Online Library

I am interested in contributing further knowledge regarding the alt-right, white supremacy, and the Internet by exploring the sinister conducting of Zoom-bombing anti-racist events. Here, I will investigate how white supremacy through the Internet can lead to violence, abuse, and fear that “transcends the virtual world to damage real, live humans beings” via Zoom-bombing, an act that is situated in a larger co-option of the Internet by the alt-right and white supremacists, but has been under theorised as a hate crime.

The key aims of this report are: To show how Jihadist movements use web forums to consolidate their existing followers and to recruit new ones; to illustrate how Jihadists, and their online supporters, use theology and ideology to justify their violent actions; and to suggest how western governments can better challenge the worldview and ideology propagated on these forums

Purpose
With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.
Design/methodology/approach
This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.
Findings
Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.
Originality/value
At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

The Islamic State of Iraq and Syria (ISIS) has received a tremendous amount of media coverage in the past few years for their successful use of social media to spread their message and to recruit new members. In this work, we leverage access to the full Twitter Firehose to perform a large-scale observational study of one year of ISIS social activity. We quantify the size of ISIS presence on Twitter, the potential amount of support it received, and its collective influence over time. We find that ISIS was able to gain a relatively limited portion from the total influence mass on Twitter and that this influence diminished over time. In addition, ISIS showed a tendency towards attracting interactions from
other similar pro-ISIS accounts, while inviting only a limited anti-ISIS sentiment. We find that 75% of the interactions ISIS received on Twitter in 2015 actually came from eventually suspended accounts and that only about 8% of the interactions they received were anti-ISIS. In addition, we have created a unique dataset of 17 million ISIS-related tweets posted in 2015 which we make available for research purposes upon request.

Terrorist groups like the Islamic State of Iraq and Syria (ISIS) have exploited social media such as Twitter to spread their propaganda and to recruit new members. In this work we study the extent to which ISIS is able to spread their message beyond their immediate supporters. Are they operating in their own sphere with limited interaction with the overall community? Or are they well rooted among normal users? We find that three-quarters of the interactions ISIS received on Twitter in 2015 actually came from eventually suspended accounts raising questions about the potential number of ISIS-related accounts and how organic ISIS audience is. Towards tackling these questions, we have created a unique dataset of 17 million ISIS-related tweets posted in 2015. This dataset is available for research purposes upon request.

The advent of social media has led to an increased concern over its potential to propagate hate speech and misinformation, which, in addition to contributing to prejudice and discrimination, has been suspected of playing a role in increasing social violence and crimes in the United States. While literature has shown the existence of an association between posting hate speech and misinformation online and certain personality traits of posters, the general relationship and relevance of online hate speech/misinformation in the context of overall psychological wellbeing of posters remain elusive. One difficulty lies in the lack of adequate data analytics tools capable of adequately analyzing the massive amount of social media posts to uncover the underlying hidden links. Recent progresses in machine learning and large language models such as ChatGPT have made such an analysis possible. In this study, we collected thousands of posts from carefully selected communities on the social media site Reddit. We then utilized OpenAI’s GPT3 to derive embeddings of these posts, which are high-dimensional real-numbered vectors that presumably represent the hidden semantics of posts. We then performed various machine-learning classifications based on these embeddings in order to understand the role of hate speech/misinformation in various communities. Finally, a topological data analysis (TDA) was applied to the embeddings to obtain a visual map connecting online hate speech, misinformation, various psychiatric disorders, and general mental health.