumanizing Language 80,000 1.80 70,000 1.60 60,000 1.40 50,000 1.20 1.00 40,000 0.80 30,000 0.60 20,000 0.40 10,000 0.20

0 0.00

3/2/94 4/6/94 1/5/94 2/2/94 2/9/94 3/9/94 5/4/94 6/1/94 6/8/94 7/6/94

1/26/94 5/11/94 6/15/94 12/1/93 12/8/93 1/12/94 1/19/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Dehumanization as % of Total Words Number of Deaths

Figure 13: Dehumanizing Language: Pre/Early Genocide

Dehumanizing Language (Pre-Early Genocide) 80,000 0.3 70,000 0.25 60,000 0.2 50,000 40,000 0.15 30,000 0.1 20,000 0.05 10,000 0 0 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Dehumanization as % of Total Words Number of Deaths

Figure 14: Dehumanizing Language: Mid/Late Genocide

Dehumanizing Language (Mid-Late Genocide) 80,000 1.80 70,000 1.60 60,000 1.40 50,000 1.20 1.00 40,000 0.80 30,000 0.60 20,000 0.40 10,000 0.20 0 0.00

Dehumanization as % of Total Words Number of Deaths

Power Language

Language attributed to the “power” category are consistently high across transcripts, with the highest score (6.92%) occurring on April 9, 1994, just two days after the genocide began, and six days before the deadliest day of the genocide. Examples of words sorted into the “power” category were “president”, “leaders”, “powers”, “under”,

“over”, “God”, “law”, and “experts”. 67

Figure 15: Internal Dictionary Results for “Power”

Power Language 80,000 8 70,000 7 60,000 6 50,000 5 40,000 4 30,000 3 20,000 2 10,000 1

0 0

6/1/94 1/5/94 2/2/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/8/94 7/6/94

12/8/93 5/11/94 6/22/94 12/1/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/18/94 5/25/94 6/15/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Power Language as % of Total Words Number of Deaths

Figure 16: Internal Dictionary Results for “Power”: Pre/Early Genocide

Power Language (Pre-Early Genocide) 80,000 8 70,000 7 60,000 6 50,000 5 40,000 4 30,000 3 20,000 2 10,000 1 0 0 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Power Language as % of Total Words Number of Deaths

Figure 17: Internal Dictionary Results for “Power”: Mid/Late Genocide

Power Language (Mid-Late Genocide) 80,000 8 70,000 7 60,000 6 50,000 5 40,000 4 30,000 3 20,000 2 10,000 1 0 0

Power Language as % of Total Words Number of Deaths

Anger Language

Language with characteristics associated with “anger” grew, with a couple of transcripts demonstrating decreases, over the course of the genocide, with irregular patterns in the period leading up to the onset of the genocide in April. On April 15, the deadliest day of the genocide, “anger” language made up 2.09% of total words, and peaked on June 3, 1994 (3.21%). Example words indicating “anger” are “insulting”,

“war”, “battle”, “enemy”, “hostilities”, “fight”, “weapons”, “attacking”, “killing”,

“tricks”, “cruelty”, and “heartless”. 69

Figure 18: Internal Dictionary Results for “Anger”

Anger Language 80,000 3.50 70,000 3.00 60,000 2.50 50,000 2.00 40,000 1.50 30,000 20,000 1.00 10,000 0.50

0 0.00

3/2/94 4/6/94 1/5/94 2/2/94 2/9/94 3/9/94 5/4/94 6/1/94 6/8/94 7/6/94

1/26/94 5/11/94 6/15/94 12/1/93 12/8/93 1/12/94 1/19/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Anger as % of Total Words Number of Deaths

Figure 19: Internal Dictionary Results for “Anger”: Pre/Early Genocide

Anger (Pre-Early Genocide) 80,000 2.5 70,000 2 60,000 50,000 1.5 40,000 30,000 1 20,000 0.5 10,000 0 0 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Anger as % of Total Words Number of Deaths

Figure 20: Internal Dictionary Results for “Anger”: Mid/Late Genocide

Anger Language (Mid-Late Genocide) 80,000 3.50 70,000 3.00 60,000 2.50 50,000 2.00 40,000 1.50 30,000 20,000 1.00 10,000 0.50 0 0.00

Anger as % of Total Words Number of Deaths

Causal Language

Last, cause-related language remains consistently high throughout the sample, with a maximum score (3.71%) occurring on March 10th, a second highest on June 25th

(3.5%), and the fourth highest score (2.65%) on April 15th, the deadliest day of the genocide. Words such as “because”, “since”, “how”, “basis”, “why”, “purpose”, “effect”,

“enable”, “therefore” “based”, and “launched all belong to the “causal” category. 71

Figure 21: Internal Dictionary Results for “Causal

Causal Language 80,000 4.00 70,000 3.50 60,000 3.00 50,000 2.50 40,000 2.00 30,000 1.50 20,000 1.00 10,000 0.50

0 0.00

3/2/94 4/6/94 1/5/94 2/2/94 2/9/94 3/9/94 5/4/94 6/1/94 6/8/94 7/6/94

1/26/94 5/11/94 6/15/94 12/1/93 12/8/93 1/12/94 1/19/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 4/27/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Causal Language as % of Total Words Number of Deaths

Figure 22: Internal Dictionary Results for “Causal: Pre/Early Genocide

Causal Language (Pre-Early Genocide) 80,000 4 70,000 3.5 60,000 3 50,000 2.5 40,000 2 30,000 1.5 20,000 1 10,000 0.5 0 0 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Causal Language as % of Total Words Number of Deaths

Figure 23: Internal Dictionary Results for “Causal: Mid/Late Genocide

Causal Language (Mid-Late Genocide) 80,000 4.00 70,000 3.50 60,000 3.00 50,000 2.50 40,000 2.00 30,000 1.50 20,000 1.00 10,000 0.50 0 0.00

Causal Language as % of Total Words Number of Deaths

Urgent Threat Construction

The highest instance of this topic occurred on March 10, 1994 (73.5%), followed by a second highest score on April 3 (52.36%), and a third on November 25, 1993

(43.11%). The final spike in Urgent Threat Construction occurs on June 2 (34.32%).

Figure 24: Urgent Threat Construction: Full Data Range

Urgent Threat Construction 80,000 80% 70,000 70% 60,000 60% 50,000 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Urgent Threat Construction Number of Deaths

Figure 25: Urgent Threat Construction: Pre/Early Phase

Urgent Threat Construction (Pre-Early Genocide) 80,000 80%

60,000 60%

40,000 40%

20,000 20%

0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Urgent Threat Construction Number of Deaths

Figure 26: Urgent Threat Construction: Mid/Late Phase

Urgent Threat Construction (Mid-Late Genocide) 80,000 60% 50% 60,000 40% 40,000 30% 20% 20,000 10% 0 0%

Urgent Threat Construction Number of Deaths

Tutsis as Oppressors

Rhetoric framing Tutsis as Oppressors began early in the life of RTLM. The most severe instance of this language occurred on December 12, 1993 (81.5%), followed by a secondary spike on April 6, 1994 (63.41%), the day that Habyarimana’s plane was shot 74 down. Beginning in late May, scores remain moderate, hovering around 20-30% until late

June.

Figure 27: Tutsis as Oppressors: Full Data Range

Tutsis as Oppressors 80,000 90% 70,000 80% 60,000 70% 50,000 60% 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Tutsis as Oppressors Number of Deaths

Figure 28: Tutsis as Oppressors: Full Data Range: Pre/Early Phase

Tutsis as Oppressors (Pre-Early Genocide) 80,000 70% 70,000 60% 60,000 50% 50,000 40% 40,000 30% 30,000 20,000 20% 10,000 10% 0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Tutsis as Oppressors Number of Deaths

Figure 29: Tutsis as Oppressors: Full Data Range: Mid/Late Phase

Tutsis as Oppressors (Mid-Late Genocide) 80,000 70% 70,000 60% 60,000 50% 50,000 40% 40,000 30% 30,000 20,000 20% 10,000 10% 0 0%

Tutsis as Oppressors Number of Deaths

Instillation of Fear

The highest scoring transcript for this topic was April 2, 1994 (45.13%), followed by March 16 (44.67%), and December 9, 1993 (39.51%). This topic comprises less than

5% in most other transcripts.

Figure 30: Instillation of Fear: Full Data Range

Instillation of Fear 80,000 50% 70,000 60,000 40% 50,000 30% 40,000 30,000 20% 20,000 10% 10,000

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Instillation of Fear Number of Deaths

Figure 31: Instillation of Fear: Pre/Early Phase

Instillation of Fear (Pre-Early Genocide) 80,000 50% 70,000 40% 60,000 50,000 30% 40,000 30,000 20% 20,000 10% 10,000 0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Instillation of Fear Number of Deaths

Figure 32: Instillation of Fear: Mid/Late Phase

Instillation of Fear (Mid-Late Genocide) 80,000 50% 70,000 40% 60,000 50,000 30% 40,000 30,000 20% 20,000 10% 10,000 0 0%

Instillation of Fear Number of Deaths

Equation of Enemy with Tutsi Identity

Equating the identity of the enemy with Tutsis figures most prominent before the beginning of the genocide, with the highest scoring transcript on March 23, 1994

(61.13%), followed by December 8, 1993 (47.59%). After killings began on April 7, this language comprised less than 12% of most transcripts. 77

Figure 33: Equation of Enemy with Tutsi Identity: Full Data Range

Equation of Enemy with Tutsi Identity 80,000 70% 70,000 60% 60,000 50% 50,000 40% 40,000 30% 30,000 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Equation of Enemy with Tutsi Identity Number of Deaths

Figure 34: Equation of Enemy with Tutsi Identity: Pre/Early Phase

Equation of Enemy with Tutsi Identity (Pre- Early Genocide) 80,000 70% 60% 60,000 50% 40% 40,000 30% 20,000 20% 10% 0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Equation of Enemy with Tutsi Identity Number of Deaths

Figure 35: Equation of Enemy with Tutsi Identity: Mid/Late Phase

Equation of Enemy with Tutsi Identity(Mid-Late Genocide) 80,000 12% 10% 60,000 8% 40,000 6% 4% 20,000 2% 0 0%

Equation of Enemy with Tutsi Identity Number of Deaths

Toxic to Ideal

Toxic to ideal language scored consistently moderate, and peaked twice within a week of the deadliest day of the genocide (April 15, with 67, 432 deaths across the country). The highest score occurred on April 14 (75.61%), the day before the deadliest day, and the second highest score followed exactly one week after the deadliest day on

April 22 (72.66%). 79

Figure 36: Toxic to Ideal: Full Data Range

Toxic to Ideal 80,000 80% 70,000 70% 60,000 60% 50,000 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Toxic to Ideal Number of Deaths

Figure 37: Toxic to Ideal: Pre/Early Phase

Toxic to Ideal (Pre-Early Genocide) 80,000 80% 70,000 70% 60,000 60% 50,000 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10% 0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Toxic to Ideal Number of Deaths

Figure 38: Toxic to Ideal: Mid/Late Phase

Toxic to Ideal (Mid-Late Genocide) 80,000 80% 70,000 70% 60,000 60% 50,000 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10% 0 0%

Toxic to Ideal Number of Deaths

Figure 39: Toxic to Idea: Final Two Months

Toxic to Ideal (Final Months of Genocide) 2,500 50%

2,000 40%

1,500 30%

1,000 20%

500 10%

0 0%

Toxic to Ideal Number of Deaths

Toxic to Self

Language framing victims as toxic to self remains consistently low, even following the deadliest phase of the genocide. This shifts to consistently high scores, as demonstrated by the heavy tail in the later phase of the genocide. The highest scoring transcript for this topic was on June 18, 1994 (61.64%), followed by June 9 (57.44%). 81

Figure 40: Toxic to Self: Full Data Range

Toxic to Self 80,000 70% 70,000 60% 60,000 50% 50,000 40% 40,000 30% 30,000 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Toxic to Self Number of Deaths

Figure 41: Toxic to Self: Pre/Early Phase

Toxic to Self (Pre-Early Genocide) 80,000 14% 70,000 12% 60,000 10% 50,000 8% 40,000 6% 30,000 20,000 4% 10,000 2% 0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Toxic to Self Number of Deaths

Figure 42: Toxic to Self: Mid/Late Phase

Toxic to Self (Mid-Late Genocide) 80,000 70% 70,000 60% 60,000 50% 50,000 40% 40,000 30% 30,000 20,000 20% 10,000 10% 0 0%

Toxic to Self Number of Deaths

Figure 43: Toxic to Self: Final Two Months

Toxic to Self (Final Months of Genocide) 2,500 70% 60% 2,000 50% 1,500 40% 1,000 30% 20% 500 10% 0 0%

Toxic to Self Number of Deaths

Critique of Government Institutions

This topic scores high prior to the deadliest day, with the highest score occurring two days after the beginning of the genocide, on April 9th (80.38%), followed by three peaks in January 1994 (January 21st at 74.24%, January 16th at 70.84% and January 14th 83 at 63.88%). It remains below 10% until what most consider the final day of the genocide,

July 3, 1994, at 23.62%.

Figure 44: Critique of Government Institutions: Full Data Range

Critique of Government Institutions 80,000 90% 70,000 80% 60,000 70% 50,000 60% 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Critique of Government Institutions Number of Deaths

Figure 45: Critique of Government Institutions: Pre/Early Phase

Critique of Government Institutions Language (Pre-Early Genocide) 80,000 100%

60,000 80% 60% 40,000 40% 20,000 20% 0 0%

Critique of Government Institutions Number of Deaths

Figure 46: Critique of Government Institutions: Mid/Late Phase

Critique of Government Institutions Language (Mid-Late Genocide) 80,000 100%

60,000 80% 60% 40,000 40% 20,000 20% 0 0%

Critique of Government Institutions Number of Deaths

Virtueltalk as Legitimation

Following a similar pattern to Toxic to Self rhetoric, Virtueltalk scores high on the mid-late range of the genocide. Highest scores occur on May 16th (80.07%), July 2

(76.18%), and May 18th (68.34%).

Figure 47: Virtueltalk as Legitimation: Full Data Range

Virtueltalk as Legitimation 80,000 90% 70,000 80% 60,000 70% 50,000 60% 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10%

0 0%

3/9/94 1/5/94 2/2/94 2/9/94 3/2/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

12/1/93 1/19/94 4/27/94 6/15/94 12/8/93 1/12/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/20/94 5/11/94 5/18/94 5/25/94 6/22/94 6/29/94

11/24/93 12/15/93 12/22/93 12/29/93

Virtueltalk as Legitimation Number of Deaths

Figure 48: Virtueltalk as Legitimation: Pre/Early Phase

Virtueltalk as Legitimation (Pre-Early Genocide) 80,000 50% 70,000 40% 60,000 50,000 30% 40,000 30,000 20% 20,000 10% 10,000 0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Virtueltalk as Legitimation Number of Deaths

Figure 49: Virtueltalk as Legitimation: Mid/Late Phase

Virtueltalk as Legitimation (Mid-Late Genocide) 80,000 90% 70,000 80% 60,000 70% 50,000 60% 50% 40,000 40% 30,000 30% 20,000 20% 10,000 10% 0 0%

Virtueltalk as Legitimation Number of Deaths

Figure 50: Virtueltalk: Final Two Months

Virtueltalk (Final Months of Genocide) 2,500 90% 80% 2,000 70% 60% 1,500 50% 40% 1,000 30% 500 20% 10% 0 0%

Virtueltalk as Legitimation Number of Deaths

Construction of Dangerous and Ubiquitous Enemy

The highest scoring day for this topic is June 4th (86.53%), followed by May 13th and May 15th, both characterized by 57% of words relating to this topic. On April 11, just four days before the genocide’s deadliest day, the topic comprised 43.04% of total words.

Figure 51: Construction of Dangerous and Ubiquitous Enemy: Full Data Range

Construction of Dangerous and Ubiquitous Enemy 80,000 100% 60,000 80% 60% 40,000 40% 20,000 20%

0 0%

1/5/94 2/2/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94 7/6/94

1/26/94 4/13/94 6/29/94 12/1/93 12/8/93 1/12/94 1/19/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/20/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94

12/29/93 11/24/93 12/15/93 12/22/93

Construction of Dangerous and Ubiquitous Enemy Number of Deaths

Figure 52: Construction of Dangerous and Ubiquitous Enemy: Pre/Early Phase

Construction of Dangerous and Ubiquitous Enemy (Pre-Early Genocide) 80,000 50%

40% 60,000 30% 40,000 20% 20,000 10%

0 0% 3/1/94 3/8/94 3/15/94 3/22/94 3/29/94 4/5/94 4/12/94 4/19/94 4/26/94

Construction of Dangerous and Ubiquitous Enemy Number of Deaths

Figure 53: Construction of Dangerous and Ubiquitous Enemy: Mid/Late Phase

Construction of Dangerous and Ubiquitous Enemy (Mid-Late Genocide) 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000 0 0%

Construction of Dangerous and Ubiquitous Enemy Number of Deaths

Kinyarwanda Topics

Conflict News

This topic, which contains words such as “radio”, “Rwanda”, “people”, and

“now” remains consistently moderate preceding and throughout the conflict, which 88 makes sense given the source of the data: radio transcripts. This topic spikes on March

30, 1994 (97.69%), with a secondary spike on December 21, 1993 (93.73%).

Figure 54: Conflict News: Full Data Range

Conflict News 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Conflict News Number of Deaths

Toxic to Ideal

This topic lacks any clear pattern, signaling possible inaccuracies with topic assignment. The highest score occurred on April 6th, the day Habyarimana died (93.39%).

Another spike occurred on June 14 (39.83%), which does mimic slight spikes in the

English version of this topic in mid-late June 89

Figure 55: Toxic to Ideal: Full Data Range

Toxic to Ideal 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Toxic to Ideal Number of Deaths

Tutsis as Oppressors

As previously discussed, this topic approximates other English transcript topics such as Virtueltalk and Toxic to Self, with scores remaining below 25% until the later stage of the genocide. There are three primary spikes, all occurring in June: June 3

(73.98%), June 5 (95.55%), and June 10th (98.37%). In interpreting these high scores, it is essential to remember that the words I extracted from the topic were mostly stop words, and thus the significance of the score is inflated. 90

Figure 56: Tutsis as Oppressors: Full Data Range

Tutsis as Oppressors 80,000 100% 70,000 60,000 80% 50,000 60% 40,000 30,000 40% 20,000 20% 10,000

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Generalizing Enemy Identity Number of Deaths

Breadth of Threat

This topic entails words pointing to Burundi and the assassination of Ndadaye, which, in the English transcripts, primarily served a paralleling function to illustrate the breadth of the enemy and threat. Scores were consistently high prior to the genocide, dropping significantly in the three months prior to the first day of killings. There are two notable spikes following the deadliest phase of the genocide, one on May 11 (79.5%), and the second on June 30 (65.73%). 91

Figure 57: Breadth of Threat: Full Data Range

Breadth of Threat 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Breadth of Threat Number of Deaths

Locations Under Threat

While this pattern also approximates English topics like Toxic to Self and

Virtueltalk, it should be interpreted tentatively, as I named the topic with minimal meaningful words. It’s highest score occurred on May 29 (85.8%), with a cluster of late

May days also comprised of scores above 70%. The first spike occurred late in the genocide, on May 14 (60.12%). 92

Figure 58: Locations Under Threat: Full Data Range

Locations Under Threat 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Locations Under Threat Number of Deaths

State of the Conflict

This topic peaked often in the fall and winter of 1993/1994. Its highest score occurred on November 1 (97.95%), followed by the previous day’s transcript on October

31 (69.51%), which may be due to the recent assassination of Ndadaye in Burundi and subsequent violent outbreaks on October 21. 93

Figure 59: State of Conflict: Full Data Range

State of Conflict 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

State of the Conflict Number of Deaths

Enemy Infiltration

The frequency of the name “Twagiramungu” aided in the naming of this construct due to frequent RTLM claims that Twagiramungu is a secret Tutsi accomplice. This signals the infiltration component that broadcasters often charge the RPF and Tutsis more generally. Scores remain moderate, between 30%-70% in the months leading up the genocide, peak on March 25 (69.98%), just two weeks before the first documented genocidal killings. They remain very low until the end of the genocide, with the exception of June 1 (32.1%). 94

Figure 60: Enemy Infiltration: Full Data Range

Enemy Infiltration 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Enemy Lies Number of Deaths

Scope of the Conflict

I also named this topic with minimal meaningful words, which must be considered when interpreting the topic patterns. Scores are low to moderate until the middle and late stages of the genocide, where a spike occurs on May 16 (73.15%), and

June 4 (68.35%). 95

Figure 61: Scope of Conflict: Full Data Range

Scope of Conflict 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Scope of Conflict Number of Deaths

Rwandan Identity

This topic may signal an attempt to unify perpetrators under Rwandan identity. It spikes on June 16 (96.18%), June 13 (96.18%), and April 13 (87.8%), two days before the deadliest day.

Figure 62: Rwandan Identity: Full Data Range

Rwandan Identity 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

2/2/94 7/6/94 1/5/94 2/9/94 3/2/94 3/9/94 4/6/94 5/4/94 6/1/94 6/8/94

4/20/94 11/3/93 12/1/93 12/8/93 1/12/94 1/19/94 1/26/94 2/16/94 2/23/94 3/16/94 3/23/94 3/30/94 4/13/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

11/17/93 10/20/93 10/27/93 11/10/93 11/24/93 12/15/93 12/22/93 12/29/93

Rwandan Identity Number of Deaths

Identity Dichotomy: Us versus Them

This topic also approximates English topics Toxic to Self and Virtueltalk, signaling an overlap in contextual calls to action and a dichotomous framing of enemy identity as opposed to perpetrator identity. The Identity Dichotomy remains relatively low until the late stage of the genocide, with a high score of 77.75% on June 6 and a secondary high score three days later on June 9th (73.33%).

Figure 63: Identity Dichotomy: Full Data Range

Identity Dichotomy: Us versus Them 80,000 100% 70,000 90% 80% 60,000 70% 50,000 60% 40,000 50% 30,000 40% 30% 20,000 20% 10,000 10%

0 0%

5/4/94 1/5/94 2/2/94 2/9/94 3/2/94 3/9/94 4/6/94 6/1/94 6/8/94 7/6/94

1/19/94 2/23/94 3/30/94 11/3/93 12/1/93 12/8/93 1/12/94 1/26/94 2/16/94 3/16/94 3/23/94 4/13/94 4/20/94 4/27/94 5/11/94 5/18/94 5/25/94 6/15/94 6/22/94 6/29/94

10/20/93 10/27/93 11/10/93 11/17/93 11/24/93 12/15/93 12/22/93 12/29/93

Identity Dichotomoy: Us versus Them Number of Deaths

Discussion

Key Findings

This project’s first proposition, that toxification and dehumanization are empirically distinguishable, enjoys substantial support. However, results suggest that

Neilsen’s original hypothesis, that toxification may serve as a more precise indicator of impending genocide, may not hold, at least in the Rwandan case. While levels of characteristics of toxification, including zero-sum rhetoric, construction of a threatening enemy, and calls to action, figure significantly into RTLM rhetoric, most of these characteristics increase after the beginning of the genocide, and even after the deadliest phase of the genocide in mid-April. Thus, the second proposition in this thesis, that toxification contributes to the onset and/or intensification of killing, is not supported.

However, resulting patterns may indicate alternative purposes for toxifying language, namely to re-motivate perpetrators to participate in killing, to legitimate killings by pointing to the visceral threat the victims pose to perpetrators, and to rationalize post-hoc participation that has already occurred (Maynard, 2014).

While Neilsen’s theory of toxification posits that toxification may be a more precise early warning sign for genocide, these results fail to support such a proposition

(Neilsen, 2015). Levels of toxification remain consistently low from late November 1993, just three months after the Arusha Accords were signed, until mid-March 1993. These low scores may be due to the RTLM’s more benign lamenting of political institutions, failure to implement transitional institutions ordained by the Arusha Accords, and general dissatisfaction with infrastructural provisions and the economy. Alternatively, low levels 98 of toxification may be attributable to a conscious decision by RTLM’s radio personalities to maintain popularity, credibility, and trustworthiness with their audience. Such a gradual instillation of extremist ideology and hateful rhetoric serves to mask genocide- inducing ideas with language that is acceptable and normalized, instead of shocking and unattractive (Maynard & Benesch, 2016, p. 77).

Similar explanations may transpire for the lower scores of dehumanizing rhetoric, thought it should be noted that dehumanizing rhetoric remains consistently lower than toxifying rhetoric, thereby illuminating the important distinction between the two concepts. The relative consistency of the co-occurrence of dehumanization and toxification supports Neilsen’s assertion that toxification includes dehumanization, though toxification includes elements that go beyond dehumanization as its distinguishing characteristics. Thus, one must first dehumanize a subject before one can render the subject toxifying. The gradual build-up of dehumanization, therefore, may also be due to the preoccupation with more object and process-oriented discussions of politics, politicians, especially Mrs. Agathe Uwilingiyimana, the Prime Minister of Rwanda from the signing of the Arusha Accords until her assassination on April 7th, whom RTLM broadcasters frequently berated.

The bi-modal distribution between “deaths per day” and spikes in toxification and dehumanization may indicate a late-stage urgent attempt to mobilize Interahamwe and any other fighter to defeat the RPF, which was quickly nearing victory over the capitol city, Kigali. This late amplification may be a reflection of the RTLM making a final 99 attempt to maintain its base of power and coalesce the remaining strength within its own associated militias.

As French troops entered Rwanda under Operation Turquoise on June 23rd, the

RTLM’s messaging began to emphasize camaraderie with French forces and urged good behavior on part of its listeners (such as instructions to not kill people in front of the

French or allow them to see many bodies) (Power, 2002, p. 380). This attempt would not be effective, however, as the RPF, with the help of Ugandan forces, successfully took

Kigali on July 4th, 1994.

In terms of the other linguistic characteristics, similar processes may be behind the trends. While “power” language remained consistently high from the first transcript in

November 1993 to the final transcript in this sample on July 3, 1994 it did not significantly spike as dehumanization and toxification did towards the end of June. Its scores still remain higher than those for dehumanization and toxification, but power language does not increase at the same relative pace as dehumanization and toxification do. This may indicate a subtle nuance in the manner in which RTLM broadcasters attempted to maintain power. Instead of using words that the analysis associates with power, such as “under”, “president”, or “experts”, all which indicate power structures or roles within a society or group, attempts to maintain power likely manifested through language of intensity and aggression, intended to incite listeners, which LIWC’s “power” category does not pick up on. RTLM broadcasters may have utilized “causal” language to accentuate blame both at the beginning of the genocide and near the end, when the fear of loss increased. According to most literature on language and genocide, causal language is 100 typically associated with branding the Other as blameworthy or a scapegoat, which is evident in the consistently high usage of “causal” language in the months leading up to the genocide (Stanton, 2004; Neilsen, 2015; Donohue, 2012; Maynard and Benesch,

2016; Maynard, 2014).

Similarly, “anger” language spiked on April 15th, the deadliest day of the genocide. The characteristics of this category are consistent with toxification’s theoretical underpinnings, as words such as “heartless”, “cruel”, “enemy”, and “attacking” appeal to fear and survival, and also serve to brand the out-group as not only non-human

(“heartless”), but as an existential threat to oneself (“tricks”, “attacking”), which Neilsen terms “toxic-to-self” (Neilsen & Williams, 2016). The following section summarizes rhetorical patterns over the three stages of analysis.

Rhetoric Preceding and During the Early Phase of Genocide

There are several reasons toxifying language was not as significant during the phase leading up to the onset of killings and the early phase of the genocid. In the months leading up to April 1994, RLTM broadcasts focused primarily on dissatisfaction with the

Arusha Accords, failure to implement transitional institutions established by the accords, and blame on the RPF as a party and military faction for these grievances. The most prominent English topic during this phase was Critique of Government Institutions, which signals that broadcasters emphasized the substantive issues at hand, though still attributed this blame to future victims. At this phase of the conflict, such rhetoric serves to metaphorically open the door to listeners to construct the minority population as a threat, enabling broadcasters, other media outlets, and officials to gradually intensify 101 enemy identity construction. Donohue’s Identity Trap supports this pattern: as peoples’ frustration with substantive issues builds, “authority figures emerge from the in-group and become the voice compelling action by first building an increasingly extreme identity of the targeted out-group and then providing a path that leads to the elimination of the out-group” (Donohue, 2012, p. 16). At the same time, in the months leading up to the genocide, businessmen, political leaders, and military personnel were importing machetes and other weapons to arm up to one in every three Hutus in Rwanda as part of Bagosora’s

“civilian self-defense program” (Desforges, 1999, p. 9). This fact, taken together with the substantive and comparatively benign grievance communication signals a possible strategy for broadcasters: while military personnel prepare materially to carry out genocide, media pundits gain the attention and loyalty of listeners, smoothing the pathway for angry civilians to participate in future violence. Thus, while frustration regarding security and government intensified, RTLM broadcasters, the authority figures in this context, capture their audiences through capitalizing on frustration and then intensify the construction of the enemy’s identity with listeners’ tacit consent.

This progression is evident in the shift from critiquing government institutions to the framing of Tutsis as oppressors, which spiked in mid-late March 1994. The word

“Inyenzi” is seldom used, though broadcasters focus on the privileged position Tutsis have historically enjoyed relative to the rest of the Rwandan population, which creates identity opposition based on ethnic lines. Kinyarwanda topic Enemy Infiltration’s consistently high scores in this phase also point to incremental intensification of identity construction, as language serves to saturate listeners with images of a sneaky, geographically virile enemy. Frequent discussion of conflict in Burundi and the RPF’s 102 role therein emphasizes that listeners in Rwanda may fall victim to similar violence and chaos. Thus, the pre- and early phases of the genocide demonstrate attempts to motivate listeners to participate in violence, supporting Maynard’s justificatory ideology, but failing to support toxification, which theoretically should have been at its highest in this period.

Rhetoric During the Deadliest Phase of Genocide

Directly preceding the deadliest day of the genocide, broadcasters begin to focus on the construction of an urgent threat and instillation of fear. Oddly, these spikes occur before President Habyarimana’s plane was shot down, indicating that plans to begin more rampant killings were in place prior to Habyarimana’s death (also indicated by the import of massive amounts of weapons). Although it is impossible to speculate the identity of the perpetrators of the assassination (both sides of the conflict blame the other side for it), it is, indeed, possible to infer that, if RTLM broadcasters knew about the catalyzing incident that would occur on April 6, they communicated the urgency of an impending threat to intensify motivation to participate in killings under the guise of self-defense.

April 2, for example, contains spikes in both Urgent Threat Construction and Instillation of Fear. The proximity of these spikes to the actual occurrence of a catastrophic events may be coincidental, or indicate broadcaster knowledge of the events that would unfold on April 6th and 7th. Attributing guilt to future victims in this way serves to justify participation in killings, the second stage of justificatory ideology (Maynard, 2014, p.

829). Again, this phase fails to support theoretical expectations of toxification, as scores should have been higher preceding the onset of genocide. 103

Rhetoric During Later Stages of Genocide

Although contrary to Neilsen’s temporal conception of toxification as an indicator prior to the onset of genocide, patterns following the most intense phase of the genocide are most compelling. Virtueltalk as Legitimation, Toxic to Ideal, Toxic to Self,

Construction of a Dangerous and Ubiquitous Enemy (all characteristics of toxification), and Anger language all intensify following the deadliest phase of the genocide in April.

Kinyarwanda topics Rwandan Identity, Tutsis as Oppressors, and Identity Dichotomy also spike in the middle and late phase of the genocide. There are several possible reasons these patterns persist. First, increases in toxifying language may serve as a post-hoc rationalization intended to alleviate any guilt associated with killings that have already occurred and to provide a cushion against international ridicule. As international attention to the conflict began to grow, RTLM broadcasters began to accentuate the necessity of preventing a minority enemy from taking over the majority population. Rhetoric accentuates the brutality of RPF soldiers against Hutus, the threat that the enemy poses to any chance at peace amidst the conflict, and the irrationality of feeling guilty for killing victims, for perpetrators are merely preemptively defending themselves against a generalizable killer. Radio hosts concretize the threat to individual Hutus and the Hutu race overall by citing purported statistics regarding how the goal percentage that Tutsis seek to reduce Hutus to in order to take over the country.

Examining the final two months of the genocide more granularly sheds light onto the attempt to legitimate violence post-hoc. For example, there are two late-stage spikes in toxic to ideal following two late-stage spikes in deaths per day. One spike in toxic to 104

ideal occurs on May 15th (34.71%) just two days after a deadly day resulted in 2,239 deaths. Similarly, toxic to ideal makes up 45.14% of the transcript for June 25th, the day after 1,637 were killed, a drastic increase from the still horrific 94 people killed on June

23rd. In addition to appealing to fear, rhetoric focused on building up the identity of those participating in violence to cushion any international ridicule that might mount during the later stages of the genocide. Virtueltalk demonstrates this post-hoc valorization of violence following the deadly May 13th, with three contiguous spikes on May 16th

(80.07%), May 17th (58.44%), and May 18th (76.18%). This may indicate a shift away from dehumanizing rhetoric, which alleviates any psychological qualms for individuals engaging in genocide when participation and maximum lethality are of utmost important to perpetrators. However, these goals may change upon the perception of loss on part of perpetrators, as well as with the arrival of French troops and overall increased international attention. Instead, broadcasters may become more focused on proving to the international community that their violent actions have been and continue to be warranted.

Second, increases in toxifying language may indicate an attempt to maintain a deteriorating grasp on political power and social control. This is complemented by the rise in Kinyarwanda topics focused on constructing and unifying Rwandan identity, yet firmly contrasting it with the RPF, cockroaches, and Tutsis more broadly. Not only does this accentuation serve to dehumanize and justify the killing of victims, but it re- energizes and reminds perpetrators that the conflict truly is Rwandans against a foreigner enemy who is not Rwandan: the RPF. The arrival of French soldiers in late June through

Operation Turquoise revitalized perpetrators, though not without imploring them to 105 refrain from public mass slaughters and to hide bodies off of the street. As RPF forces began to encroach on Kigali in late June, minimizing the power of the Rwandan Armed

Forces, RTLM transcripts began to focus on ratcheting citizens to participate in a valorous defense of the Rwandan capital, lest they lose power to the minority. Thus, increases in calls to action, toxic to self language, and emphasis on the ubiquity of the enemy serve as a last-ditch attempt to revitalize enthusiasm for participation in the genocide in an attempt to retain power.

Taken together, linguistic patterns suggest that rhetoric was meant to serve a three purposes: re-motivate perpetrators to engage as a last ditch attempt to defeat the RPF, justify violence that has already occurred, and signal to the international community that aggressors have been merely defending themselves against a cruel aggressor.

Implications for Theory of Toxification

While results fail to support the project’s propositions as well as Neilsen’s theory, they do offer several implications as to the furtherance of toxification theory and, more generally linguistic analysis of genocidal rhetoric. Each case of genocide unfolds differently. In the Rwandan case, it unfolded quickly, piqued in mid-April, but remained an efficient genocidal machine until early July, with deaths still occurring in the thousands per day. The combat dynamics between the RPF, the Interahamwe, and the

Rwandan Armed Forces may imply that linguistic patterns rely heavily on the success or failure of military factions, and that pundits may be reacting to the encroachment of the enemy faction, “successful” massacres of victims, or the arrival of international military support (in this case, French troops). It is also possible that toxification can fail at its 106 purposes and intentions. That is, perhaps toxification does lead to the intensification of killings in certain genocidal contexts, but broadcasters’ use of toxifying language in this context was not enough to re-motivate individuals to kill on as massive of a scale as mid-

April (though this still indicates failure of the theory in the Rwandan case). Thus, while this case indicated a failure of toxification to predict the onset of genocide, it may, indeed, do so in other cases with different military dynamics. However, in the Rwandan case, toxification would not have been a more precise indicator of an impending genocide.

There are, however, several operational refinements I suggest to understanding the nuances of toxification as distinct from dehumanization, though they may not contribute to its predictive power. The first additional indicator is that toxification seems to contain language that inversely approximates a component of dehumanizing language, the taking away of victim agency (Luna, 2018). Dehumanization’s characteristic of removing agency from victims (i.e. we will take from them, we will do this to them) is inverted in toxifying language. Transcripts demonstrating high scores of toxic to ideal and toxic to self, as well as framing Tutsis as oppressors often contained language highlighting the victim taking something from the perpetrators, not the other way around.

These selections frequently cited Tutsis as doing something brutal to Hutus, as the minority taking something from the majority.

A second refinement regards the specific call to action that is common in

Virtueltalk, toxic to self and toxic to ideal. Rather than simply calling to action the military, the legitimate national defenders, I propose that toxification includes universal 107 calls to action. Calls to action for every Rwandan instills in listeners that they are, indeed, defenders of their own physical existence (toxic to self) and the ideal of peace and Hutu power in Rwanda (toxic to ideal). Universal calls to action have to ability to convince listeners that they are just as important and valorous as uniformed fighters, thereby increasing the attractiveness of engaging in killing. This distinction is important to make given the ability of universal calls to action to motivate entire populations to participate in genocide, not just those in uniform, which has the potential to greatly increase the lethality of genocide.

Third, toxification seems to entail an element of surreptitiousness, trickery, and infiltration. These are all underdiscussed elements in Neilsen’s initial proposal of the theory, though they are characteristic of viruses, disease, and other elements that threaten survival, concepts essential to her formulation. While this type of language was present in early and mid-phase transcripts in Rwanda, they intensified in transcripts scoring high on toxic to self, toxic to ideal, and construction of a dangerous and ubiquitous enemy. Such rhetoric frames the enemy as evermore lethal given their ability to blend in, infiltrate, and perform lethal trickery. This is distinct from dehumanization, which by means of denying essential human characteristics like intellect, rationality, or refinement, disqualifies the dehumanized from possessing enough intelligence to sneak around, lie, or infiltrate a powerful group (Haslam, 2006, p. 256). As an empirical indicator of toxification, attribution of characteristics like these highlights that the victim poses a threat to the perpetrator, which wouldn’t be consistent if the perpetrator were merely dehumanizing the victim. This language also intensifies the urgency in “protection”, used as a 108 euphemism for pre-emptive killing, which creates a more compelling motivation to participate in genocide.

Should We Disregard Toxification?

Toxification did not serve as a precise indicator of genocide in the Rwandan context, but it should not vanish from genocide scholarship or future empirical work. In fact, its failure to predict the onset of killings in the Rwandan context should provide more impetus to conduct further studies with the operationalization utilized and expanded upon in this study’s topic models. If additional case studies fail to find high occurrences of toxification preceding the onset or intensification of killing, it will become necessary to discuss whether or where toxification belongs in the genocidal process. In this case, toxification served as a motivating factor upon the perceived loss of power as well as a legitimating and rationalizing justification to continue participation in genocide, publicly defend to the international community the atrocities occurring in Rwanda, and dissuade any feelings of guilt or regret for past participation. If future studies corroborate this pattern, then toxification should be inserted into the stages of genocide as a post-hoc justificatory stage.

Limitations of the Study

While I am not privy to the fluency and transcription abilities of transcribers, I am confident that the International Criminal Tribunal on Rwanda employed the best and most fluent transcribers possible. However, there are likely translation or transcription errors that may inhibit the accuracy of results. 109

A second limitation to data collection is the uncertainty that all existing transcripts are available online. It is possible that online repositories only host samples of their entire collection. Furthermore, as previously mentioned, there are 23 Kinyarwanda transcripts lacking identifiable dates, which I excluded from the analysis.

Third, LIWC’s categorization of certain words are not contextualized, which occasionally resulted in the inflation of construct scores. While the lack of contextualization is actually a unique strength of LIWC, as it preserves objectivity, it requires post-hoc examination of the words sorted into each construct. In this project, I found only a couple instances of artificially inflated scores due to the lack of contextualization.

Finally, I often found errors between the date officially recorded on the meta-data page for each transcript and occasional narrated dates transcribed within the actual transcript. For the English transcripts, I changed any erroneous date to the narrated date stated within the transcript. However, for Kinyarwanda transcripts, I was not able to do the same given my language constraints, except in cases where the date was written in number form, in which case I defaulted to the narrated date.

Conclusion

This project set out to identify whether a nuanced type of rhetoric, toxifying rhetoric, is empirically distinguishable from dehumanizing rhetoric, and whether it figured more significantly into the onset of genocide in 1994 Rwanda. Through computational linguistic analysis of a corpus of RTLM transcripts in English and

Kinyarwanda preceding and during the genocide, I reached two main conclusions: 110 toxification is, indeed, empirically distinguishable from dehumanization, but it does not serve as a more precise indicator of genocide, at least in the Rwandan context. I demonstrated that toxification differs empirically from dehumanization in three ways additional to Neilsen’s distinctions. First, one indicator of dehumanization is the taking away of agency from a victim through words or phrases such as “we will take from them”, or “we will make them do this” (Luna, 2018). Toxifying rhetoric can be characterized as communicating the inverse of this, often referring to victims as trying to take something from the perpetrator, or do something to the perpetrator, thereby affording the victim agency. Second, Neilsen distinguishes toxification from dehumanization in their respective tendencies to communicate calls to action, which she argues toxification does, and dehumanization does not. I argue this needs to be specified even further, as I found most calls to action were directed at a universal audience, not simply the military or political officials. Third, I found that, instead of denying victims uniquely human traits such as intellect, rationality, or refinements, as Haslam purports is a crucial indicator of dehumanization (2006), toxifying rhetoric instead affords these characteristics to victims by painting them as sneaky tricksters and surreptitious infiltrators. I also found support for Neilsen’s original distinctions, such as requisite calls to action, framed by zero-sum rhetoric about physical and group-ideal survival, and urgency in action.

While I did not find support that toxification leads to the onset of or an intensification of killing in Rwanda, further research should be conducted to examine whether this pattern fails to arise in other genocidal contexts. Utilizing Neilsen’s and the indicators I suggest above, it should be more feasible to distinguish between dehumanization and toxification in future studies. Further scholarship seeking to apply 111 toxification should ideally include data showcasing political speeches, print media, educational material, and in more contemporary cases, social media, as other rhetorical venues might illuminate variation between language patterns in a genocidal context.

Twenty-six years have passed since the first day of the Rwandan Genocide, and scholars rightly persist in trying to understand how neighbors and members of ethnically mixed families turned against each other and towards the machete or gun. While genocide is a complex amalgamation of structural, historical, and psychological factors, it is critical to examine the very fundamentals of interpersonal and intergroup relationships: language. Such examination increases understanding of the more subtle mechanisms at work during the various stages of genocide, and sophisticating understanding of these subtleties will allow scholars and policymakers to better prevent, or if it is too late, intervene in one of humanity’s most shameful behaviors.

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Appendices

Appendix 1 – Toxification/Dehumanization Dictionary

% 1 Toxification 2 Dehumanization % kill 1 dirty 2 1 barbarous 2 1 evil 2 1 inhumane 2 unhuman 2 animal 2 Inyenzi 2 1 Inyenzi-Inkotanyi 2 1 deadly 1 lethal 1 rotten 2 1 cancerous 2 1 infectious 2 1 malignant 1 filthy 2 1 stupid* 2 machine* 2 rat 2 1 pollut* 2 1 purify 1 sanitize 1 clean* 1 dangerous* 1 disease* 2 1 hurry 1 quick* 1 must 1 fast 1 enemy 2 1 ibinhindugemb 2 1 defekten 2 1 Erkrankung 2 1 toxic 2 1 giftig 1 Giftpilz 2 1 121 gangrenous 2 1 devil 2 1 torture 2 1 threat 1 icyitso 2 1 annihilate 1 destroy 1 obliterate 1 robot* 2 invasive 2 1 parasit* 2 1 subversive 2 1 bloodsucker 2 1 sick* 2 malaise 2 1 ill 2 rot* 2 1 epidemic 2 1 contaminat* 2 1 virus 2 1 microbe 2 1 impure 2 unhygienic 2 horrific 2 remove 1 plan 1 necessary 1 essential 1 infected 2 1 cut 1 corrupt 2 eradicate 1 corrosive 2 1 contaminate 2 1 accomplice 2 1 purge 1 treacherous 2 1 duty 1 immediate* 1 instant* 1 steal 1

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Appendix 2 – Original Kinyarwanda Topic Modeling Output

Topic Top Words... Id 0 ngo ni mu rero muri ko na ati ari ubwo ibyo ariko hari ku se abantu ee umuntu uko ntabwo 1 ni mu ko ngo rero muri ari na uko ku rwanda ariko icyo aho ya nduga ibyo ati nta ubwo 2 mu ngo ko ni na ku ari muri ee rero ariko rwanda iyo abantu ibyo uko icyo hari kandi cyane 3 ko mu ni muri ngo ari na ku rero ariko nta uko kandi ya ati abantu aho icyo kuko ibyo 4 mu ko muri ni ngo na rero ari ku fpr inkotanyi abantu ya ariko bati uko icyo bari abo kanyarengwe 5 mu ko ngo ni muri rero na gahigi ya ari bati uko rwose gaspard ubwo se hein ku hari ariko 6 mu ko na ngo ni muri ku rero ari uko burundi abantu ee ariko ya kandi ndadaye hari kuko iyo 7 de le la les qui des est en ce il e9 du pas qu vous je dans au pour nous 8 94 92 mu ko ngo 92uko e9 92u sp sn sv ni 92i ati ku ari pr kandi 92igihugu e9s 9 rero mu ngo na ko ni muri ati ku ari abantu ariko ibyo noneho nta ya hari kandi byo ubwo

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Appendix 3 - Index of Names and Groups People Dr. Theodore Sindikubwabo Interim President following Habyarimana’s death and continued to serve as head of state during the genocide. He belonged to the MDR party. Jean Kambanda Prime Minister of the Interim Government during the genocide and member of the MDR. Agathe Uwilingiyimana Served as Prime Minister of Rwanda, representing a moderate voice of the MDR party, from July 1993 until her assassination on April 7, 1994. Mathieu Ngirumpatse President of the MRND and held relative control over the Interahamwe, MRND’s youth wing. Edouard Karemera Minister of the Interior during Kambanda’s interim government. He also served as the First Vice President of the MRND during the genocide. Faustin Twagiramungu President of the MDR party, often accused of being a Tutsi in disguise. He urged for continued cooperation with the RPF, leading to an attempt to expel him from the party in February 1993. Colonel Théoneste Bagosora Established the Interahamwe and distributed arms throughout Rwanda leading up to the genocide as part of the “civilian self-defense program”. He largely led military component of interim government. Colonel Alexis Kanyarengwe Prominent Hutu politician who, after fleeing from Rwanda amidst allegations he was plotting to overthrow Habyarimana, rose in the ranks of the RPF. Pasteur Bizimungu Worked in Habyarimana’s MRND administration as director-general of the national electricity company. He then joined the RPF in 1990 and became president of Rwanda after the RPF took Kigali in July 1994. Joseph Kavaruganda President of the Rwandan Constitutional Court until his assassination on April 7, 1994. He was a stout opponent of the Hutu Power movement, and

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Justin Mugenzi Minister of Trade & Industry of interim government. He was also the leader of the Hutu Power faction of the Liberal Party. Froduald Karamira Vice President of the MDR and subsequent leader of “Hutu Power”, an extremist wing of the party. General Paul Kagame Commander of the RPF during the Rwandan Civil War and the genocide. He became the Vice president of Rwanda after the RPF took Kigali in July 1994 (though he was considered the de facto leader during Bizimungu’s presidency), and remains in power as the president of Rwanda in 2020. Parties PSD (Social Democratic Party) One of Habyarimana’s opposition parties, along with the Liberal Party and the MDR. Members of the PSD were targeted during the genocide as well for being open to talking with the RPF and for ostensibly destroying Hutu unity. CDR (Coalition for the Defense of the Republic) Hutu-dominated party which was staunchly anti-Tutsi. The CDR collaborated with MRND frequently, despite its position that the MRND ceded too much to opposition parties, especially the RPF. Their priorities were advancing the interests of the majority Hutu population. MRND (National Revolutionary Movement for Development) The only legal party in Rwanda from 1975-1991 under President Habyarimana. MRND was a northern Hutu-dominated party, rivaling the Parmehutu party which flourished in southern Rwanda. The party changed its name to the National Republican Movement for Democracy and Development after legislation was passed in 1991 allowing other parties to form in Rwanda. MDR (Republican Democratic Movement) Party established in 1991 following the decline of the MRND. MDR enjoyed most of its support from central-Rwandan Hutus RPF (Rwandan Patriotic Front) Rebel group, and later political party, who initially attacked Rwandan from Uganda in October 1990, beginning the Rwandan Civil War. The RPF was led by Paul Kagame during the genocide and continues to be in power under Kagame in Rwanda in 2020. PL (Liberal Party) Established in 1991, the Liberal Party was another opposition party to Habyarimana.