Online Library

Current literature on online criminal and deviant groups recognizes the role of online forums in the transfer of knowledge and socialization of members, but debates on the role of the Internet in the socialization and radicalization processes in the context of online extremist groups. This study contributes to the discussion by examining online radicalization process through the use of social learning theory and social network analysis. This innovation allows for assessment on the impact of online interactions with forum members on radicalization process. Findings found strong support of differential association and differential reinforcement, but showed the possibility of other mechanisms, such as self-radicalization, at play. Findings from the study highlight the need to for theory integration, the inclusion of online peer association, and replication to address the complex phenomenon of online radicalization.

Do social media companies exacerbate antisemitism and hate through their own recommendation and amplification tools? We investigated how four of the biggest social media platforms treated users who searched for or engaged with content related to anti-Jewish tropes, conspiracy theories, and other topics. Three of them–Facebook, Instagram, and Twitter1 (now known as X after a rebranding by owner Elon Musk)–filled their feeds with far more antisemitic content and conspiratorial accounts and recommended the most hateful influencers and pages to follow. One platform, YouTube, did not.

In this joint study by the ADL Center for Technology and Society (CTS) and Tech Transparency Project (TTP), researchers created six test personas of different ages and genders and set up accounts for them on Facebook, Instagram, Twitter, and YouTube. Researchers had them search each platform for a basket of terms related to conspiracy theories as well as popular internet personalities, commentators, and video games. The study then examined the content that the platforms’ algorithms recommended to these hypothetical users.

Do social media and search companies exacerbate antisemitism and hate through their own design and system functions? In this joint study by the ADL Center for Technology and Society (CTS) and Tech Transparency Project (TTP), we investigated search functions on both social media platforms and Google. Our results show how these companies’ own tools–such as autocomplete and auto-generation of content–made finding and engaging with antisemitism easier and faster.1 In some cases, the companies even helped create the content themselves.

Our researchers compiled a list of 130 hate groups and movements from ADL’s Glossary of Extremism, picking terms that were tagged in the glossary with all three of the following categories: “groups/movements,” “white supremacist,” and “antisemitism.”2 The researchers then typed each term into the respective search bars of Facebook, Instagram and YouTube, and recorded the results.

Major social media platforms have recently taken a more proactive stand against harmful far-right content and pandemic-related disinformation on their sites. However, these actions have catalysed the growth of fringe online social networks for participants seeking right-wing content, safe havens, and unhindered communication channels. To better understand these isolated systems of online activity and their success, the study on Gab Social examines the mechanisms used by the far right to form an alternative collective on fringe social media. My analysis showcases how these online communities are built by perpetuating meso-level identity-building narratives. By examining Gab’s emphasis on creating its lasting community base, the work offers an experiential examination of the different communication devices and multimedia within the platform through a netnographic and qualitative content analysis lens. The emergent findings and discussion detail the far right’s virtual community-building model, revolving around its sense of in-group superiority and the self-reinforcing mechanisms of collective. Not only does this have implications for understanding Gab’s communicative dynamics as an essential socialisation space and promoter of a unique meso-level character, but it also reflects the need for researchers to (re)emphasise identity, community, and collectives in far-right fringe spaces.

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF‐IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF‐IDF unigram model confirming 0.67 F1 score. The proposed multi‐ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.