A Framework to Detect and Prevent Cyberbullying from Social Media by Exploring Machine Learning Algorithms

Social media is the most popular way to meet new people and interact with friends and associates nowadays. But unfortunately, users get subject to bully or harassment while surfing through social media. Over the last decade, cyberbullying surfaced as one of the most significant issues in the digital...

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Vydané v:2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) s. 1 - 4
Hlavní autori: Mitra, Shutonu, Tasnim, Tasfia, Islam, Md. Arr Rafi, Khan, Nafiz Imtiaz, Majib, Mohammad Shahjahan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 26.12.2021
Predmet:
Blogs
Classification algorithms
Cyber bullying
Cyberbullying
Decision making
Machine Learning
Machine learning algorithms
Sentiment analysis
Social Media
Taxonomy
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Shrnutí:Social media is the most popular way to meet new people and interact with friends and associates nowadays. But unfortunately, users get subject to bully or harassment while surfing through social media. Over the last decade, cyberbullying surfaced as one of the most significant issues in the digital world. Although significant research has been carried out to identify cyberbullying through text-mining techniques on many online platforms, still there is a long way to have a concrete solution to remove cyberbullying from social media. This paper introduces a way for the prevention of cyberbullying from social media by identification of cyberbullying texts (Twitter only) through sentiment analysis, and also classification of cyberbullying according to bullying characteristics depending on the proposed taxonomy. In this context, a suitable framework consisting of three modules (e.g., user interaction, analytics, and decision making) is proposed to prevent cyberbullying from social media. The user interaction module contains user profiles from where posts and comments are taken to the analytics module, the analytics module generates results according to the type of bully and the decision-making module takes action finally. Temporary/permanent ban on posting or commenting, bully badge shown at the personal profile are the actions proposed. However, in both bully identification and classification case, the Random Forest algorithm with TFIDF embedding has performed better with an F1 score of 80.8 and 58.4 respectively.
DOI:10.1109/IC4ME253898.2021.9768450