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Journal of Cognitive Human-Computer Interaction

Volume 2, Issue 2, PP: 56-59, 2022 | Cite this article as | XML | | Html PDF

Hate Speech Detection on Social Media Using Machine Learning Algorithms

Rupesh Chaudhari   1 , Ritik Gad   2 , Pranav Gawali   3 , Mangesh Gite   4 , Dr. A. B. Pawa   5

  • 1 Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India. - (rupeshchaudhari2151@gmail.com,)
  • 2 Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India. - (ritikgade22@gmail.com)
  • 3 Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India. - (pranavgawali2510@gmail.com)
  • 4 Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India. - (mangeshgite9@gmail.com)
  • 5 Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India. - (pawaranilcomp@sanjivani.org.in)

    • Doi: https://doi.org/10.54216/JCHCI.020203

    Received:December28, 2021 Accepted:April2, 2022
    Abstract

    There is an enormous growth of social media which fully promotes freedom of expression through its

    anonymity feature. Freedom of expression is a human right but hate speech towards a person or group

    based on race, caste, religion, ethnic or national origin, sex, disability, gender identity, etc. is an abuse of

    this sovereignty. It seriously promotes violence or hate crimes and creates an imbalance in society by

    damaging peace, credibility, and human rights, etc. To overcome this problem, the hate speech detection

    model is made which will classify the speech and if the speech used by user is containing hate word, it

    will be detected and system will sent an alert message to user about it. In order to solve various hate

    speech problems we use some of the machine learning algorithms such as logistic regression and random

    forest. If user disrupts cyber guidelines, then strict action shall be taken and user’s account will be ban

    forever. This help to reduce cyber crimes in effective and efficient manner.

    Keywords :

    Machine learning, Hate speech, Natural language processing, Data pre-processing, Random forest,

    Logistic regression, Hate word classification.

    References

    [1] Schmidt, Anna Wiegand, Michael, ”A Survey on Hate Speech Detection using Natural Language Processing”, 1-

    10. 10.18653/v1/W17-1101. URL : https://aclanthology.org/W17-1101 , (2017).

    [2] H. Watanabe, M. Bouazizi and T. Ohtsuki, ”Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful

    and Offensive Expressions and Perform Hate Speech Detection,” in IEEE Access, vol. 6, pp. 13825-13835,

    2018, doi: 10.1109/ACCESS.2018.2806394.

    [3] Stephen Wermiel, ”The Ongoing Challenge to Define Free Speech”, 43 Human Rights 82 (2018).

    [4] MacAvaney S, Yao H-R, Yang E, Russell K, Goharian N, Frieder O, ”Hate speech detection: Challenges and

    solutions”, PLoS ONE 14(8): e0221152, https://doi.org/10.1371/journal.pone.0221152 , (2019).

    [5] Vu, Xuan-Son Vu, Thanh Tran, Mai-Vu Le-Cong, Thanh Nguyen, Huyen, ”HSD Shared Task in VLSP

    Campaign 2019:Hate Speech Detection for Social Good”,(2020).

    [6] ’Twitter’ Website, 2021, The Twitter Rules, Retrieved from this site: https://support.twitter.com/articles/.

    [7]’Youtube’ Website, 2021, Hate speech, Retrieved from this site :

    https://support.google.com/youtube/answer/2801939?hl=en

    [8] K. A. Qureshi and M. Sabih, ”Un-Compromised Credibility: Social Media Based Multi-Class Hate Speech

    Classification for Text”, in IEEE Access, vol. 9, pp. 109465-109477, 2021, doi:

    10.1109/ACCESS.2021.3101977.

    [9] Mathew, Binny, et al. "Analyzing the hate and counter speech accounts on twitter." arXiv preprint

    arXiv:1812.02712 ,url- https://doi.org/10.48550/arXiv.1812.02712 (2018).

    [10] P. Kavitha , R. Subha Shini , R. Priya, "An Implementation Of Statistical Feature Algorithms For The Detection

    Of Brain Tumor", Journal of Cognitive Human-Computer Interaction, 2021, DOI:

    https://doi.org/10.54216/JCHCI.010202.

    Cite This Article As :
    Rupesh Chaudhari, Ritik Gad, Pranav Gawali, Mangesh Gite, Dr. A. B. Pawa. "Hate Speech Detection on Social Media Using Machine Learning Algorithms." Full Length Article, Vol. 2, No. 2, 2022 ,PP. 56-59 (Doi   :  https://doi.org/10.54216/JCHCI.020203)
    Rupesh Chaudhari, Ritik Gad, Pranav Gawali, Mangesh Gite, Dr. A. B. Pawa. (2022). Hate Speech Detection on Social Media Using Machine Learning Algorithms. Journal of , 2 ( 2 ), 56-59 (Doi   :  https://doi.org/10.54216/JCHCI.020203)
    Rupesh Chaudhari, Ritik Gad, Pranav Gawali, Mangesh Gite, Dr. A. B. Pawa. "Hate Speech Detection on Social Media Using Machine Learning Algorithms." Journal of , 2 no. 2 (2022): 56-59 (Doi   :  https://doi.org/10.54216/JCHCI.020203)
    Rupesh Chaudhari, Ritik Gad, Pranav Gawali, Mangesh Gite, Dr. A. B. Pawa. (2022). Hate Speech Detection on Social Media Using Machine Learning Algorithms. Journal of , 2 ( 2 ), 56-59 (Doi   :  https://doi.org/10.54216/JCHCI.020203)
    Rupesh Chaudhari, Ritik Gad, Pranav Gawali, Mangesh Gite, Dr. A. B. Pawa. Hate Speech Detection on Social Media Using Machine Learning Algorithms. Journal of , (2022); 2 ( 2 ): 56-59 (Doi   :  https://doi.org/10.54216/JCHCI.020203)
    Rupesh Chaudhari, Ritik Gad, Pranav Gawali, Mangesh Gite, Dr. A. B. Pawa, Hate Speech Detection on Social Media Using Machine Learning Algorithms, Journal of , Vol. 2 , No. 2 , (2022) : 56-59 (Doi   :  https://doi.org/10.54216/JCHCI.020203)

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