Online Library

This study considers the ways that overt hate speech and covert discriminatory practices circulate on Facebook despite its official policy that prohibits hate speech. We argue that hate speech and discriminatory practices are not only explained by users’ motivations and actions, but are also formed by a network of ties between the platform’s policy, its technological affordances, and the communicative acts of its users. Our argument is supported with longitudinal multimodal content and network analyses of data extracted from official Facebook pages of seven extreme-right political parties in Spain between 2009 and 2013. We found that the Spanish extreme-right political parties primarily implicate discrimination, which is then taken up by their followers who use overt hate speech in the comment space.

Who is likely to view materials online maligning groups based on race, nationality, ethnicity, sexual orientation, gender, political views, immigration status, or religion? We use an online survey (N = 1034) of youth and young adults recruited from a demographically balanced sample of Americans to address this question. By studying demographic characteristics and online habits of individuals who are exposed to online extremist groups and their messaging, this study serves as a precursor to a larger research endeavor examining the online contexts of extremism. Descriptive results indicate that a sizable majority of respondents were exposed to negative materials online. The materials were most commonly used to stereotype groups. Nearly half of negative material centered on race or ethnicity, and respondents were likely to encounter such material on social media sites. Regression results demonstrate African-Americans and foreign-born respondents were significantly less likely to be exposed to negative material online, as are younger respondents. Additionally, individuals expressing greater levels of trust in the federal government report significantly less exposure to such materials. Higher levels of education result in increased exposure to negative materials, as does a proclivity towards risk-taking.

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.

There can be few greater fears for a parent than their child being contacted by a stranger, indoctrinated with an extreme ideology, and encouraged to join a violent movement, all while accessing the internet from their bedroom. Children’s smartphones and computers
may be portals to the most dangerous places on earth. The use of the internet, and more specifically social media, by violent extremists is certainly nothing new. The technical skills and proficiency displayed by groups such as ISIS have been causing concern for governments, law enforcement, industry, schools, religious leaders, and parents around the world. In reality the radicalization of children is rare and particularly nuanced, and far from a linear process that exclusively occurs online. While the media have reported high-profile cases, such as the teenagers from Chicago who tried to leave the United States and join ISIS or the three girls from the United Kingdom who traveled through Turkey to Syria the frequency and nature of conversion to violent extremism is a lot more complex than often reported. Evidence from search histories, online interactions and social media profiles suggest that contact is being made by those intent on radicalizing others. However, while information can be sought and contact can be initiated, complete conversion to a violent ideology is not happening in isolation online. That is to say that the internet is serving as a facilitator, not a direct means of recruitment. In any case action needs to be taken to safeguard children and young people from these risks.

To prevent and counter the spread of illegal hate speech online, in May 2016, the Commission agreed with Facebook, Microsoft, Twitter and YouTube a “Code of conduct on countering illegal hate speech online”. The implementation of the Code of Conduct is evaluated through a regular monitoring exercise set up in collaboration with a network of organisations located in the different EU countries. Using a commonly agreed methodology, these organisations test how the IT companies are implementing the commitments in the Code.

To prevent and counter the spread of illegal hate speech online, in May 2016, the Commission agreed with Facebook, Microsoft, Twitter and YouTube a “Code of conduct on countering illegal hate speech online”. The implementation of the Code of Conduct is evaluated through a regular monitoring exercise set up in collaboration with a network of organisations located in the different EU countries. Using a commonly agreed methodology, these organisations test how the IT companies are implementing the commitments in the Code.