Detecting Jihadist Messages on Twitter

Ashcroft, Michael B.; Fisher, Ali; Kaati, Lisa; Omer, Enghin; Prucha, Nico

doi:10.1109/eisic.2015.27

Cited by 77 publications

(49 citation statements)

References 9 publications

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“…Many such datasets (e.g., [2,3,28]) were collected using sets of keywords, where users whose tweets contain those words would be regarded as in the "radicalised" set. However, we continue to observe that many who use radicalisation terminology in their tweets are simply reporting current events (e.g., "Islamic State hacks Swedish radio station', or sharing harmless religious rhetoric (e.g., "If you want to talk to Allah, pray.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding detection we can highlight the works of Berger [5,6], Agarwal [2], Ashcroft [3] and Saif [29].…”

Section: Computational Approachesmentioning

confidence: 99%

“…In 2015 Ashcroft [3] aimed to automatically detect messages released by jihadist groups on Twitter. They collected tweets from 6729 Jihadist sympathisers .…”

Section: Computational Approachesmentioning

confidence: 99%

“…WebSci '18, May [27][28][29][30]2018 A well known example is the so-called Islamic State (IS), which is arguably one of the leading organisations in the use of social media for sharing their propaganda, for raising funds, and for radicalising and recruiting individuals around the globe. According to a 2015 U.S government report, 3 this organisation succeeded in recruiting more than 25,000 foreign fighters in Syria and Iraq, including 4,500 from Europe and North America. In a desperate attempt to disrupt and disconnect such radicalisation channels, some governments, organisations, and social media platforms continuously search and disable social media accounts that are found to be associated with such terrorist groups.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Roots of Radicalisation on Twitter

Fernández

Asif

Alani

2018

Proceedings of the 10th ACM Conference on Web Science

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In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. 1 Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of 'roots of radicalisation' from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 "general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation.

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Section: Discussionmentioning

confidence: 99%

“…Regarding detection we can highlight the works of Berger [5,6], Agarwal [2], Ashcroft [3] and Saif [29].…”

Section: Computational Approachesmentioning

confidence: 99%

“…In 2015 Ashcroft [3] aimed to automatically detect messages released by jihadist groups on Twitter. They collected tweets from 6729 Jihadist sympathisers .…”

Section: Computational Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding the Roots of Radicalisation on Twitter

Fernández

Asif

Alani

2018

Proceedings of the 10th ACM Conference on Web Science

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“…Their domain are Western Jihadists. In contrast, Ashcroft et al (2015) identify specific messages from Twitter. Similarly, Wei et al (2016) identify Jihadist-related conversations.…”

Section: Background and Related Workmentioning

confidence: 99%

Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups

Hartung

Klinger

Schmidtke

et al. 2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extremist motives such as mobilization, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of rightwing extremism, we frame the task as ranking each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust performance (up to 0.81 F 1 score) when being used for predicting discrete class labels. At the same time, the model provides plausible continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements.

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Identifying Extremism in Text Using Deep Learning

Johnston

Marku

2019

Development and Analysis of Deep Learning Architectures

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Detecting Jihadist Messages on Twitter

Cited by 77 publications

References 9 publications

Understanding the Roots of Radicalisation on Twitter

Understanding the Roots of Radicalisation on Twitter

Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups

Identifying Extremism in Text Using Deep Learning

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