Next Article in Journal
Corporate Governance of Artificial Intelligence in the Public Interest
Next Article in Special Issue
Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review
Previous Article in Journal
Environment Monitoring System of Dairy Cattle Farming Based on Multi Parameter Fusion
Previous Article in Special Issue
An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions
Article

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

Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Josiane Mothe and Willy Susilo
Information 2021, 12(7), 274; https://doi.org/10.3390/info12070274
Received: 31 May 2021 / Revised: 24 June 2021 / Accepted: 30 June 2021 / Published: 2 July 2021
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
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. View Full-Text
Keywords: change point detection; terrorism; hate speech; online content; topic detection change point detection; terrorism; hate speech; online content; topic detection
Show Figures

Figure 1

MDPI and ACS Style

Theodosiadou, O.; Pantelidou, K.; Bastas, N.; Chatzakou, D.; Tsikrika, T.; Vrochidis, S.; Kompatsiaris, I. Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators. Information 2021, 12, 274. https://doi.org/10.3390/info12070274

AMA Style

Theodosiadou O, Pantelidou K, Bastas N, Chatzakou D, Tsikrika T, Vrochidis S, Kompatsiaris I. Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators. Information. 2021; 12(7):274. https://doi.org/10.3390/info12070274

Chicago/Turabian Style

Theodosiadou, Ourania, Kyriaki Pantelidou, Nikolaos Bastas, Despoina Chatzakou, Theodora Tsikrika, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2021. "Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators" Information 12, no. 7: 274. https://doi.org/10.3390/info12070274

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop