Statistical Analysis of Risk Assessment Factors and Metrics to Evaluate Radicalisation in Twitter

TL;DR

This study evaluates various indicators and metrics to assess the risk of radicalisation in Twitter users, highlighting effective keyword-based metrics and their performance across different datasets.

Contribution

It provides a detailed analysis of indicators and metrics for radicalisation risk assessment on social networks, with experimental validation on multiple datasets.

Findings

Keyword-based metrics effectively measure perceptions related to radicalisation.

Habit-based metrics like writing ellipses are less effective.

Metrics based on language and sentiment perform well in risk assessment.

Abstract

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.