{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:19:47Z","timestamp":1771075187391,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["PROPHETS [grant agreement No. 786894]"],"award-info":[{"award-number":["PROPHETS [grant agreement No. 786894]"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["CONNEXIONs [grant agreement No 786731]"],"award-info":[{"award-number":["CONNEXIONs [grant agreement No 786731]"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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\u2014either in a univariate or two-dimensional case\u2014can 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.<\/jats:p>","DOI":"10.3390\/info12070274","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:34:36Z","timestamp":1625438076000},"page":"274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators"],"prefix":"10.3390","volume":"12","author":[{"given":"Ourania","family":"Theodosiadou","sequence":"first","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyriaki","family":"Pantelidou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolaos","family":"Bastas","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Despoina","family":"Chatzakou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theodora","family":"Tsikrika","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-9020","authenticated-orcid":false,"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"ref_1","first-page":"208","article-title":"Terrorism and social media: Global evidence","volume":"22","author":"Asongu","year":"2019","journal-title":"J. Glob. Inf. Technol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13673-019-0185-6","article-title":"Detection and classification of social media-based extremist affiliations using sentiment analysis techniques","volume":"9","author":"Ahmad","year":"2019","journal-title":"Hum. Cent. Comput. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Abrar, M.F., Arefin, M.S., and Hossain, M.S. (2019, January 7\u20139). A Framework for Analyzing Real-Time Tweets to Detect Terrorist Activities. Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ECACE.2019.8679430"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1467-9892.2012.00819.x","article-title":"Structural breaks in time series","volume":"34","author":"Aue","year":"2013","journal-title":"J. Time Ser. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107299","DOI":"10.1016\/j.sigpro.2019.107299","article-title":"Selective review of offline change point detection methods","volume":"167","author":"Truong","year":"2020","journal-title":"Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neunet.2013.01.012","article-title":"Change-point detection in time-series data by relative density-ratio estimation","volume":"43","author":"Liu","year":"2013","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Goutte, C. (2017, January 4). Detecting changes in twitter streams using temporal clusters of hashtags. Proceedings of the Events and Stories in the News Workshop, Vancouver, BC, Canada.","DOI":"10.18653\/v1\/W17-2702"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tasoulis, S.K., Vrahatis, A.G., Georgakopoulos, S.V., and Plagianakos, V.P. (2018, January 3\u20135). Real Time Sentiment Change Detection of Twitter Data Streams. Proceedings of the 2018 Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, Greece.","DOI":"10.1109\/INISTA.2018.8466326"},{"key":"ref_9","unstructured":"Goutte, C., Wang, Y., Liao, F., Zanussi, Z., Larkin, S., and Grinberg, Y. (2018, January 7\u201312). Eurogames16: Evaluating change detection in online conversation. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1080\/19434472.2012.725225","article-title":"Detecting significant changes in dark networks","volume":"5","author":"Everton","year":"2013","journal-title":"Behav. Sci. Terror. Political Aggress."},{"key":"ref_11","unstructured":"Tickle, S., Eckley, I., and Fearnhead, P. (2020). A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. arXiv, Available online: https:\/\/arxiv.org\/abs\/2011.03599."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Porter, M.D., White, G., and Mazerolle, L. (2012). Innovative methods for terrorism and counterterrorism data. Evidence-Based Counterterrorism Policy, Springer.","DOI":"10.1007\/978-1-4614-0953-3_5"},{"key":"ref_13","unstructured":"Nizzoli, L., Avvenuti, M., Cresci, S., and Tesconi, M. (July, January 30). Extremist propaganda tweet classification with deep learning in realistic scenarios. Proceedings of the 10th ACM Conference on Web Science, Boston, MA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Badjatiya, P., Gupta, S., Gupta, M., and Varma, V. (2017, January 3\u20137). Deep Learning for Hate Speech Detection in Tweets. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3054223"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"de Gibert, O., Perez, N., Garc\u00eda-Pablos, A., and Cuadros, M. (2018, January 31). Hate Speech Dataset from a White Supremacy Forum. Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), Brussels, Belgium.","DOI":"10.18653\/v1\/W18-5102"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, T., Dong, J., Li, H., and Gao, Y. (2017, January 10\u201312). Simple convolutional neural network on image classification. Proceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China.","DOI":"10.1109\/ICBDA.2017.8078730"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gamb\u00e4ck, B., and Sikdar, U.K. (2017, January 4). Using convolutional neural networks to classify hate-speech. Proceedings of the First Workshop on Abusive Language Online, Vancouver, BC, Canada.","DOI":"10.18653\/v1\/W17-3013"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., and Bhattacharya, S. (2017, January 4\u20139). Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"ref_19","unstructured":"Gehring, J., Auli, M., Grangier, D., and Dauphin, Y. (August, January 30). A Convolutional Encoder Model for Neural Machine Translation. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, BC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tamchyna, A., and Veselovsk\u00e1, K. (2016, January 16\u201317). UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1059"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., and Zhao, J. (2015, January 25\u201330). Recurrent Convolutional Neural Networks for Text Classification. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C. (2014, January 25\u201329). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_24","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv, Available online: https:\/\/arxiv.org\/abs\/1301.3781."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Davidson, T., Warmsley, D., Macy, M., and Weber, I. (2017, January 15\u201318). Automated hate speech detection and the problem of offensive language. Proceedings of the International AAAI Conference on Web and Social Media, Montr\u00e9al, QC, Canada.","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, X., Jiang, J., Zhao, D., Feng, Y., and Hong, Y. (2018). A Convolutional Attention Model for Text Classification. Natural Language Processing and Chinese Computing, Springer International Publishing.","DOI":"10.1007\/978-3-319-73618-1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Song, P., Geng, C., and Li, Z. (2019, January 27\u201329). Research on Text Classification Based on Convolutional Neural Network. Proceedings of the 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi\u2019an, China.","DOI":"10.1109\/ICCNEA.2019.00052"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/01621459.2013.849605","article-title":"A nonparametric approach for multiple change point analysis of multivariate data","volume":"109","author":"Matteson","year":"2014","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13388-014-0005-5","article-title":"Automatic detection of cyber-recruitment by violent extremists","volume":"3","author":"Scanlon","year":"2014","journal-title":"Secur. Inform."},{"key":"ref_30","unstructured":"Burke, R.A. (2017). Counter-Terrorism for Emergency Responders, CRC Press."},{"key":"ref_31","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/7\/274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:25:29Z","timestamp":1760163929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/7\/274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,2]]},"references-count":31,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["info12070274"],"URL":"https:\/\/doi.org\/10.3390\/info12070274","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,2]]}}}