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Toward efficient and effective bullying detection in online social network

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Abstract

With the advances of Information Communication Technology (ICT) and the popularity of intelligent terminals, Online Social Network, which is characterized by powerful functions of information publishing, dissemination, acquisition and sharing, has attracted a huge number of users and become one of the most popular internet application services currently. However, the growth of Online Social Network has also led to the emergence of cyberbullying issues. Information spreads extremely fast via Online Social Network, making the harm caused by cyberbullying grow exponentially with time. As a result, it becomes critical to detect the cyberbullying in a quick and efficient way. In this paper, in order to solve this challenge, we propose an improved TF-IDF based fastText (ITFT) model for effective cyberbullying detection. Specifically, in our proposed scheme, we improve the TF-IDF algorithm by adding the position weight, keywords are extracted by the improved algorithm and used as input to achieve the purpose of filtering noise data to improve the accuracy. We use the fastText to construct a binary classifier to categorize the input data. Extensive experiments are conducted, and the results demonstrate that our proposed scheme can achieve better efficiency and accuracy in cyberbullying detection as compared with baselines.

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Jiale Wu, Mi Wen & Jinguo Li

  2. Faculty of Computer Science, University of New Brunswick, Fredericton, E3B 5A3, Canada

    Rongxing Lu

  3. College of Cybersecurity, Sichuan University, Chengdu, 610065, China

    Beibei Li

Authors
  1. Jiale Wu
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  2. Mi Wen
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  3. Rongxing Lu
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  4. Beibei Li
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  5. Jinguo Li
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Correspondence to Mi Wen.

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This work is supported by the National Natural Science Foundation of China under Grant No.61872230, No.61702321 and No.61572311

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Wu, J., Wen, M., Lu, R. et al. Toward efficient and effective bullying detection in online social network. Peer-to-Peer Netw. Appl. 13, 1567–1576 (2020). https://doi.org/10.1007/s12083-019-00832-1

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