Statistical Analysis of Risk Assessment Factors and Metrics to Evaluate Radicalisation in Twitter
This work addresses the challenge of detecting radicalization in social media for security and monitoring purposes, but it is incremental as it builds on existing data mining methods without introducing major innovations.
The paper tackled the problem of assessing radicalization risk on Twitter by evaluating various indicators and metrics across three datasets, finding that keyword-based metrics performed well for measuring frustration and ideological declarations, while metrics based on writing habits like ellipses were insufficient.
Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radicalisation of a precise individual on three different datasets. Keyword-based metrics, even though depending on the written language, performs well when measuring frustration, perception of discrimination as well as declaration of negative and positive ideas about Western society and Jihadism, respectively. However, metrics based on frequent habits such as writing ellipses are not well enough to characterise a user in risk of radicalisation. The paper presents a detailed description of both, the set of indicators used to asses the radicalisation in Social Networks and the set of datasets used to evaluate them. Finally, an experimental study over these datasets are carried out to evaluate the performance of the metrics considered.