Year: 2024
Type: article
Abstract: In recent years, social media users have become increasingly concerned about sharing content that may be unpleasant or harmful. The widespread use of platforms like Facebook and Twitter has contributed significantly to this growing awareness. The primary objective of our approach is to accelerate and automate the detection of offensive content posted on these platforms, simplifying the process of taking necessary actions and filtering harmful communications. A benchmark dataset, OLID 2019 (Offensive Language Identification Dataset), is available online to aid in this task. Our study focuses on identifying whether a tweet is offensive. Our team, which included several members, rigorously compared various feature extraction methods and model-building algorithms. Ultimately, our comparative analysis revealed that decision trees were the most effective model. The decision trees applied to the normalized dataset resulted in an 84% improvement in the Macro F1 score, which aligns with previous research. In conclusion, a real-time system could be developed across multiple social media platforms to detect and evaluate objectionable posts, enabling timely interventions to promote healthier online behavior and foster a positive societal impact.
DOI: 10.33411/ijist/1135
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203547783