Abstract
In recent years, a lot of data is being poured on social media. Due to the penetration of social media among people, a lot of people have started posting their sentiments, ideas, etc., on social media. These posts can be facts or personal emotions. In this paper, we introduce the concept of hate speech and discuss how it differs from non-hate speeches. The concept of hate speech is very old; however, posting them on social media needs special attention. We have reviewed several techniques and approaches to identify hate speech from textual data with a focus on micro-blogs. Since the notion of hate speech is quite personal, we feel that better IR systems are required to identify hate speech and delete build the systems that are capable to delete the content automatically from social media.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Twitter Policy homepage. https://help.twitter.com/en/rules-and-policies/hateful-conduct-policy. Accessed on 14 Dec 2019
Malmasi S, Zampieri M (2017) Hate speech on facebook. In: Proceedings of recent advances in natural language processing, Varna, Bulgaria, pp 467–472
Sutejo TL, Lestari D (2018) Indonesia hate speech detection using deep learning. In: International conference on Asian language processing (IALP)
Law Commission of India, Report No. 267
Badjatiya P, Gupta M, Varma V, Gupta S (2017)Deep learning for hate speech detection in tweets. In: Proceedings of ACM—WWW’17 companion
Lee Y, Jung K, Yoon S (2018) Comparative studies of detecting abusive language on twitter. In: 2nd Workshop on abusive language online to be held at EMNLP
Huang R, Gao L (2017) Detecting online hate speech using context aware models. In: Proceedings of the international conference recent advances in natural language processing, pp 260–266
Fortuna P, Nunes S (2018) A survey on automatic detection of hate speech in text. ACM Comput Surv 51(4):1–30
Nobata C, Tetreault J, Thomas A, Mehdad Y, Chang Y (2016) Abusive language detection in online user content. In: Proceedings of the 25th international conference on world wide web. International world wide web conferences steering committee, pp 145–153
Modha S, Mandl T, Majumder P, Patel D (2019) Overview of the HASOC track at FIRE 2019: hate speech and offensive content identification in Indo-European languages. In: Proceedings of the 11th annual meeting of the forum for information retrieval evaluation
Cleverdon CW, Mills J, Keen EM (1966) Factors determining the performance of indexing systems, vol 1
Gaydhani A, Bhagwat L, Kendre S, Doma V (2018) Detecting hate speech and offensive language on twitter using machine learning: an N-gram and TF-IDF based approach. In: IEEE international advance computing conference
Watanabe H, Ohtsuki T, Bouazizi M (2018) Hate speech on twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6:13825–13835
Davidson T, Warmsley D, Macy M, Macy I (2017) Automated hate speech detection and the problem of offensive language. In: International AAAI conference on web and social media
Greevy E (2004) Automatic text categorisation of racist webpages. Ph.D. Dissertation. Dublin City University
Burnap P, Williams ML (2014) Hate speech, machine classification and statistical modelling of information flows on twitter: interpretation and communication for policy decision making. In: Proceedings of the conference on the internet, policy & politics, pp 1–18
Tulkens S, Hilte L, Lodewyckx E, Verhoeven B, Daelemans W (2016) A dictionary-based approach to racism detection in dutch social media. arXiv Preprint arXiv:1608.08738
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vadesara, A., Tanna, P., Joshi, H. (2021). 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. https://doi.org/10.1007/978-981-15-4474-3_26
Download citation
DOI: https://doi.org/10.1007/978-981-15-4474-3_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4473-6
Online ISBN: 978-981-15-4474-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)