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Erschienen in: Cognitive Computation 1/2022

16.02.2021

An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing

verfasst von: Oscar Araque, Carlos A. Iglesias

Erschienen in: Cognitive Computation | Ausgabe 1/2022

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Abstract

The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.

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Literatur
8.
Zurück zum Zitat Cambria E, Fu J, Bisio F, Poria S. AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 2015. pp. 508–14. Cambria E, Fu J, Bisio F, Poria S. AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 2015. pp. 508–14.
9.
15.
Zurück zum Zitat Chen M, Wang S, Liang PP, Baltrušaitis T, Zadeh A, Morency LP. Multimodal sentiment analysis with word-level fusion and reinforcement learning. In Proceedings of the 19th ACM International Conference on Multimodal Interaction. 2017. pp. 163–71. https://doi.org/10.1145/3136755.3136801. Chen M, Wang S, Liang PP, Baltrušaitis T, Zadeh A, Morency LP. Multimodal sentiment analysis with word-level fusion and reinforcement learning. In Proceedings of the 19th ACM International Conference on Multimodal Interaction. 2017. pp. 163–71. https://​doi.​org/​10.​1145/​3136755.​3136801.
27.
Zurück zum Zitat Akhtar MS, Ekbal A, Cambria E. How intense are you? predicting intensities of emotions and sentiments using stacked ensemble. IEEE Comput Intell Mag. 2020;15(1):64–75.CrossRef Akhtar MS, Ekbal A, Cambria E. How intense are you? predicting intensities of emotions and sentiments using stacked ensemble. IEEE Comput Intell Mag. 2020;15(1):64–75.CrossRef
29.
Zurück zum Zitat Sarkar K. A stacked ensemble approach to bengali sentiment analysis. In: Tiwary US, Chaudhury S, editors. Intelligent Human Computer Interaction., ppCham: Springer International Publishing; 2020. p. 102–111.CrossRef Sarkar K. A stacked ensemble approach to bengali sentiment analysis. In: Tiwary US, Chaudhury S, editors. Intelligent Human Computer Interaction., ppCham: Springer International Publishing; 2020. p. 102–111.CrossRef
31.
Zurück zum Zitat Bandhakavi A, Wiratunga N, Massie S, Padmanabhan D. Lexicon generation for emotion detection from text. IEEE Intell Syst. 2017;32(1):102–8.CrossRef Bandhakavi A, Wiratunga N, Massie S, Padmanabhan D. Lexicon generation for emotion detection from text. IEEE Intell Syst. 2017;32(1):102–8.CrossRef
33.
Zurück zum Zitat Correa D, Sureka A. Solutions to detect and analyze online radicalization: a survey. arXiv preprint 2013. arXiv:1301.4916. Correa D, Sureka A. Solutions to detect and analyze online radicalization: a survey. arXiv preprint 2013. arXiv:1301.4916.
36.
Zurück zum Zitat Rowe M, Saif H. Mining pro-isis radicalisation signals from social media users. In Proceedings of the tenth international AAAI conference on web and social media (ICWSM 2016). pp. 329–38. Rowe M, Saif H. Mining pro-isis radicalisation signals from social media users. In Proceedings of the tenth international AAAI conference on web and social media (ICWSM 2016). pp. 329–38.
38.
Zurück zum Zitat Agarwal S, Sureka A. Applying social media intelligence for predicting and identifying on-line radicalization and civil unrest oriented threats. arXiv preprint 2015. arXiv:1511.06858. Agarwal S, Sureka A. Applying social media intelligence for predicting and identifying on-line radicalization and civil unrest oriented threats. arXiv preprint 2015. arXiv:​1511.​06858.
39.
Zurück zum Zitat López-Sáncez D, Revuelta J, de la Prieta F, Corchado JM. Towards the automatic identification and monitoring of radicalization activities in twitter. In International Conference on Knowledge Management in Organizations. Springer, 2018. pp. 589–99. https://doi.org/10.1007/978-3-319-95204-8\_49. López-Sáncez D, Revuelta J, de la Prieta F, Corchado JM. Towards the automatic identification and monitoring of radicalization activities in twitter. In International Conference on Knowledge Management in Organizations. Springer, 2018. pp. 589–99. https://​doi.​org/​10.​1007/​978-3-319-95204-8\_​49.
41.
Zurück zum Zitat Chalothorn T, Ellman J. Affect analysis of radical contents on web forums using sentiwordnet. International Journal of Innovation Management and Technology. 2013;4(1):122–4. Chalothorn T, Ellman J. Affect analysis of radical contents on web forums using sentiwordnet. International Journal of Innovation Management and Technology. 2013;4(1):122–4.
42.
Zurück zum Zitat Pennebaker JW, Francis ME, Booth RJ. Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001. Pennebaker JW, Francis ME, Booth RJ. Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001.
43.
Zurück zum Zitat Vergani M, Bliuc A-M. The evolution of the ISIS language: a quantitative analysis of the language of the first year of Dabiq magazine. Sicurezza, Terrorismo e Società Security, Terrorism and Society. 2015;2(2):7–20. Vergani M, Bliuc A-M. The evolution of the ISIS language: a quantitative analysis of the language of the first year of Dabiq magazine. Sicurezza, Terrorismo e Società Security, Terrorism and Society. 2015;2(2):7–20.
44.
47.
Zurück zum Zitat Dewan P, Suri A, Bharadhwaj V, Mithal A, Kumaraguru P. Towards understanding crisis events on online social networks through pictures. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2017. pp. 439–46. https://doi.org/10.1145/3110025.3110062. Dewan P, Suri A, Bharadhwaj V, Mithal A, Kumaraguru P. Towards understanding crisis events on online social networks through pictures. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2017. pp. 439–46. https://​doi.​org/​10.​1145/​3110025.​3110062.
48.
Zurück zum Zitat Bermingham A, Conway M, McInerney L, O’Hare N, Smeaton AF. Combining social network analysis and sentiment analysis to explore the potential for online radicalisation. In Social Network Analysis and Mining, 2009. ASONAM’09. International Conference on Advances in. IEEE, 2009. pp. 231–6. https://doi.org/10.1109/ASONAM.2009.31. Bermingham A, Conway M, McInerney L, O’Hare N, Smeaton AF. Combining social network analysis and sentiment analysis to explore the potential for online radicalisation. In Social Network Analysis and Mining, 2009. ASONAM’09. International Conference on Advances in. IEEE, 2009. pp. 231–6. https://​doi.​org/​10.​1109/​ASONAM.​2009.​31.
52.
Zurück zum Zitat Dadvar M, Jong FD, Ordelman R, Trieschnigg D. Improved cyberbullying detection using gender information. In Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012). University of Ghent, 2012. pp. 23–5. Dadvar M, Jong FD, Ordelman R, Trieschnigg D. Improved cyberbullying detection using gender information. In Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012). University of Ghent, 2012. pp. 23–5.
55.
Zurück zum Zitat Nandhini BS, Sheeba J. Cyberbullying detection and classification using information retrieval algorithm. In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015). pp. 1–5. https://doi.org/10.1145/2743065.2743085. Nandhini BS, Sheeba J. Cyberbullying detection and classification using information retrieval algorithm. In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015). pp. 1–5. https://​doi.​org/​10.​1145/​2743065.​2743085.
57.
58.
Zurück zum Zitat Kwok I, Wang Y. Locate the hate: Detecting tweets against blacks. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press, 2013. p. 1621–2. Kwok I, Wang Y. Locate the hate: Detecting tweets against blacks. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press, 2013. p. 1621–2.
60.
Zurück zum Zitat Davidson T, Warmsley D, Macy M, Weber I. Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International AAAI Conference on Web and Social Media, ICWSM. 2017. pp. 512–5. Davidson T, Warmsley D, Macy M, Weber I. Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International AAAI Conference on Web and Social Media, ICWSM. 2017. pp. 512–5.
64.
Zurück zum Zitat Warner W, Hirschberg J. Detecting hate speech on the world wide web. In Proceedings of the second workshop on language in social media. Association for Computational Linguistics, 2012. pp. 19–26. Warner W, Hirschberg J. Detecting hate speech on the world wide web. In Proceedings of the second workshop on language in social media. Association for Computational Linguistics, 2012. pp. 19–26.
65.
Zurück zum Zitat Agarwal S, Sureka A. Characterizing linguistic attributes for automatic classification of intent based racist/radicalized posts on tumblr micro-blogging website. arXiv preprint 2017. arXiv:1701.04931. Agarwal S, Sureka A. Characterizing linguistic attributes for automatic classification of intent based racist/radicalized posts on tumblr micro-blogging website. arXiv preprint 2017. arXiv:​1701.​04931.
66.
Zurück zum Zitat Hutto CJ, Gilbert E. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International AAAI Conference on Weblogs and Social Media, 2014. Hutto CJ, Gilbert E. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International AAAI Conference on Weblogs and Social Media, 2014.
67.
Zurück zum Zitat Del Vigna F, Cimino A, Dell’Orletta F, Petrocchi M, Tesconi M. Hate me, hate me not: Hate speech detection on facebook. In Proceedings of the First Italian Conference on Cybersecurity (ITASEC17). 2017 pp. 86–95. Del Vigna F, Cimino A, Dell’Orletta F, Petrocchi M, Tesconi M. Hate me, hate me not: Hate speech detection on facebook. In Proceedings of the First Italian Conference on Cybersecurity (ITASEC17). 2017 pp. 86–95.
71.
Zurück zum Zitat Le Q, Mikolov T. Distributed representations of sentences and documents. In International Conference on Machine Learning. 2014. pp. 1188–96. Le Q, Mikolov T. Distributed representations of sentences and documents. In International Conference on Machine Learning. 2014. pp. 1188–96.
79.
Zurück zum Zitat Baeza-Yates R, Ribeiro-Neto B et al. Modern information retrieval, volume 463. ACM press New York, 1999. Baeza-Yates R, Ribeiro-Neto B et al. Modern information retrieval, volume 463. ACM press New York, 1999.
80.
Zurück zum Zitat Gambhir HK. Dabiq: The strategic messaging of the islamic state. Institute for the Study of War, 15, 2014. Gambhir HK. Dabiq: The strategic messaging of the islamic state. Institute for the Study of War, 15, 2014.
83.
Zurück zum Zitat Basile V, Bosco C, Fersini E, Nozza D, Patti V, Pardo FMR, Rosso P, Sanguinetti M. Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation. 2019. pp. 54–63. https://doi.org/10.18653/v1/S19-2007. Basile V, Bosco C, Fersini E, Nozza D, Patti V, Pardo FMR, Rosso P, Sanguinetti M. Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation. 2019. pp. 54–63. https://​doi.​org/​10.​18653/​v1/​S19-2007.
85.
Zurück zum Zitat Demšar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan):1–30, 2006. Demšar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan):1–30, 2006.
Metadaten
Titel
An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing
verfasst von
Oscar Araque
Carlos A. Iglesias
Publikationsdatum
16.02.2021
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 1/2022
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-021-09845-6

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