Kun Wang
Editorial Notes
The editors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on February 9, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.
Abstract
With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect on mental health, especially for teenagers. In order to reduce or even stop cyberbullying, different machine learning techniques are applied and numerous studies have been conducted so far. However, conventional detection schemes still have challenges, such as low accuracy. Therefore, it is of significant importance to find an efficient detection solution in the natural language processing and machine learning communities. In the present study, characteristics of cyberbullying are initially analyzed from vocabulary and syntax points of view. Then a new detection algorithm is proposed based on FastText and word similarity schemes. Finally, experiments are carried out to evaluate the effectiveness and performance of the proposed method. Obtained results show that the proposed algorithm can effectively improve the detection accuracy and recall rate of cyberbullying detection.
Supplemental Material
Available for Download
Version of Record for "Cyberbullying Detection, Based on the FastText and Word Similarity Schemes" by Wang et al., ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 20, Issue 1 (TALLIP 20:1).
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Index Terms
- Cyberbullying Detection, Based on the FastText and Word Similarity Schemes
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