Detecting Multipliers of Jihadism on Twitter

Kaati, Lisa; Omer, Enghin; Prucha, Nico; Shrestha, Amendra

doi:10.1109/icdmw.2015.9

Cited by 54 publications

(25 citation statements)

References 7 publications

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“…While such an approach seems effective in distinguishing radicalised users, it is unable to properly deal with lexical ambiguity (i.e., polysemy). Furthermore, in [12] the authors focused on detecting Twitter users who are involved with "Media Mujahideen", a Jihadist group who distribute propaganda content online. They used a machine learning approach using a combination of data-dependent and data-independent features.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Nouh

Nurse

Goldsmith

2019

2019 IEEE International Conference on Intelligence and Security Informatics (ISI)

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The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is threefold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.

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Section: Related Workmentioning

confidence: 99%

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Nouh

Nurse

Goldsmith

2019

2019 IEEE International Conference on Intelligence and Security Informatics (ISI)

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“…Magdy et al [66] classified twitter users whether they are supporting or opposing ISIS by discriminating the language that shows support for ISIS. Kaati et al [67] proposed a model that detects whether a user is more likely to support Jihadist groups. Their experiment showed that AdaBoost classifier worked well for English tweets but it did not give the expected performance in Arabic tweets.…”

Section: Computer Science and Information Technology (Cs And It) 91mentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

115

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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“…For instance, Kaati et al [38] in their study ''Detecting Multipliers of Jihadism on Twitter '' collected two different sets of data that involved in media Mujahideen and Jihadist propaganda. The Mujahideen consists of 835 English tweeps and 337 Arabic tweeps.…”

Section: Review Of the Performance Measure For Cyber Propaganda Dementioning

confidence: 99%

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

et al. 2020

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This systematic mapping literature aims to identify current research and directions for future studies in terms of combating cyber propaganda in the social media, which is used by both human effort and technological approaches (socio-technical) for mitigation. Out of 5176 retrieved articles, only 98 of them were selected for primary studies; classified based on research artifacts, mitigation effort, and the social media platforms involved in the research. The search was conducted using selected databases and applying selection criteria set for this research. Through the analysis, important research trends were identified based on human effort and technological approaches in mitigating and combating the cyber-propaganda issues. The authors also identified various mitigation socio-technical approaches such as identification, detection, image recognition, prediction, truth discovery and comprehension of rumours flow. The study also highlights areas for further improvements, to complement the performances of existing techniques. Besides, the study provides a brief review of cyber propaganda detection using classification techniques. Hence, it has set forth applicable research focus on the areas dealing with the mitigation of risk borne by cyber propaganda in the social media. INDEX TERMS Systematic mapping review, social media, cyber propaganda, mitigation, human effort, socio-technical approaches.

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Detecting Multipliers of Jihadism on Twitter

Cited by 54 publications

References 7 publications

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

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