Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach
Smart cities are shifting the presence of people from physical world to cyber world (cyberspace). Along with the facilities for societies, the troubles of physical world, such as bullying, aggression and hate speech, are also taking their presence emphatically in cyberspace. This paper aims to dig the posts of social media to identify the bullying comments containing text as well as image. In this paper, we have proposed a unified representation of text and image together to eliminate the need for separate learning modules for image and text. A single-layer Convolutional Neural Network model is used with a unified representation. The major findings of this research are that the text represented as image is a better model to encode the information. We also found that single-layer Convolutional Neural Network is giving better results with two-dimensional representation. In the current scenario, we have used three layers of text and three layers of a colour image to represent the input that gives a recall of 74% of the bullying class with one layer of Convolutional Neural Network.
KeywordsOnline social network Cyberbullying TF–IDF Deep learning Convolutional Neural Network
The first author would like to acknowledge the Ministry of Electronics and Information Technology (MeitY), Government of India, for the financial support provided to her during the research work through Visvesvaraya Ph.D. Scheme for Electronics and IT.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Badjatiya P, Gupta S, Gupta M, Varma V (2017) Deep learning for hate speech detection in tweets. In: Proceedings of the 26th international conference on world wide web companion. International world wide web conferences steering committee, pp 759–760Google Scholar
- Bohra A, Vijay D, Singh V, Akhtar SS, Shrivastava M (2018) A dataset of Hindi-English code-mixed social media text for hate speech detection. In: Proceedings of the second workshop on computational modeling of people’s opinions, personality, and emotions in social media, pp 36–41Google Scholar
- Chatzakou D, Kourtellis N, Blackburn J, De Cristofaro E, Stringhini G, Vakali A (2017) Mean birds: detecting aggression and bullying on Twitter. In: Proceedings of the 2017 ACM on web science conference. ACM, pp 13–22Google Scholar
- Chen Y, Zhou Y, Zhu S, Xu H (2012) Detecting offensive language in social media to protect adolescent online safety. In: Privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international conference on social computing (SocialCom). IEEE, pp 71–80Google Scholar
- Chen H, Mckeever S, Delany SJ (2017) Harnessing the power of text mining for the detection of abusive content in social media. In: Advances in computational intelligence systems, Springer, New York. pp 187–205Google Scholar
- Dadvar M, De Jong F (2012) Cyberbullying detection: a step toward a safer internet yard. In: Proceedings of the 21st international conference on world wide web. ACM, pp 121–126Google Scholar
- Davidson T, Warmsley D, Macy M, Weber I (2017) Automated hate speech detection and the problem of offensive language. arXiv preprint arXiv:1703.04009
- Dinakar K, Jones B, Havasi C, Lieberman H, Picard R (2012) Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans Interact Intell Syst (TiiS) 2(3):18Google Scholar
- Hosseinmardi H, Mattson SA, Rafiq RI, Han R, Lv Q, Mishr S (2015) Prediction of cyberbullying incidents on the Instagram social network. arXiv preprint arXiv:1508.06257
- Hosseinmardi H, Rafiq RI, Han R, Lv Q, Mishra S (2016) Prediction of cyberbullying incidents in a media-based social network. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 186–192. IEEEGoogle Scholar
- Kornblum J (2008) Cyberbullying grows bigger and meaner with photos, video. USA Today, dated July 17, 2008, Retrieved from https://cybercrimes.wordpress.com/2008/07/17/cyberbullying-growsbigger-and-meaner-with-photos-video/
- Kumari K, Singh JP, Dwivedi YK, Rana NP (2019) Aggressive social media post detection system containing symbolic images. In: Conference on e-Business, e-Services and e-Society. Springer, New York, pp 415–424Google Scholar
- League AD (2011) Glossary of cyberbullying terms. adl.org, Retrieved from https://www.sd35.bc.ca/wp-content/uploads/sites/2/2015/05/glossary_of_cyberbullying_terms.pdf. Accessed 23 Nov 2017
- Nahar V, Li X, Pang C (2013) An effective approach for cyberbullying detection. Commun Inf Sci Manag Eng 3(5):238Google Scholar
- Paavola J, Helo T, Jalonen H, Sartonen M, Huhtinen A (2016) Understanding the trolling phenomenon: the automated detection of bots and cyborgs in the social media. J Inf Warf 15(4):100–111Google Scholar
- Pater JA, Miller AD, Mynatt ED (2015) This digital life: a neighborhood-based study of adolescents’ lives online. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM, pp 2305–2314Google Scholar
- Reynolds K, Kontostathis A, Edwards L (2011) Using machine learning to detect cyberbullying. In: 2011 10th international conference on machine learning and applications and workshops (ICMLA), vol. 2, pp 241–244. IEEEGoogle Scholar
- Singh VK, Ghosh S, Jose C (2017) Toward multimodal cyberbullying detection. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems. ACM, pp 2090–2099Google Scholar
- Tommasel A, Rodriguez JM, Godoy D (2018) Textual aggression detection through deep learning. In: Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018), pp 177–187Google Scholar
- Waseem Z, Hovy D (2016) Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proceedings of the NAACL student research workshop, pp 88–93Google Scholar
- Yin D, Xue Z, Hong L, Davison BD, Kontostathis A, Edwards L (2009) Detection of harassment on web 2.0. Proc Content Anal WEB 2:1–7Google Scholar
- Zhao R, Mao K (2016) Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans Affect Comput 99:1–1Google Scholar