Temporal Behavioural Analysis of Extremists on Social Media: A Machine Learning Based Approach

Lutfi, Saad; Yasin, Rand; Barachi, May El; Oroumchian, Farhad; Ajala, Imène; Mathew, Sujith Samuel

doi:10.23919/splitech52315.2021.9566446

Cited by 1 publication

(2 citation statements)

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“…Twitter was chosen as the platform of choice due to its popularity for the expression of political views and opinions. Figure 1 depicts the key elements of our system, which is an extension of the architecture we proposed in [17]. The process begins by extracting the Twitter dataset, then pre-processing it.…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…This is particularly true for the United States, in which two-thirds of terrorist attacks were perpetrated by right-wing extremists in 2019, and over 90% of attacks between January and May 2020 [13]. Building on the approach we proposed in [17] in which sentiment and links analysis was used, our proposed framework aims at examining the behavioral and social dynamics of extremists on social media, by leveraging advanced natural language processing techniques, including 1) data augmentation and clustering; 2) social circle analysis; 3) content analysis, and 4) temporal behavior analysis. The proposed framework can serve as a decision support tool for government officials and intelligence agencies seeking to monitor and analyze extreme views expressed online.…”

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Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Barachi

Mathew

Oroumchian

et al. 2022

JCOMSS

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Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process's complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users' interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making.Index Terms-smart cities, social media analytics, extreme views, temporal behavior, natural language processing. I. INTRODUCTIONFuture smart cities aim at achieving sustainability and quality of life using a combination of smart technologies [1]. To attain such a goal, smart cities rely on a data-centric approach in which assets, people, and events are constantly monitored, and

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Section: Proposed Frameworkmentioning

confidence: 99%

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confidence: 99%