Online Library

This systematic review explores the utilization of crowdsourcing for geoinformation in enhancing awareness and mitigating terrorism-related disasters. Out of 519 studies identified in the database search, 108 were deemed eligible for analysis. We focused on articles employing various forms of crowdsourcing platforms, such as Twitter (now known as X), Facebook, and Telegram, across three distinct phases of terrorism-related disasters: monitoring and detection, onset, and post-incident analysis. Notably, we placed particular emphasis on the integration of Machine Learning (ML) algorithms in studying crowdsourced terrorism geoinformation to assess the current state of research and propose future directions. The findings revealed that Twitter emerged as the predominant crowdsourcing platform for terrorism-related information. Despite the prevalence of natural language processing for data mining, the majority of studies did not incorporate ML algorithms in their analyses. This preference for qualitative research methods can be attributed to the multifaceted nature of terrorism, spanning security, governance, politics, religion, and law. Our advocacy is for increased studies from the domains of geography, earth observation, and big data. Simultaneously, we encourage advancements in existing ML algorithms to enhance the accurate real-time detection of planned and onset terrorism disasters.

The Islamic State of Iraq and Syria (ISIS) is a radicalized group that has the goal of building an Islamic caliphate globally. ISIS recruits its members globally and spreads the extremism narrative widely. ISIS recruits more men to fulfill their roles as fighters and martyrs, but the role of women is also needed in ISIS activities as a supporting capacity in every activity carried out by jihadists. The involvement of women is seen as quite effective in polarizing radicalism, because women will continue the descendants of the next generation of ISIS and have a higher humanist and cognitive spirit than men. For this reason, it is interesting to study the radicalization process carried out by ISIS on women, propaganda, and the pattern of approach. This research uses a qualitative approach to gain an understanding of how transnational networks of the ISIS movement influence women and encourage women to join acts of terrorism through propaganda and extremism narratives. It was found that ISIS spreads its ideological narrative through offline and online channels where the basis of its approach is more towards an emotional intelligence approach related to social identity. ISIS uses many social media instruments in the process of spreading its ideology, so a strategy that focuses on a multidimensional and gender-valued approach that involves the entire spectrum of society needed, including governments, civil society, and families, as well as interstate cooperation to prevent polarization of ISIS ideology to women, such as internalization of education and gender equality.

The article explores Bitchute, a video-hosting platform associated with the Far/Alt Right, with the aim of understanding how it reconfigures political communication and the digital public sphere. Methodologically, the article employs the walkthrough method and non-participant observation to identify the main features and functionalities offered to users. These include a set of values that prioritise creators, an algorithmic organisation that keeps users engaged with a single creator channel rather than with the same topic across channels; and embedded buttons for tips and pledges for creators enabling them to directly monetise their content. The content posted on Bitchute tends to coalesce around politicised cultural issues. It is noteworthy that although Bitchute hosts some advertising, it does not use data for microtargeting and in general makes limited use of user data. We interpret these findings as suggesting that Bitchute constitutes a media infrastructure that encourages, incentivises and sustains microcelebrities of the Far/Alt Right, who act as ideology entrepreneurs. Bitchute can therefore be seen as an infrastructure for the multiplication/sustenance of ideological entrepreneurs/political influencers who vie for the attention and money of far-right publics. If we can speak of a structural transformation of the public sphere associated with Alt Tech, our discussion of Bitchute suggests that this takes the form of a political media infrastructure that enables the continued existence and consolidation of a new type of political actor, the ideology entrepreneur.

This document sets up the main recommendations identified through the three years of engagement and dialouge on this topic. It is anchored in the discussions from the round tables. It further builds on the experiences from UN field presences that are working to address these challenges at country level. The paper is the result of a cooperation between my Office and the ESRC Human Rights, Big Data and Technology Project at the University of Essex. I would like to especially thank Ahmed Shaheed, Deputy Director, Essex Human Rights, Big Data and Technology Project and former United Nations Special Rapporteur on Freedom of Religion or Belief for this partnership over the last several years and which has resulted in this policy paper.

Measuring extremist behavior and discourse online presents a significant methodological challenge largely due to the volume of content. This paper assesses the extent to which discourse communities created by extremists identified through qualitative analysis in prior research are present in a novel Telegram dataset and can be identified through Latent Dirichlet Allocation topic modeling and network analysis. It then compares the results of Louvian and Girvan-Newman network community detection methods and the topics assigned to each community to see if the underlying structure of association between topics is robust to the use of different community detection methods. The results indicate that it may be possible to map discourse communities through topic modeling and network analysis. However, the comparison of the algorithms is inconclusive. This work contributes to our understanding of how computational social science methods can be used to measure and analyze extremist use of the Internet at scale.