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Certain studies of social conflicts and geopolitical processes through online social networks entail qualitative analysis. One such issue is the tension between Western and Muslim societies. We introduce computer-assisted qualitative sentiment analysis for the inquiry and extraction of varied sentiments. The analysis explores the prevalent meanings of the term jihad through discussions of Muslims and non-Muslims in the online public sphere. After examining 4,630 Facebook comments and replies, our examination leads to a holistic mapping that details “peaceful,” “moderate,” and “radical” opinions regarding jihad, which is an integral institution of the Muslim world. Through this method, we suggest a “Muslim–non-Muslim tension indicator,” which can be used in a range of political analyses.

Which functions do social media fill for non-state armed groups in countries with internal armed conflict? Building on conflict data, interviews and media monitoring, we have reviewed the use of social media by Myanmar’s nine most powerful armed groups. The first finding is that they act like states, using social media primarily to communicate with their constituents. Second, they also use social media as a tool of armed struggle, for command and control, intelligence, denunciation of traitors, and attacks against adversaries. Third, social media serves for national and international outreach. Like Myanmar’s national army, the armed groups have combined prudent official pages with an underworld of more reckless profiles and closed groups that often breach Facebook’s official community standards. In February 2019, when Facebook excluded four groups from its platform, they lost much of their ability to reach out and act like states. Yet they kept a capacity to communicate with their constituents through closed groups, individual profiles and sophisticated use of links and shares. Finally, the article affirms that the Facebook company, in the years 2018–2020,took upon itself a role as an arbiter within Myanmar’s internal conflicts, deciding what information was allowed and disallowed.

It is now widely understood that extremists use the Internet in attempts to accomplish many of their objectives. In this chapter we present a web-crawler called the Terrorism and Extremism Network Extractor (TENE), designed to gather information about extremist activities on the Internet. In particular, this chapter will focus on how TENE may help differentiate terrorist websites from anti-terrorist websites by analyzing the context around the use of predetermined keywords found within the text of the webpage. We illustrate our strategy through a content analysis of four types of web-sites. One is a popular white supremacist website, another is a jihadist website, the third one is a terrorism-related news website, and the last one is an official counterterrorist website. To explore differences between these websites, the presence of, and context around 33 keywords was examined on both websites. It was found that certain words appear more often on one type of website than the other, and this may potentially serve as a good method for differentiating between terrorist websites and ones that simply refer to terrorist activities. For example, words such as “terrorist,” “security,” “mission,” “intelligence,” and “report,” all appeared with much greater frequency on the counterterrorist website than the white supremacist or the jihadist websites. In addition, the white supremacist and the jihadist websites used words such as “destroy,” “kill,” and “attack” in a specific context: not to describe their activities or their members, but to portray themselves as victims. The future developments of TENE are discussed.

Exposure to hate material is related to a host of negative outcomes. Young people might be especially vulnerable to the deleterious effects of such exposure. With that in mind, this article examines factors associated with the frequency that youth and young adults, ages 15 to 24, see material online that expresses negative views toward a social group. We use an online survey of individuals recruited from a demographically balanced sample of Americans for this project. Our analysis controls for variables that approximate online routines, social, political, and economic grievances, and sociodemographic traits. Findings show that spending more time online, using particular social media sites, interacting with close friends online, and espousing political views online all correlate with increased exposure to online hate. Harboring political grievances is likewise associated with seeing hate material online frequently. Finally, Whites are more likely than other race/ethnic groups to be exposed to online hate frequently.

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93 % AUC for extremist user detection, up to 80 % AUC for content adoption prediction, and finally up to 72 % AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.