Abstract
Due to the growing use of social media, incidents of online abuse are also on rise. Online abusive behavior is defined as the use of electronic devices connected through internet for offensive activities. It is mostly in the form of comments containing abusive words about others, which affect the target users’ psychology and depresses them. This paper is aimed at devising method for detecting abusive behavior using supervised learning techniques. Two hypotheses are presented to extract features for detection of offensive comments. The initial experiments show that using features using our proposed method has better accuracy than the traditional feature extraction techniques like TF-IDF.
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Qaddoum, K., Ahmad, I., Javed, Y., Rodan, A. (2019). An Enhanced Model for Abusive Behavior Detection in Social Network. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_43
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DOI: https://doi.org/10.1007/978-3-030-12839-5_43
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Online ISBN: 978-3-030-12839-5
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