Detection of Hate Tweets using Machine Learning and Deep Learning

Ketsbaia, Lida, Issac, Biju and Chen, Xiaomin (2020) Detection of Hate Tweets using Machine Learning and Deep Learning. In: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom): 29 December 2020 – 1 January 2021 Guangzhou, China. IEEE, Piscataway, NJ, pp. 751-758. ISBN 9781665403931, 9781665403924

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Abstract

Cyberbullying has become a highly problematic occurrence due to its potential of anonymity and its ease for others to join in the harassment of victims. The distancing effect that technological devices have, has led to cyberbullies say and do harsher things compared to what is typical in a traditional face-to-face bullying situation. Given the great importance of the problem, detection is becoming a key area of cyberbullying research. Therefore, it is highly necessary for a framework to accurately detect new cyberbullying instances automatically. To review the machine learning and deep learning approaches, two datasets were used. The first dataset was provided by the University of Maryland consisting of over 30,000 tweets, whereas the second dataset was based on the article `Automated Hate Speech Detection and the Problem of Offensive Language' by Davidson et al., containing roughly 25,000 tweets. The paper explores machine learning approaches using word embeddings such as DBOW (Distributed Bag of Words) and DMM (Distributed Memory Mean) and the performance of Word2vec Convolutional Neural Networks (CNNs) to classify online hate.

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