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Content based approach to find the credibility of user in social networks: an application of cyberbullying

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Abstract

Cyberbullying is derogatory act carried out intentionally by sending or posting harmful material on social networks to cheat or tarnish anybody’s image in real world. Today it has become a significant problem among teenagers and kids as they spend much time on social networking. Two types of cyberbullying have been observed in the messages posted on social network: direct cyberbullying and indirect cyberbullying. Direct cyberbullying is to send disrespectful/abusing material in the form of text, images, videos and audios to harass/torture individual directly. Indirect cyberbullying is to attack or torture individuals indirectly by doing activities like sending objectionable contents such as false rumors, lies etc. concerning them, tagging their embarrassing images, refuse to socialize with the victim. These type of activities can be viewed by large number of audience on social media. Ground breaking research is being carried out only on the identification of cyberbullying and not on its categories such as direct and indirect cyberbullying. As indirect cyberbullying is much harmful than direct cyberbullying due to the messages posted online are visible to large number of users, which may adversely impact the victim’s reputation/position. So, it becomes necessary to find the solution for this problem. In this paper, we first categorize the messages into direct and indirect bullying messages and then proceed to find the solution for controlling the bullying through checking the credibility of user .

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Authors and Affiliations

  1. Computer Engineering Department, Netaji Subhas Institute of Technology, Azad Hind Fauj Marg, Sector 3, Dwarka, New Delhi, India

    Geetika Sarna & M. P. S. Bhatia

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  1. Geetika Sarna
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  2. M. P. S. Bhatia
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Correspondence to Geetika Sarna.

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Sarna, G., Bhatia, M.P.S. Content based approach to find the credibility of user in social networks: an application of cyberbullying. Int. J. Mach. Learn. & Cyber. 8, 677–689 (2017). https://doi.org/10.1007/s13042-015-0463-1

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  • DOI: https://doi.org/10.1007/s13042-015-0463-1

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