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The Effect of Extremist Violence on Hateful Speech Online

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The Effect of Extremist Violence on Hateful Speech Online Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018) The Effect of Extremist Violence on Hateful Speech Online Alexandra Olteanu Carlos Castillo Jeremy Boy Kush R. Varshney IBM Research UPF, Barcelona UN Global Pulse IBM Research [email protected] [email protected] [email protected] [email protected] Abstract same period, several deadly terror attacks have occured in Western nations (Wang 2017; Global Terrorism Database User-generated content online is shaped by many factors, in- cluding endogenous elements such as platform affordances 2017), leading to an increasingly alarming anti-Muslim and norms, as well as exogenous elements, in particular sig- rhetoric (TellMAMA 2017) by right-wing populist move- nificant events. These impact what users say, how they say ments (Greven 2016) and right-leaning media outlets (Wor- it, and when they say it. In this paper, we focus on quan- ley 2016), often conflating refugees and Muslims with Is- tifying the impact of violent events on various types of hate lamic fanatics (Diene` 2006). This rhetoric has also gained speech, from offensive and derogatory to intimidation and ex- adoption online (UNGP and UNHCR 2017), prompting gov- plicit calls for violence. We anchor this study in a series of ernmental agencies2 and NGOs to call on social media plat- attacks involving Arabs and Muslims as perpetrators or vic- forms to step up their efforts to address the problem of hate tims, occurring in Western countries, that have been covered speech (Roberts 2017; TellMAMA 2017). The concern is extensively by news media. These attacks have fueled intense that the increase in hateful narratives online led to an up- policy debates around immigration in various fora, including online media, which have been marred by racist prejudice and surge in hate crimes targeting Muslim communities (Roberts hateful speech. The focus of our research is to model the ef- 2017). Insights into how online expressions of hate thrive fect of the attacks on the volume and type of hateful speech and spread can help stakeholders’ efforts to de-escalate ex- on two social media platforms, Twitter and Reddit. Among isting tensions (Burnap and Williams 2014). other findings, we observe that extremist violence tends to In this paper, we explore how hate speech targeting spe- lead to an increase in online hate speech, particularly on mes- cific groups on social media is affected by external events. sages directly advocating violence. Our research has implica- Anchoring our analysis in a series of Islamophobic and Is- tions for the way in which hate speech online is monitored lamist terrorism attacks in Western countries, we study their and suggests ways in which it could be fought. impact on the prevalence and type of hate and counter-hate speech targeting Muslims and Islam on two different social 1 Introduction media platforms: Twitter and Reddit. Hate speech is pervasive and can have serious consequences. Our contribution. We conduct a quantitative exploration of According to a Special Rapporteur to the UN Humans the causal impact of specific types of external, non-platform Rights Council, failure to monitor and react to hate speech in specific events on social media phenomena. For this, we cre- a timely manner can reinforce the subordination of targeted ate a lexicon of hate speech terms, as well as a collection of minorities, making them “vulnerable to attacks, but also in- 150M+ hate speech messages, propose a multidimensional fluencing majority populations and potentially making them taxonomy of online hate speech, and show that a causal in- more indifferent to the various manifestations of such ha- ference approach contributes to understanding how online tred” (Izsak´ 2015). At the individual level, people targeted hate speech fluctuates. Among our findings, we observe that by hate speech describe “living in fear” of the possibility that extremist violence attacks tend to lead to more messages online threats may materialize in the “real world” (Awan and directly advocating violence, demonstrating that concerns Zempi 2015). At the level of society, hate speech in social about a positive feedback loop between violence “offline” media has contributed to fuel tensions among communities, and hate speech online are, unfortunately, well-founded. in some cases leading to violent clashes (Izsak´ 2015). Paper Outline and Methodology Overview. Outlined in Following one of the most severe humanitarian crises Figure 1, our approach consists of several steps: in recent history, Europe has seen a high immigration in- 1 Step 1: We create a longitudinal collection of hate speech flux, including Syrian, Afghan, and Iraqi refugees. In the messages in social media from Twitter and Reddit that cov- Copyright c 2018, Association for the Advancement of Artificial ers a period of 19 months. This collection is based on a series Intelligence (www.aaai.org). All rights reserved. of keywords that are obtained through an iterative expansion 1World Economic Forum (Dec. 2016) “Europe’s refugee and migrant crisis” https://www.weforum.org/agenda/2016/12/ 2BBC News (Sep. 2017) “Social media warned to crack down europes-refugee-and-migrant-crisis-in-2016-in-numbers on hate speech” http://bbc.com/news/technology-41442958 221 Step 1: Data acquisition & lexicon construction Step 2: Data & terms categorization Step 4: Impact analysis on time series Collection of queries Data sources Target group Severity Stance […] Time series generation Reddit: volume of posts/ Social media Promotes violence comments/users Initial hate query Muslims Twitter: volume of tweets/ Twitter Immigrants Intimidates Framing […] unique tweets/users (Decahose) Expanded query terms […] […] Impact analysis Pre-intervention Post-intervention Reddit (Search API) External hate lexicon Step 3: Event selection News Original time series Conterfactual time series (volume & topics) Themes/ Locations GDELT … Results Query expansion (manual & automated steps) Figure 1: Steps in our analysis framework: (1) data acquisition and lexicon creation, §3; (2) data categorization, §4; (3) events selection, §6; (4) impact analysis on time series of hate related terms, §5 and results, §6. of known hate speech terms (§3). mittee of Ministers, Council of Europe 1997). We extend Step 2: We categorize the data along four dimensions: 1) the this definition to further consider as hate speech “animosity group each message refers to, 2) the attitude of speakers, or disparagement of an individual or a group on account of a 3) the severity of hateful expressions, particularly whether group characteristic such as race, color, national origin, sex, they advocate violence, and 4) the framing of content (§4). disability, religion, or sexual orientation” (Nockleby 2000), Step 3: We select 13 extremist attacks involving Arabs and allowing us to juxtapose hateful speech directed at Muslims Muslims as perpetrators or victims, like the Berlin Christmas with other groups (e.g., LGBTQ, immigrants). market attack on Dec. 2016, perpetrated by a follower of Counter-hate speech. Censoring hate speech may clash jihadist group ISIL, or the Quebec City mosque shooting on with legal protections on free speech rights. Partially due to Jan. 2017, by a far-right white nationalist (Table 2). this tension, the position of international agencies like UN- Step 4: As evaluating the effect of such attacks on vari- ESCO is that “the free flow of information should always be ous slices of social media is a causal question, we frame the norm. Counter-speech is generally preferable to suppres- it as measuring the impact of an intervention (event) on a sion of speech” (Gagliardone et al. 2015). Thus, it is impor- time series (temporal evolution of speech). Following tech- tant not only to study hate speech, but also to contrast it with niques for causal inference on time series (Brodersen et al. counter-speech efforts—a rare juxtaposition in social media 2015), we estimate an event’s impact on various types of research (Benesch et al. 2016; Magdy et al. 2016). Magdy hate and counter-hate speech by comparing the behavior of et al. (2016) estimate that a majority of Islam and Muslim corresponding time series after an event, with counterfactual related tweets posted in reaction to the 2015 terrorist attacks predictions of this behavior had no event taken place (§5). in Paris stood in their defense; an observation also made by The last sections present (§6) and discuss (§7) our results. UNGP and UNHCR (2017) following the 2016 terrorist at- tack in Berlin, and supported by our own results (§6). 2 Background & Prior Work Hate speech and violent events. The prevalence and We are interested in the relation between online hate speech severity of hate hate speech and crimes tends to increase and events. To ground our study, we first review work defin- after “trigger” events, which can be local, national, or ing hate and counter-hate speech. Given our focus on anti- international, often galvanizing “tensions and sentiments Muslim rhetoric in the context of extremist violence, we against the suspected perpetrators and groups associated outline previous works on hate speech after terror attacks, with them” (Awan and Zempi 2015). For instance, Benesch and studies of hateful narratives targeting Muslims. We also et al. (2016) found extensive hate and counter-hate speech cover observational studies on social media, particularly after events that triggered widespread emotional response those focusing on harmful speech online. like the Baltimore protests, the U.S. Supreme Court de- cision on same-sex marriage, and the Paris attacks during Hate Speech Online and Offline 2015; while Faris et al. (2016) found spikes in online harm- Defining hate speech. While hate speech is codified by ful speech to be linked to political events. While these stud- law in many countries, the definitions vary across jurisdic- ies are related to ours, they focus on content posted during tions (Sellars 2016). As we study hate speech directed at specific events, or on correlating changes in patterns (e.g., followers of a religion (Islam) that is often conflated with a spikes) with events’ occurrence.
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