Abstract
Online social media (OSM) plays a major role in today’s life by providing people with a platform to share individual contents such as photos, ideas, opinions, and videos. However, at the same time, there are many negative carry-overs on social media users, which lead to uncontrolled and offensive communication called online hate. It has become a major concern for all online social media (OSM) users as it creates nothing but a negative influence on the users. Hence, developing a better generalized online hate classifier and network analyzer across domains is the challenge. This article highlights a novel 8-stage methodology that is proposed to develop an efficient generalized online hate classifier and network analyzer both which can help in overcoming the challenges. As a preliminary step toward 8-stage process proposed, the dataset related to women is collected from Twitter and analyzed to find out the frequent words used, number of retweets increased are negative tweets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zeng, D., Chen, H., Lusch, R., Li, S.: Social media analytics and intelligence. IEEE Intell. Syst. 25(6), 13–16 (2010)
Vidgen, B., Harris, A., Nguyen, D., Tromble, R., Hale, S., Margetts, H.: Challenges and frontiers in abusive content detection. In: Proceedings of the Third Workshop on Abusive Language Online (2019). https://doi.org/10.18653/v1/W19-3509
Anand, M., Eswari, R.: Classification of abusive comments in social media using deep learning. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 974–977 (2019)
Cécillon, N., Labatut, V., Dufour, R., Linarès, G.: Abusive language detection in online conversations by combining content- and graph-based features. Front. Big Data 2, 8 (2019). https://doi.org/10.3389/fdata.2019.00008
Al-Makhadmeh, Z., Tolba, A.: Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach. Computing 102, 501–522 (2020). https://doi.org/10.1007/s00607-019-00745-0
Salminen, J., Almerekhi, H., Milenković, M., Jung, S., An, J., Kwak, H., Jansen, B.J.: Anatomy of online hate: developing a taxonomy and machine learning models for identifying and classifying hate in online news media. In: Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2018), San Francisco, CA, USA (2018)
Salminen, J., Hopf, M., Chowdhury, S.A. et al.: Developing an online hate classifier for multiple social media platforms. Hum. Cent. Comput. Inf. Sci. 10, 1 (2020). https://doi.org/10.1186/s13673-019-0205-6
Englmeier K., Mothe J.: Application-oriented approach for detecting cyberaggression in social media. In: Ahram, T. (ed) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol. 1213. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51328-3_19
Shibly F.H.A., Sharma U., Naleer H.M.M.: Classifying and measuring hate speech in twitter using topic classifier of sentiment analysis. In: Gupta D., Khanna A., Bhattacharyya S., Hassanien A.E., Anand S., Jaiswal A. (eds.) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol. 1165. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5113-0_54
Adesara, A., Tanna, P., Joshi, H.: Hate speech detection: a bird’s-eye view. In: Kotecha K., Piuri V., Shah H., Patel R. (eds.) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol. 52. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-4474-3_26
Wachs, S., Costello, M., Wright, M.F., Flora, K., Daskalou, V., Maziridou, E., Kwon, Y., Na, E.-Y., Sittichai, R., Biswal, R., Singh, R., Almendros, C., Gámez-Guadix, M., Gӧrzig, A., Hong, J.S.: “DNT LET ’EM H8 U!”: Applying the routine activity framework to understand cyberhate victimization among adolescents across eight countries, Computers & Education, vol. 160. 104026. ISSN 0360–1315, (2021). https://doi.org/10.1016/j.compedu.2020.104026
Trana, R.E., Gomez, C.E., Adler, R.F.: Fighting cyberbullying: an analysis of algorithms used to detect harassing text found on YouTube. In: International Conference on Applied Human Factors and Ergonomics (pp. 9–15). Springer, Cham (2020)
Das, S., Anwar, M.M.: Discovering topic oriented highly interactive online community. Front. Big Data 2, 10 (2019)
Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., Hoste, V.: Automatic detection of cyberbullying in social media text. PloS one 13(10), e0203794 (2018)
Martins, R., Gomes, M., Almeida, J. J., Novais, P., Henriques, P.: Hate speech classification in social media using emotional analysis. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 61–66). IEEE (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ramana, D.V.S., Reddy, T.H. (2022). Detection of Online Hate in Social Media Platforms for Twitter Data: A Prefatory Step. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_40
Download citation
DOI: https://doi.org/10.1007/978-981-16-6616-2_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6615-5
Online ISBN: 978-981-16-6616-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)