Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Ourania Theodosiadou,Despoina Chatzakou,Theodora Tsikrika,Stefanos Vrochidis,Kyriaki Pantelidou,Ioannis Kompatsiaris,Nikolaos Bastas

doi:10.3390/info12070274

Ourania Theodosiadou, Despoina Chatzakou + Show 5 more

Open Access

https://doi.org/10.3390/info12070274

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Abstract

Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.

Highlights

In recent years, considerable terrorism-related activity, including propaganda dissemination, recruitment and training, finance raising, and hate spreading towards specific social groups, has been observed in various online platforms [1]
The output is exploited by the change point detection algorithm to detect previously unknown change points in the related time series that probably signify the occurrence of events of interest
Two time series are constructed and used as input to the change point detection (CPD) algorithm: (a) the time series of posts classified as terrorism related; and (b) those identified as containing hate speech

Summary

Introduction

Considerable terrorism-related activity, including propaganda dissemination, recruitment and training, finance raising, and hate spreading towards specific social groups, has been observed in various online platforms [1]. The first step in this process is the detection of content of interest, and, far, several works have focused on developing effective classification frameworks suitable for distinguishing between terrorism vs non-terrorism [3] or extremism vs non-extremism content [2], among others These methods are more oriented towards detecting suspicious content, but without focusing on the significant changes that take place over time. Such an assessment can be performed using change point detection (CPD) methods applied on suitably constructed time series which can serve as indicators of terrorism or crime activity. The idea of using a CPD method in time series of terrorismor hate speech-related posts can be seen as an alternative way to identify links between online activity and terrorism

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Conclusion