Open Access Open Access  Restricted Access Subscription or Fee Access

Automatic Hate Speech Notifier Using Machine Learning Techniques

Dev Sharma, Ankur Singh Rawat, Aman Singhal, Ankit Verma, Ashish Kumar

Abstract


The Internet which was once considered to be a part of only high class society is now within the reach of every section of society. Due to which the number of online platform users has increased drastically. According to a survey the number of social media users has reached 1 billion. Social media platforms provide us an online platform to share our thoughts, ideas , perspectives to a larger section of a society at an instant of time with a touch of a finger. But along with these benefits it also gives rise to certain problems and one of those problems which took a steady increase in the recent decade is online hate speech. It is one of the major problems that is faced by countless people throughout the world, because of which research papers are published every year to spread awareness about online hate speech and what are the countermeasures we can take to put an end to it. In our research paper we have proposed a model of a social networking site that is capable of predicting offensive or hateful content whenever a user creates or updates a post. For creating the machine learning model we use different machine learning algorithms like passive-aggressive classifier, SVM, Naive Bayes etc and use the most accurate one for our model.

Full Text:

PDF

References


Zewdie Mossie, Jenq-Haur Wang. Vulnerable community identification using hate speech detection on social media. Information Processing & Management. 2020;57(3): 102087.

Mozafari M., Farahbakhsh R., et al. A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media. International Conference on Complex Networks and their Applications. December 10-12, 2019; Lisbon, Portugal: Springer, Cham. 2020.

Council on Foreign Relations. Hate Speech on Social Media: Global Comparisons [Online]. Available from https://www.cfr.org/backgrounder/hate-speech-social-media-global-comparisons

Archit Lohani. Countering Disinformation and Hate Speech Online: Regulation and User Behavioural Change. ORF Occasional. Paper No. 296, January 2021, Observer Research Foundation.

György Kovács, Pedro Alonso, et al. Challenges of Hate Speech Detection in Social SN Computer Science. 2021;2(95):1-15.

Joni Salminen, Maximilian Hopf, et al. Developing an online hate classifier for multiple social media platforms. Human-centric Computing and Information Sciences. 2020;10(1):1-34.

Irene Kwok, Yuzhou Wang. Locate the hate: detecting tweets against blacks. AAAI'13: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. 2013.

Edel Greevy, Alan F. Smeaton. Classifying racist texts using a support vector machine. 27th annual international ACM SIGIR conference on Research and development in information retrieval. 2004.

Edel Greevy , Alan F. Smeaton. Classifying racist texts using a support vector machine. 27th annual international ACM SIGIR conference on Research and development in information retrieval. 2004.

Jacob Devlin, Ming-Wei Chang, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Computation and Language. https://arxiv.org/abs/1810.04805 2018.

Scott, Sam. Feature engineering for a symbolic approach to text classification. uO Research. 1998. https://ruor.uottawa.ca/handle/10393/4124

Vipin Kumar and Basant Subba. A TfidfVectorizer and SVM based sentiment analysis framework for text data corpus. 2020 National Conference on Communications (NCC). 21-23 Feb. 2020; Kharagpur, India, New York: IEEE; 2020.

Cai-zhi Liu, Yan-xiu Sheng, et al. Research of Text Classification Based on Improved TF-IDF Algorithm. 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE). 24-27 Aug. 2018; Lanzhou, China, New York: IEEE; 2018.

Y.C. Ho & D.L. Pepyne. Simple Explanation of the No-Free-Lunch Theorem and Its Implications. Journal of Optimization Theory and Applications. 2002;115(3):549-570.

Ye Wang, Zhi Zhou, et al. Comparisons and Selections of Features and Classifiers for Short Text Classification. IOP Conf. Series: Materials Science and Engineering. 2017;261(012018):1-7.

Bottou Léon and Yann LeCun. Large scale online learning. Advances in Neural Information Processing Systems. 2004;16:217-224.

Koby Crammer, Ofer Dekel, et al. Online passive aggressive algorithms. Journal of Machine Learning Research. 2006;7:551–585.

Jing Lu, Peilin Zhao, et al. Online passive-aggressive active learning. Machine Learning. 2016;113:141-183.

Zhijie Liu, Xueqiang Lv, et al. Study on SVM compared with the other text classification methods. 2010 Second International Workshop on Education Technology and Computer Science. 6-7 March 2010; Wuhan, China, New York: IEEE; 2010.

Genkin, Alexander, David D. Lewis, et al. Large-scale Bayesian logistic regression for text categorization. Technometrics. 2007;49(3):291-304.

Ali, Jehad, Rehanullah Khan, et al. Random forests and decision trees. International Journal of Computer Science. 2012;9(5:3):272-278.

Ruibo Wang, Jihong Li. Bayes test of precision, recall, and f1 measure for comparison of two natural language processing models. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, July 28 - August 2, 2019. 2019 Association for Computational Linguistics.

Ashraf M. Kibriya, Eibe Frank, et al. Multinomial Naive Bayes for Text Categorization Revisited. Australasian Joint Conference on Artificial Intelligence. Springer, Berlin, Heidelberg, 2004.

Jeff Forcier, Paul Bissex, & Wesley Chun. Python Web Development with Django. Addison-Wesley Professional; 1st edition (24 October 2008).


Refbacks

  • There are currently no refbacks.