Online Library

Following the January 6 insurrection on the US Capitol, we sought to explore how two social media platforms were being used concurrently to disseminate far-right memes and discourse. Our study employs a mixed-methods approach to collect a large data set of images from 4chan/pol/ and using the “#stopthesteal” hashtag on Twitter between 1 January 2021 and 13 January 2021. Our findings reveal how each platform influenced the usage of memes toward identity building and far-right activism in the days leading up to and immediately after the insurrection. Our findings reveal that Twitter was used to mobilize users leading up to January 6 but led to in-fighting among the pro-Trump crowd in the days after. Meanwhile 4chan/pol users took advantage of the Overton window of the Insurrection to disseminate far-right ideology and attempt to recruit and radicalize disgruntled Trump supporters after the insurrection was deemed a failure.

Given that terrorism is omnipresent on social media, it is imperative to study how seeing terror content online is related to individuals’ attitudes, behaviors, and emotions. This study investigates how exposure to terrorism on social media associates with terror-related online self-disclosure and how self-disclosure, in turn, relates to fear of terrorism. A quota-based survey of young social media users (16- to 25-year-olds; N = 864) in Germany revealed that exposure to Islamist and far-right terrorism is related to higher online self-disclosure. Political ideology moderated the relationship between exposure to far-right terrorism and online self-disclosure, but not when exposed to Islamist terrorism. Attitudinal differentiation was negatively associated with self-disclosure. Additionally, we found an interaction effect of exposure to Islamist terrorism and attitudinal differentiation on self-disclosure. Finally, the results showed that online self-disclosure was positively related to fear of terrorism. By and large, our findings highlight the relevance of social media for the levels of fear.

Proliferation of social media platforms in recent past has resulted into upsurge in the number of users. Advent of these sites have paved way for the users to easily express share and communicate. In such a scenario, it is imperative to analyze the content and identify nasty content so as to avoid unpleasant situations. Machine learning techniques are extensively used for this purpose. In this paper, we propose a language model for the identification of hate speech in twitter data. Distil-BERT, a context aware embedding model along with Support Vector Machine (SVM) for the classification of hate speech has been used. SVM with a 10-fold cross validation and linear kernel has been found to provide better accuracy as compared to existing models. Results show that accuracy is improved with the use of context aware embedding model.

VOX-Pol Blog post.

In the aftermath of the Boston Marathon bombings Bipartisan Policy Center Homeland Security Project co-chair and former Governor Tom Kean, a 9/11 Commission co-chair, talks to BBC World News about the dangers of online radicalisation. Originally published by the BBC on 26 April 2013

The proliferation of social media has created a terrorist finance vulnerability due to the ease with which propaganda can be spread, promoting fund-raising for a certain cause. Social media companies recognize the importance of preventing violent extremist and terrorist content, but less attention is paid to their fund-raising role. As well as presenting a threat, the movement of terrorist fund-raising activities online creates a disruption opportunity. This article argues that social media companies need to display greater awareness of their vulnerability to supporting terrorist financing and greater collaboration with law enforcement and financial institutions to strengthen the integrity of the system against abuse.