Political Contagions

A Thesis

Presented in Partial Fulﬁllment of the Requirements for the Degree Master of Arts in the Graduate School of The Ohio State University

Kyle E. Davis, M.A.

Graduate Program in Department of Political Science

The Ohio State University

2019

Master’s Examination Committee:

William Minozzi, Advisor Michael Neblo c Copyright by

Kyle E. Davis

2019 Abstract

This thesis explores the comparison between political misinformation and biological infections. The current thesis addresses problems in political science and communications involving measurements of political eﬃcacy and reverse causality with misinformation studies. The thesis ends by suggesting a new way forward, by borrowing methodologies developed in epidemiology to best address reoccurring issues of misinformation in American public opinion. Doing so will allow readers to better categorize knowledge, forecast real-world phenomena, and recognize the dangerous structure and composition that exists with misinformation.

ii Chapter 1: Introduction

In 2017, suspicions arose that Russian influence in the United State’s 2016 pres- idential election would influence the way the public would vote. This influence was confirmed to be in favor of now President Donald Trump by intelligence officials and partially included influencing public opinion through social media [1]. Other times, however, Russian “meddling” just included trying to create divisions in the Ameri- can electorate, including race tensions alongside the Black Lives Matter movement of 2018 [2]. This paper will ultimately discuss the effect this influence has, and how scholars may better model it.

These Russian opinion sharing channels, BlackToLive and DontShoot, were widely shared among the Black Lives Matter community - going viral and a part of the larger political discourse [2]. Beyond online, public discourse also includes the interpersonal conversations at the coﬀee and barber shops [3]. The American National Election

Service (ANES) Time Series data asking respondents how often they talk politics with friends and family indicate a hyperactive political environment. Over time, respondents have reported talking politics with friends and family at about a 70% rate; of those who responded that they do talk politics report that they do so very frequently. The trend of talking about politics seven days a week is generally modest from those who say they talk politics two days a week. These numbers could indicate

1 Figure 1.1: ANES Time Series Data: Political Talk Frequency

one of two things, either the public is very politically active, or (with skepticism of self-report surveying) the public thinks it is best to be politically active. In either case ideas will spread and it is critical to understand how political misinformation

(factually false information) or disinformation (intentionally malicious information) are a part of a larger system of susceptibility, infection, and recovery. I will frequently just mention misinformation, but often disinformation could be equally swapped out.

I will highlight distinctions as they occur.

This thesis frequently compares political information to an infection. This analogy is useful for a few reasons. First, it properly highlights the importance of political misinformation and disinformation. Second, deﬁning the components (susceptibility,

2 infection, and recovery) help researchers understand the larger structure and composition behind misinformation ﬂow. Last, Models used to address biological infections can be similarly used to track political infections.

Some scholars have warned the usage of biological comparisons in social science, often because neither the methodology nor theories ever align [4]. As I will argue, comparing political misinformation to that of a foreign infection highlights the importance without abandoning methodological and theoretical insights. I will explain in Chapter 6 the methodological and theoretical similarities. In Chapter 2 I will describe how being “infected” with political misinformation leads to developing our understanding of political efficacy better than ever before. This has additional ben- efits of forcing researchers to operationalize and differentiate between states of being susceptible, infected, or recovered. The same is true for communications research in that researchers need mix individual and sociological insights to understand how misinformation spreads similar to an infection. Chapter 3 will unveil this literature and begin to discuss problems with reverse causality.

These two problems, better understanding measures of eﬃcacy in political science and modeling the larger structure and composition of misinformation for communications, are solved by utilizing methodological and theoretical insights from the bio-sciences. Beyond this, these tools will give researchers the the power to not only explain human behavior more precisely but to model predictions of epidemics when they occur.

In the next section I’ll describe how political sophistication is the best dependent variable for understanding when someone is susceptible, and one could recovery from

3 misinformation. The history of public opinion to this point is discussed, and operationalization is speciﬁed. Following this I’ll describe how information is transferred between individuals - citing relevant literature in mass communication research. Fi- nally, the paper will close by replicating some models predicting the above, and suggesting a new way forward.

1.1 Organization of this Thesis

The rest of this thesis is organized as follows.

Chapter 2 will introduce the problems in the political science literature related to efforts to measure public efficacy. I will then connect one particular measure of political sophistication to the analogous infection model and introduce how it is essential for understanding the effects of political misinformation.

Chapter 3 will introduce the study of information spread itself. This includes research in mass media and sociology. I will compare eﬀorts in psychology and sociology to describe the sharing of political information. I will end by declaring hypotheses based upon diﬀering ways information can spread among individuals or across mass media.

Chapter 4 tests a hypothesis made in Chapter 3 and highlights the possible confounds to political discussion and sophistication. Chapter 5 will discuss the implica- tions that come from the models reported in the Chapter.

Chapter 6 will suggest a way forward: drawing connections and reporting how each component of political contagion are a part of a larger system. This section will also introduce the reader to stochastic contagion analysis and theorizing.

4 Appendix A explains the data used to create Table 4.1. This includes question wording, re-coding, and residual testing.

5 Chapter 2: The Problem To Be Solved in Political Science

Foreign intrusion in American discourse causes understandable unease. On one hand the United States is a historical champion of free speech and deliberation. On the other hand the United States suffers more than authoritarian states by having such open access to information. If foreign “meddling” causes a significant effect in public opinion, and since we cannot stifle free speech or the public’s interest in politics, then there should be some response to counteract or inhibit such meddling. Yet, to care about the problem foreign encroachment we first have to understand the effect it actually has. First understanding the public’s efficacy of information is essential, and not a new area of research as scholars have modified the dependent variable in public opinion research over the years to better ascertain what is most important to

American democracy. Starting with political eﬃcacy is important in understanding both susceptibility of misinformation and possibility of recovering - and how these have been deﬁned over time.

Research on public eﬃcacy has developed over time and territory. At its founda- tion, normative theory has long argued the merits of a deliberative democracy and the role of the public. The study of the public’s role in democracy was of importance to as early as ancient Athens,[5] and has continued to connect the importance of public deliberation to empirical study [6]. Notable theories that have grown from

6 this line of research include deliberative democratic studies,[7] economic theories of democracy,[8] an urgency for republican practices,[9][10] and a skepticism of eﬀective political discourse in the ﬁrst place [11].

Scholars have studied public eﬃcacy with information in many diﬀerent ways.

Richard Lau and David Redlawsk looked at election results using a measure of “correct votes.” They took each voters partisanship, policy positions, and evaluations of candidate performance as givens and found that an average of 70 percent of voters chose the candidate who best matched their own modeled preference. This result lead Lau and Redlawsk to be “pleasantly surprised”[12]. In the view of democratic

“realists,” that 70% ﬁgure is either not high enough or is biased due to voters lack of information on candidates evaluations and performances. This skepticism lies in the myriad other studies on this topic; for example, few respondents can name their own representatives when asked in an open response. Granted, if names are listed in a multiple-response then respondents are better at recalling their representative but only to marginally higher rates.

How can we expect the public to delineate between fact and fiction when research is rife with the troubling finding of public ignorance about politics? Findings such as these lead scholars, including Walter Lippmann (1925), to write that the unattain- able ideal of “the omnicompetent, sovereign citizen” is bad in just the same sense that “it is bad for a fat man to try to be a ballet dancer”[13]. This recognition too is built into the economic theory of democracy, found most plainly in Down’s median voter theorem [14]. John Zaller, in 2012, commented that the median voter theorem had “great curb appeal” because “the rationally ignorant median voter gets what he wants without much effort”[15]. However, as Achen and Bartles note, there is a

7 logical problem with this unidemensional modeling of democracy because in a mul- tidementional space where we include individual preferences across various stages of rationally it is unclear how the economic model will explain the “coherent will of the people”[11, p. 30].

Beyond the economic measures of democracy, public efficacy has too been measured via “don’t know responses.” One issue with these measures is that it is hard to delineate between “don’t know” and “don’t care”[16]. In the first case it is meaningful to see how citizens become knowledgeable, in the latter case it is meaningful to see under what conditions participants care. In either case the “don’t know” response acts as a great base-standard of public efficacy, but it is impossibly seperable to “low road” survey respondents![17]. Scholars have tried to get around this by measuring the actions of the public to see how much they care politics (thus likely to be of higher efficacy). One measure that captures this is the measure of political participation and engagement [18].

Where a canonical political knowledge index often includes questions such as “How many Justices sit on the Supreme Court?” or “ How long is a senator’s term?”1 political engagement questions often include questions such as “do you wear a political button?” and “do you display a political yard sign?” The initial wave of results for eﬀective public citizenship lead to studies to attempt to redeem the public in The

Changing American Voter [20]. However, the improved public eﬃcacy found in The

Changing American Voter was later found to be a function of changing questions from previous studies not a function of an improved citizenry [21].

1A question which, as Delli Carpini found, had only a 25% correct response [19].

8 Be it “correct vote,” the median voter theorem, political knowledge, “don’t know,” or civic engagement, a more fundamental concern is that there is no reason why anyone should theoretically care about these measures when it comes to political misinformation. Political knowledge especially does not capture the normative dangers that misinformation pose, given both its pop-quiz-like nature, and its disengagement from political eﬃcacy. If we ultimately care about a healthily skeptical public in the face of foreign encroachment, no one should care whether current citizens know who the

Chief Justice is, has a political bumper-sticker, and admits they forgot what the Get- tysburg Address was about. Scholars ought care more about the passion the citizens show for correct political information, their practice of investigation in selecting political information, and their nuance in handling their political opinions and how these opinions connect to their larger ideology. I think these principles matter more for an enlightened citizenry, and I think they are best captured by the popular measure of political sophistication.

Political sophistication has been measured a few diﬀerent ways and has its origins in political psychology. Robert Luskin wrote the aptly named “Measuring Political

Sophistication” article which investigated these operationalizations and their advan- tages and consequences [22]. For Luskin, sophistication is the “density, coverage, and organization” of one’s belief system, or associations (constraints) in a similar sense to prior work by Converse and other “Michigan School” scholars of public opinion [23].

These political belief systems, in the most simple sense, are a measure of a respondent associations, or considerations, for their decisions.2 In the least sophisticated sense a participant may (to use Luskin’s example) “as well be living on Neptune for all they

2This has it’s roots, if not is directly correlated with the ﬁle drawer model of public opinion.

9 know about politics; others are walking Washington Posts or New York Timeses” [22, p. 859]. Those with a dearth of political sophistication would know little about politics with little sources as references to their knowledge. Those with high political sophistication would be unlike “scattered croutons ﬂoating in undiﬀerentiated cognitive soup” but rather wielding “large, intricate lattices of cognitive material” [24] in

[25].

Another component of political sophistication recognizes the range of political belief systems. Some respondents may be narrowly interested in race relations, abortion, and the electoral college. Yet, the political sophistication index would reward those respondents with a wide range of political interest and specialization from foreign policy, to ﬁscal know-how, to other topics previously mentioned that others may be narrowly specialized in.

The ﬁnal component of political sophistication re-introduces Converse’s constraint.

Those with more interconnection of different factoids of political knowledge across their system’s range are rewarded for having a tighter neural network of ideas rather than knowledge floating in their own particular siloed category. Here I think democracy is benefited by this particular component within the sophistication operationalization because this gets at citizens being able to help others with their knowledge in what another may care about, and perhaps treat an idea with particular nuance from multiple areas of insight.

No measure is ever perfect, however I think the sophistication measure best captures the components we ought care about when political misinformation is shared online. A decline in sophistication would mean a falling out with politics or isolation

10 on a particular news source, whereas an increase in sophistication would show how misinformation stands out sharply from reality.

Last, time is important for measuring any changes to political sophistication.

Spreading political information both inﬂuences sophistication, and is a product of increased sophistication. This cyclical process requires individualized data analysis of those who are new to a source, contain it, and spread it. Sophistication does not comment on the actual transfer or communication mechanism of online ideas, it is just a product and producer of it.

Political sophistication is essential in the larger analogous infectious system. Using this variable would predict that those with scattered belief systems would be more likely to be influenced by political misinformation (or disinformation). Likewise, those with “large intricate lattices of cognitive material” have the resources similar to antibodies to fight off an infection. For example, misinformation on American race relations can be thwarted with additional orthogonal knowledge about economics, history, community development efforts, or any additional knowledge that may address a specific claim.

Research in political science has yet to use the measure of sophistication in this way: as a prevention to political misinformation. Beyond this, political science has yet to connect these ﬁndings to the larger structural problem involving how political information is communicated and spread. In the next section I hope to bridge these two to construct the larger systemic problem between the two ﬁelds.

11 Chapter 3: The Problem To Be Solved in Communications

Modern social science has become increasingly fascinated with individual psychological effects of communication even though many of the “giants” of social science have offered firm sociological shoulders to “stand upon.” For example, the accusation of selling military secrets by Alfred Dreyfus in 1898, alongside the rise of the newspaper industry, led to one of the worst political scandals in history with public being staunchly divided between those who believed Dreyfus and those who believed him to be a traitor to the French army. As scholars have noted before, it is the modeling of unintended effects, such as those by the French newspapers, that are important for monitoring public relations [4].

However it is not just discussions of past events, modern research too has become increasingly concerned with sociological effects in public discussion. What was once deliberated in British coffee houses and French salons now is commented on by thou- sands online. However the issues discussed are no easier, and the conversations no more or less difficult. Scholars now are privileged with big data and technological advancements that allow the use of methods that can model, measure, explain, and predict complex human behavior. Now more than ever communication research can model and explain the unintended effects of political information across any available modality.

12 Certainly psychological dynamics of interpersonal communication remain – perhaps this is impossible to ever be free of. However the sheer dearth of whole-network or stochastic modeling analysis warrants diﬀerent approaches to answering these age old questions. However, at its core, there is an element of psychology related to learn- ing - processing and retaining information provided from another person in real-time is essential to understand how information “sticks”. This may lead readers to derive that information transfer is not that complex but rather a series of dyadic relation- ships that are all weighted by their unique modiﬁers leading to some result for an individual.

This “series of dyads” line of research ignores many larger sociological inﬂuences however. Social networks are in themselves not formed by the individual but by small cumulative changes, sometimes seemingly random, from genesis onward. Com- munities and subcultures are all nested in unique pockets in society, independent of individual psychological phenomena. These matter because the very community or development of a particular culture can in itself inﬂuence both the individual and the transfer of information between individuals – including groups of individuals. As a matter of design a study not examining whole network data cannot comment on the sociological complexity of human interaction.

What exactly an information “transfer” looks like can be diﬃcult in a methodological sense. Consider the motivating example of Russian inﬂuence on the Black

Lives Matter community through like-group racial propaganda sharing. As early as the 1940s, scholars have suggested ideas such as these follow a “two-step ﬂow”[26].

That is, that information ﬁrst travels to “opinion leaders” who then share the information outward. In this example, Russian misinformation remains siloed until shared

13 by the Black Lives Matter oﬃcial page, to the masses for consumption. This process has been expressed both in the mass media sense (as I have just described it) and for simple interpersonal interactions. It’s the connection of both psychological and sociological elements that help communication scholars communicate how individuals spread political infections.

Here the highly sophisticated would qualify as opinion leaders, who share information towards those willing to accept it, and sometimes information flows in the opposite direction. This theory is not just specific to misinformation, indeed positive factual political information (or scientific information, health information, etc.) can spread similarly.

It may be helpful to start with more manageable components and then build up to the larger ideal test of infection. Let’s first consider how information can travel from person to person, influencing their sophistication along the way. Is the transferring of information like a contagion, similar to a virus? After all, we do talk about “viral sen- sations” influencing public opinion all the time. Here we have no litmus tests, cotton swabs, or beakers to compare the infectious nature of political information. Rather, communication scholars have used a combination of comprehension checks, thought diaries, and panel studies to monitor effects of changing information, contexts, and behaviors.

There are a few processes that could occur between just a few individuals within a network. Consider citizen A, B, and C. These citizens, along with a whole alphabet of other citizens, happen to list each other as someone they “talk political matters” with in a standard name-generator survey question. This survey is collected across time and at each step along the way we measure the citizen’s sophistication. Without

14 .9 .4 A B C

Figure 3.1: Example of informational decay

going back into the operationalization of political sophistication, let’s just assume here that we’ve placed it on an ordinal -1 to 1 scale. To better display a breakage in sophistication ﬂow between two nodes I will interrupt the tie with a break (//). A break would occur if two individuals could have been connected but did not discuss political matters.

The ﬁrst, and perhaps most common, observation may be simple informational decay. Similar to the telephone game, information here would be transmitted from a node (A) down to another (B) and that node may then inﬂuence further nodes (C).

In the case of the decay, the rate at which the sophistication fails to transfer to others matters. Going from an almost complete transfer (near 1) only yielded a .4 transfer from B to C in the second degree. This transfer process makes the most intuitive sense as the closer to the source you are the more comfortable and knowledgeable you may be to argue in favor of it.

What is most relevant for informational decay is closeness to source. That is, in a mass media sense, the sharing of information eﬀects the receivers of the shared information the same as the host. However, as shown above, in an interpersonal scenario participants are likely to be more comfortable sharing and receiving information the closer they or the host is to the source of the infection.

In a similar vein, nodes may be connected with transfers only existing between certain kinds of nodes. For instance, nodes A and B may talk politics with one

15 .9 A B // C

Figure 3.2: Example of information recall.

another but do not spread the information outwards beyond a tight-knit group. In this case, we ﬁnd that B has gained a change in information from A from t1 to t2.

Yet, in t3, instead of transferring information onward to C, A instead increased in sophistication from a continued dialogue between A and B. Note that reciprocity of information does not often occur in two-step ﬂow models.

The reciprocal nature of information exchange adds an additional complication for properly modeling political infections. Not only can an individual be siloed in sophistication but communities themselves can exist where only certain information is allowed in, and the most popular ideas rise to the top of the news feed.

In another case, An individual may talk politics with B but B may not show any eﬀects at all. Yet, the mere act of practicing one’s thoughts increased the eﬀects within A. This process acts more of a way to explain variation in an isolated nodes’ sophistication between time points, rather than to suggest network loops of self- conversation.

To delineate between a self-feeding sophistication transfer and a recall transfer from another node, we would need to respect the time dynamics between the stages in the sophistication transfer. I argue that sophistication is not born out of one particular conversation or transfer but rather exists in small nudges across a long- memory process.

16 0 A B // C

0.8

Figure 3.3: Example of self-feeding information

Yet, the possibility of self-feeding sophistication poses a large causal problem to communication scholars. For how would we ever know if changes in individual behavior are because of an infection (or correction) or because of individual practicing of information? Here the answer would be that we are not necessarily interested in the psychological or individual diﬀerences from time point to another, but rather these processes explain the myriad variations in the larger communal sense. If our goal is to determine the potential devastation from political information then the scope remains large with these individual processes existing in all of their various forms under the metaphorical hood of a larger machine.

To this point, I have highlighted the importance of political sophistication as both the antecedent to preventing a political infection, and the catalyst for it if one is of sioled political information. The sharing of information itself can take many forms if examined on the individual level, and is nearly impossible to examine causally.

However, two correlative hypotheses can be made:

H1: Those with higher political sophistication are more likely to discuss politics.

H2: Those with higher political sophistication are less likely to be inﬂuenced by

political misinformation.

17 The data required for this can be survey based, and would just require opera-

tionalizing discussion and sophistication, along with a variety of controls. Proving

H1 and H2 would reconﬁrm previous research, but it would not describe the more

speciﬁc process identiﬁed or how the aggregated infection of misinformation would

enter and (hopefully) leave a population. Yet, for the purposes of this thesis it is

ﬁrst important to know the possible confounds before making a more formal test of

misinformation ﬂow.

In the next section I test H1 ﬁrst, ﬁnding the confounds and the relationship

between sophistication and reporting discussion. Following this, I will report how

contagion modeling can help us test H2 and simulate the overall eﬀect of misinformation on a population. Doing so could utilize novel data and methods to answer age old questions [4] involving the larger system of information sharing and the potential diﬀerences between varying infection dynamics.

18 Chapter 4: Methods and Discussion

Following past research, previous studies have used general linear models to ﬁnd the variables that increase discussion and sophistication. Here I closely replicate one study from Diana Mutz in 2002 - using the same year data from 1992’s ANES [27].

This is cross-sectional data from the same data source reported in Chapter 1. The dependent variable of Mutz’s study is likelihood to vote, rather than the eﬀects of sophistication. Yet the two are similar in their care for discussion, and the dependent variable as a catalyst for interactivity. In the following model I include many of the same variables others have included: TV News frequency, newspaper readership, party strength, political interest, education, race (black), age, being female, income, and discussion. All variables are coded in the same ordinal direction, and discussion, news, and newspaper frequency is coded by how many days the person reports it (0 to 7). Sophistication, albeit not perfect, is the simple interaction between reported newspaper readership and television news viewership. More speciﬁc information about the variables used, and additional model statistics can be found in

Appendix A.

The results of the two models shown in Table 4.1 describe the variables that best explain the likelihood of discussion and sophistication. For every day of increased television news viewership participants are more likely to discuss politics (β = 0.07).

19 Table 4.1: Statistical models

Discussion Sophistication Intercept −0.76∗ 2.72∗ [−1.48; −0.03] [1.34; 4.11] TV News 0.07∗ [0.02; 0.11] Newspaper 0.01 [−0.03; 0.05] Party Strength (Republican) −0.00 −0.06 [−0.06; 0.06] [−0.18; 0.05] Political Interest 1.09∗ 1.01∗ [0.92; 1.26] [0.66; 1.36] Education Year 0.12∗ 0.16∗ [0.07; 0.17] [0.07; 0.26] Black −0.49∗ 0.87∗ [−0.85; −0.14] [0.18; 1.56] Age −0.00 0.08∗ [−0.01; 0.00] [0.07; 0.10] Female −0.10 −0.58∗ [−0.36; 0.15] [−1.07; −0.09] Income −0.01 −0.02 [−0.03; 0.02] [−0.07; 0.02] Discussion 0.13∗ [0.02; 0.24] R2 0.20 0.19 Adj. R2 0.19 0.19 Num. obs. 1174 1174 RMSE 1.97 3.84 ∗ 0 outside the conﬁdence interval

However do not find the same effect for newspaper readership(β = 0.01 and party strength (β = −0.00). Political interest are significant in both discussion and developing sophistication(β = 1.09;1.01). This result mirrors Mutz’s (2002) finding which instead uses voting as her dependent measure of political action [27]. Education is

20 signiﬁcant in predicting discussion and sophistication(β = 0.12;0.16). Interestingly, being black decreases the likelihood of discussion (β = −0.49) yet increases the likelihood of sophistication(β = 0.87). Age, being female, and one’s income are not predictive of political discussion; yet, age and being female signiﬁcantly increase and decrease the likelihood of being sophisticated respectively. Finally, political discussion

(the dependent variable from the ﬁrst model) inﬂuences the likelihood of sophistication (β = 0.13).

This model’s purpose was to report the possible confounds to misinformation spread, and test the hypothesis (H1) that those with higher sophistication are more likely to discuss politics. Starting with the later, we ﬁnd that those who view TV

News networks more frequently are more likely to discuss politics with friends and family. Since political sophistication here is the sum of both TV News viewership and

Newspaper viewership we can see that the TV viewership half of the political sophistication variable would motivate those to discuss politics. This may be because TV

News are more frequently become more discussion based - a component that is diﬃ- cult to convey over a printed news medium. As suggested before, one’s discussion has a cyclical eﬀect on one’s sophistication (β = 0.13). This poses a problem for future researchers in justifying no reverse causality in designs involving political misinformation and political sophistication (as described in the previous chapter). Here the model is clear, being informed (a part of political sophistication) drives conversation, and conversation drives one’s sophistication.

These models also helped us understand relevant confounds. One can increase sophistication via political interest, continued education, age, and discussion. Al- though its coeﬃcient is low, the model also ﬁnds that strong Republican ideology can

21 reduce sophistication (β = −0.06). The model also reports community differences in sophistication among Black (β = 0.87 and female (β = −0.58) demographics. Of course these demographics themselves cannot be changed to increase sophistication, but more attention can be spent on those communities to further enhance sophistication (Black) and correct any dearth in sophistication (female). A lack of findings can sometimes be just as interesting as the significant findings themselves. Changes in income seem to not influence discussion or sophistication - suggesting that political infections may transcend socioeconomic differences.

Holistically, the model paints a picture of a co-play between discussion and sophistication where the two are entangled together - moderated by education, political interest, and one’s demographics. This is suggestive of a contagion-like data generating process, but does not accurately test it. In the following section I will discuss how the developing ﬁeld of contagion studies could be utilized by social scientists to more accurately study discussion phenomena.

Beyond this, the above model speaks more to individual diﬀerences than what contagion modeling yields. The trade-oﬀs in design choice will be discussed in the following section.

22 Chapter 5: Discussion on Contagion and Future Work

Thus far, this thesis has described an analogy of political contagions. Here someone becomes infected with political misinformation - if they do not have a sufficient political sophistication to fight it off it has the potential to spread. It spreads in mass based upon one’s political interest, education year, race, and TV News viewership.

On an individual basis it may spread in a variety of ways throughout time, but what we are most interested in is the larger sociological eﬀect of the information itself. Us- ing population statistics we can model and predict the larger eﬀect of misinformation in a similar way to how popular viruses such as ebola and H1N1 are modeled.

The kinds of data needed are the initial challenge - as it is with any empirical study. In an ideal world data would be collected before, during, and to the conclusion of a “viral” event. The total population is measured as is the number of initial infected individuals. The rate of infection is calculated over the ﬁrst few periods of time, or initially estimated given prior research. Likewise, a recovery rate is measured. These statistics are all one needs to begin the most basic SIR models of infection.

S I R

Figure 5.1: Standard SIR Model.

23 The standard method for studying deterministic models (rather than stochastic models) is the basic S.I.R model, where susceptible individuals become infected, and then recover (or die in the case of an actual infection). Here recovered individuals can no longer become susceptible or infected. These methods, popularized in the use against viruses like ebola, H1N1, and even the common cold have success both in policy and in academia. How these methods mirror over to political science and communication is diﬃcult but have had recent success [28].

Starting with “susceptible” individuals, what does it mean to be susceptible?

Perhaps being a political independent means you are open to being infected by the

Republican or Democrat “virus.” This is precicely the claim made by Volkening et al., using a dynamic system we can ﬁnd the relationship between voters and states to make fairly accurate election predictions [28]. Rather than being an independent voter, being susceptible to misinformation means being open to receiving political information in the ﬁrst place. In biostatistics, scientists identify a person’s particular threshold for receiving an infection via a parameter (Q) which in the political science could be measured via surveying individuals on a variety of questions to identify how willing they are to accept new information. In more advanced stochastic models, this Q parameter could be used to have varying levels of infection to scale against the recovery rate. These thresholds are important for identifying when information should have transferred, and when it should fail at creating an “infected” individual.

In a more simplistic case being susceptible could being just not being infected with misinformation, be it having no exposure or priors of a particular conspiracy theory, or false political factoid.

24 Being “infected” with political information comes off as pejorative. It is a bad circumstance in the case of political misinformation or disinformation, but it could also be reversed to a particular scientific fact or idea. Being infected involves walking around with a particular piece of information at the top of your recall or lying dormant awaiting activation. The probability of infecting another individual is also defined in biostatistics, this coefficient is the R0. If someone has an R0 = 1 (as it is with the common cold), it would signify that it’s likely the person will infect one other person.

This likelihood changes based upon one’s position within a network, and how many ties and how much interaction is possible per person. Some infections may be endemic, never truly leaving a population. Perhaps ideas like a superior race, or conspiracy theories are truly endemic (R0 never being 0). Researchers could test whether this is true using these models, or test if information recall (information being novel, or deep-seeded) is relevant to an endemic or not.

Recovering from political information is forgetting the information or correction with new information entirely (R0 = 0). This is diﬃcult for political science because

“recovering” and “death” never really happen like they do in biosciences. I would argue that nothing in social sciences is ever truly a “0” or out of question. Beyond this, evidence of “sleeper effects” can make us wonder if recovery ever happens or if the infection is just awaiting an individual to forget the source [29]. Mass communication studies are rich with articles trying to piece together how someone get’s influenced by different modalities and under different contexts - but few ever focus on recovery from these influences. Researchers could utilize biostatistics to examine information correction on a mass scale, or if misinformation can never truly be scrubbed out of a population. Of course, social science researchers need not to merely define “recovered”

25 individuals as a sharp 0, but this would require additional components of the SIRS model. Specifically, researchers would need to define the sharp difference between being recovered and susceptible - and where precisely the two may intercept.

These models do not need to end with recovery, perhaps individuals never truly recover from misinformation. Beyond this, do individuals become susceptible again or is it similar to viruses like the chicken pox - infecting individuals only once? In either case there are SIRS models which track susceptibility after recovery, and SIS models that ignore this recovery altogether. These additional models have their own variations based upon network dynamics, if a deterministic model is good enough, the sample size of a population, and if individual thresholds should be considered.

Below is an example simulated SIS model result, the data needed is the number of infected individuals, the total population, the infection rate, and the rate of recovery.

Based upon these we can model how individuals ultimately reach an equilibrium

(endemic). One insight we ﬁnd is that much of the work of spreading disinformation

(or a virus) occurs at the ﬁrst few time points (called generations). That is, if the infection rate is small enough it will not “go viral” and reach enough people to become endemic.

This simulation shows the deterministic model in solid lines, and overlays the stochastic variation with the red lower line being the number of infected individuals

(starting with 100) and the number of susceptible individuals on top (starting with

1,000). The infection rate of those 100 infected individuals is set to 1, with the recovery rate only being 0.5. This model predicts that around generation 7 and endemic would have formed with and average of about 500 infected individuals infecting and

26 Figure 5.2: Simulated SIS model with endemic equilibrium

27 becoming susceptible again until intervention. What “intervention” means in a political context is also theoretically diﬃcult, but could be the variable of interest in future studies.

One this is certain with SIS models of infection, if the information does not go viral early on in its infection, it will likely not form an endemic. There are likely many cases of disinformation on the internet that did not reach popularity and thus did not reach fruition. Researchers collecting data on this might be disappointed, they had the foresight to measure data early on in an infection - yet it did not reach a popular audience as they wanted. However even these “failed” infections are important. Failed infections provide a sort of counter factual that has yet to be documented in communication or political science research. What makes a case of disinformation fail? Have foreign governments gotten better at generating eﬀective disinformation? How eﬀective will disinformation be in our elections ten to twenty years from now?

Finally, an SIS model could report a slow infection. Perhaps only a few individuals begin infected. The above model starts with 10% of the population infected, but this is likely not often the case.3 What happens if the infection spreads from a small group of ﬁve people and the infection is not as popular, down 20% (to a 0.8 instead of 1)?

Here we see that an endemic doesn’t occur until many generations down the line

(oﬀ the chart). Using this intuition, even the most unpopular ineﬀective infections may cause an endemic if interesting enough to be communicated.

3Perhaps it is if a major TV news outlet spreads disinformation, in which these models could also be used for journalistic accountability.

28 Figure 5.3: Simulated SIS model, x1 = 5, λ = 0.8

29 Chapter 6: Contributions and Conclusion

Creating connections, there is a dearth of research on the dangerous eﬀects and dynamics of misinformation is the social sciences. As this paper has argued, researchers now live in a privileged time of accessible data and technology to answer questions new and old. With evidence of foreign election meddling, this may warrant novel approaches to the eﬀects of misinformation and disinformation on public opinion.

In this thesis I have compared misinformation and disinformation to a biological infection. One’s susceptibility and recovery depend upon one’s political sophistication, network positioning, and a host of confounding variables. Utilizing regression analyses I have reiterated the important confounds for determining discussion and sophistication: political interest, education, race, age, and gender. If examined on the individual level, infected individuals can spread information in a variety of ways including informational decay, feedback, and self-serving. However, if examined at a larger scope researchers could make accurate predictions about the misinformation and population as a whole.

This thesis has made progress on two essential problems. First, the problem of understanding political eﬃcacy is solved by suggesting we look at the symptoms before the cause. By examining political misinformation we can ﬁnd that political sophistication is most important, then model it’s relative value in a real world study of

30 misinformation - something that has never been done before. Second, by bridging the gap in communications research between individual and sociological research we can begin to address apparent problems of reverse causality. Controlling for individual sophistication, while helpful in individual examinations of misinformation, is danger- ously ineﬀective when considering the larger composition of how misinformation is spread.

The problems described in this thesis are better answered by using the comparison of misinformation to that of a political infection. The methods described in the previous chapter not only can account for individual thresholds, but can accurately model misinformation under a variety of conditions. This conditional research, as I have argued, can help us understand phenomena such as political endemics, diﬀering modalities, and network structures.

Researchers ought care more about these larger sociological questions involving misinformation. What are the conditions under which misinformation spreads best?

Are dangerous ideas such as racism endemic to a society, or can we hope for a better future? Are cases of disinformation becoming more effective, or is increasing internet efficacy proving to be the victor? What is the most effective “cure” to a mass epidemic of political misinformation?

The study of political discussion has its roots in democracy studies and public opinion, but also has institutional insights in representation, accountability, and elite messaging. For communication research, the study of misinformation is unique in its necessity for political listening to occur in order to “receive” information before

31 accepting it and integrating it with competing world views.4 This research also chal- lenges researchers to consider the mass effects of mass communication - a field largely dominated by individual psychological effects.

We already consider popular online phenomena as “going viral” - why not model it as such? Doing so would stand on the giants before it by controlling for long-known confounds and further deﬁning, categorizing, and innovating the social sciences.

4This runs alongside Zallar’s RAS model of public opinion [30].

32 Appendix A: Reporting on Table 4.2

This is the data that was used to produce the table in Table 4.1. The data used are from the 1992 ANES data set. Variables were re-coded to have an ordinal scale from 0 and up for aid with interpretation. Beyond this, not available (NA) and “don’t know” (DK) responses were removed in the regression.

Questions, Coding, Summary Statistics

TV News: NES[900072]

“How many days IN THE PAST WEEK did you watch the news on TV?”

Ranges from 0 to 7, removed NA and DK. n = 1356, µ = 5.011, σ = 2.516.

Newspaper: NES[900071]

“How many days IN THE PAST WEEK did you read a daily newspaper?”

Ranges from 0 to 7, removed NA and DK. n = 1357, µ = 4.02, σ = 2.919.

Party Strength (Republican): NES[923634]

Built from previous questions K1, K1a-c in NES codebook.

Ranges from 0 (Strong Democrat) to 6 (Strong Republican). Removed NA, other

33 minor party/refused to say, apolitical. n = 2445, µ = 2.705, σ = 2.027.

Political Interest: NES[900062]

“In this interview I will be talking with you about the recent elections, as well as a number of other things. First, I have some questions about the political campaigns that took place this election year. Q.A1: Some people don’t pay much attention to political campaigns. How about you? Would you say that you were VERY MUCH

INTERESTED, SOMEWHAT INTERESTED, or NOT MUCH INTERESTED in following the political campaigns this year?”

Ranges from 0 (Not Much Interested) to 2 (Very Much Interested), removed NA and DK. n = 1358, µ = 0.915, σ = 0.725.

Education Year: NES[900554]

“What is the highest grade of school or year of college you have completed?”

Ranges from 0 (no schooling) to 17 (graduate study), removed NA and DK n = 1356,

µ = 12.56, σ = 2.945.

Black: NES[924202]

Race was recorded based on interviewer perception, or respondents were asked if they were on a previous panel (1990).

Recoded dichotomous 0 is White, 1 is Black. Removed NA, DK, Asian/paciﬁc, Amer- ican Indian, other. n = 2392, µ = 0.1329, σ = 0.34.

34 Age: NES[900548]

Coded from household listings, given by respondent.

Coded from 17 to 94. Removed NA. n = 1359, µ = 44.83, σ = 0.725.

Female: NES[924201]

Sex was based on interview perception, or responders were asked if they were on a previous panel (1990). Removed NA. n = 2485, µ = 0.534, σ = 0.4989.

Income: NES[900664]

“Now we are interested in the income that you yourself received in 1989, not including any of the income received by (your spouse and) the rest of your family. Please look at this page and tell me the income YOU YOURSELF had in 1989 BEFORE

TAXES. This ﬁgure should include salaries, wages, pensions, dividends, interest, and all other income. (IF UNCERTAIN: What would be your best guess?)”

Coded from 01 (NONE OR LESS THAN $2,999) to 23 ($90,000 AND OVER).

Removed DK, refused to answer, and NA. n = 1273, µ = 9.522, σ = 6.35.

Discussion: NES[900070]

“How many days in the PAST WEEK did you talk about politics with family or

friends?”

Coded from 0 to 7 (every day), removing NA and DK. n = 1342, µ = 1.724, σ = 2.189.

35 Sophistication:

Sophistication was coded as the sum of both TV News viewership and Newspaper

viewership. Thus, the code ranged from 0 to 14 (every day, both mediums). This

measure of sophistication does not measure the interconnection of ideas, but estimates

it by assuming that those with high TV and newspaper readership are more likely to

be exposed to diﬀerent interconnected ideas. NA and DK were removed in previous

variables, mentioned above.

n = 1354, µ = 9.027, σ = 4.262.

Model Statistics

Figure A.1: Discussion Model 1’s residuals

36 Figure A.2: Sophistication Model 2’s residuals

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