A Review: Context-Aware Hate Speech Detection using Feature Fusion
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22673Keywords:
hate speech, offensive language,, machine learning, deep learning,, Transformers,, Attention Mechanism,, Transfer learningAbstract
The way people communicate has changed as a result of the proliferation of digital platforms and smart devices, which provide individuals with a means to interact, voice their opinions, and share their ideas. This trend has led to an increase in posts and comments. Some individuals have resorted to posting abusive content, including hate speech, which aims to insult or incite violence and crimes against a group or individual based on a set of protected characteristics, such as religion, race, disability, religious orientation, gender, and others. The number of hate speech posts has increased recently, creating a hostile and unsafe online environment for vulnerable groups, in addition to its psychological effects on individuals within these groups. Social media platforms have resorted to using automated methods and techniques to detect and mitigate this phenomenon. This review aims to offer an in-depth understanding of previous studies in the field of hate speech detection, describing the methodologies, frameworks, and models developed using a range of standard databases while highlighting their strengths and limitations. The paper also seeks to learn how feature fusion can be used to promote the text representation with the addition of information that would advance the model to make a distinction between the categories whether in multi- or binary classification and particularly in the complex environment. What is more, the paper explains the role of context-aware models which consider texts as a unified grammatical unit in order to enhance semantic interpretation since this strategy is essential in hate speech since meaning can be context-dependent.
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Copyright (c) 2026 Marwa Adnan Ahmad, Ali Obied, Ali Hamzah Najim
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