Abstract
A core advantage of social media platforms is the freedom that comes with the way users express their opinions and share information as they deem fit, in line with the subject of discussion. Advances in text analytics have allowed researchers to adequately classify information expressed in natural language text, which emanates in millions per minute, under well-defined categories like “hate” or “radicalized” content which provide further insight into intent of the sender. This analysis is important for social media intelligence and information security. Commercial intent classifications have witnessed several research attentions. However, social intent classification of topics in line with hate, radicalized posts, have witnessed little research effort. The focus of this study is to develop a roadmap of a model for automatic bilingual intent classification of hate speech. This empirical model will involve the use of bi-gram words for intent classification. The feature extraction will include expected cross entropy, while topic modeling will use supervised context-based n-gram approach. Classification will be done using ensemble-based approach which will include the use of Naïve Bayes and Support Vector Machine. This study will also discuss the differences between the concept of fake news, stance and intent identification. We anticipate that the proposed roadmap, if implemented, will be useful in the classification of intent as it relates to hate speech in bilingual twitter post. The proposed model has the potential to improve intent classification and that could be useful in hate speech detection, which can avert social or security problems.
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References
Kröll M, Strohmaier M (2015) Associating intent with sentiment in weblogs. In: International conference on applications of natural language to information systems. Springer, Cham
Albright J (2016) The# Election2016 micro-propaganda machine. https://medium.com/@d1gi/the-election2016-micro-propaganda-machine-383449cc1fba#.idanl6i8z. Accessed 15 Jan 2017
Lozhnikov N, Derczynski L, Mazzara M (2018) Stance prediction for Russian: data and analysis
Shu K et al (2017) Fake news detection on social media: a data mining perspective, 19(1):22–36
Purohit H et al (2015) Intent classification of short-text on social media. In: 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity). IEEE
Kröll M, Strohmaier M (2009) Analyzing human intentions in natural language text. In: Proceedings of the fifth international conference on knowledge capture. ACM
Mohtarami M et al (2018) Automatic stance detection using end-to-end memory networks
Zubiaga A et al (2018) Detection and resolution of rumours in social media: a survey, 51(2):32
Lozhnikov N, Derczynski L, Mazzara M (2018) Stance prediction for russian: data and analysis. arXiv preprint arXiv:1809.01574
Dai HK et al (2006) Detecting online commercial intention (OCI). In: Proceedings of the 15th international conference on world wide web. ACM
Kirsh D (1990) When is information explicitly represented? Information, language and cognition - the Vancouver studies in cognitive science. UBC Press, pp 340–365
Ajzen I (1991) The theory of planned behavior, 50(2):179–211
Malle BF, Knobe J (1997) The folk concept of intentionality, 33(2):101–121
Sloman SA et al (2012) A causal model of intentionality judgment, 27(2):154–180
Melnikov A et al (2018) Towards dynamic interaction-based reputation models. In: 2018 IEEE 32nd international conference on advanced information networking and applications (AINA). IEEE
Hollerit B, Kröll M, Strohmaier M (2013) Towards linking buyers and sellers: detecting commercial intent on Twitter. In: Proceedings of the 22nd international conference on world wide web. ACM
Benczúr A et al (2007) Web spam detection via commercial intent analysis. In: Proceedings of the 3rd international workshop on adversarial information retrieval on the web. ACM, pp 89–92
Lewandowski D, Drechsler J, Von Mach S (2012) Deriving query intents from web search engine queries. J Am Soc Inform Sci Technol 63(9):1773–1788
Guo Q, Agichtein E, Clarke CL, Ashkan A (2008) Understanding “abandoned” ads: towards personalized commercial intent inference via mouse movement analysis. Inf Retr Advert IRA 2008:27–30
Lewandowski D (2011) The influence of commercial intent of search results on their perceived relevance. In: Proceedings of the 2011 iConference. ACM, pp 452–458
Ben-David A, Matamoros-Fernandez A (2016) Hate speech and covert discrimination on social media: monitoring the Facebook pages of extreme-right political parties in Spain. Int J Commun 10:1167–1193
Wang X, McCallum A, Wei X: Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: ICDM. IEEE, pp 697–702
Chavhan RN (2016) Solutions to detect and analyze online radicalization, 1(4)
Agarwal S, Sureka A (2017) Characterizing linguistic attributes for automatic classification of intent based racist/radicalized posts on Tumblr micro-blogging website
Montejo-Ráez A et al (2014) A knowledge-based approach for polarity classification in Twitter, 65(2):414–425
Balahur A, Perea-Ortega JM (2015) Sentiment analysis system adaptation for multilingual processing: the case of tweets, 51(4):547–556
Montoyo A, MartíNez-Barco P, Balahur A (2012) Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Elsevier (2012)
Vilares D et al (2017) Supervised sentiment analysis in multilingual environments, 53(3):595–607
Gomes HM et al (2017) A survey on ensemble learning for data stream classification, 50(2):23
Sanfilippo A et al (2009) VIM: a platform for violent intent modeling. In: Social computing and behavioral modeling. Springer, Heidelberg, pp 1–11
Ben-David A, Matamoros-Fernandez A (2016) Hate speech and covert discrimination on social media: monitoring the Facebook pages of extreme-right political parties in Spain, 10:1167–1193
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Adekotujo, A.S., Lee, J., Enikuomehin, A.O., Mazzara, M., Aribisala, S.B. (2020). Bi-lingual Intent Classification of Twitter Posts: A Roadmap. In: Ciancarini, P., Mazzara, M., Messina, A., Sillitti, A., Succi, G. (eds) Proceedings of 6th International Conference in Software Engineering for Defence Applications. SEDA 2018. Advances in Intelligent Systems and Computing, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-14687-0_1
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