The rapid rise of social media has emphasized the spread of harmful and hateful content, making it challenging for its identification. Contextual semantics is very important as prior studies present that context level semantics is a more trustworthy indicator of hatefulness than word level semantics for detecting hate speech. This paper attempts to check the usability of transformer-based models for the identification of hate speech on code-mixed datasets, which includes Google-MuRIL, LaBSE, XLMRoberta-base, mbert and distil-mbert. The above is largely due to its ability for high-level representations of complex and context-dense meaning. Besides this, we experiment on ensemble approach that covers all of the above models to reach out for an even higher level of performance in detection. The experiment results show the best performing macro F1-scores are reported in case of MuRIL in comparison to other implemented models.

Social media hate speech is a significant issue because it may incite violence, discrimination, and social unrest. Anonymity and reach of such platforms enable the rapid spread of harmful content, targeting individuals or communities based on race, gender, religion, or other attributes. The detection of hate speech is very important for the creation of safe online environments, protection of marginalized groups, and compliance with legal and ethical standards. This paper aims to analyze complex social media content using a combination of textual and audio features. The experimental results establish the effectiveness of the proposed approach, with F1-scores reaching 72% for Tamil, 77% for Malayalam, and 36% for Telugu. Such results strongly indicate that multimodal methodologies have significant room for improvement in hate speech detection in resource-constrained languages and underscore the need to continue further research into this critical area.

A meme is essentially an artefact of content- usually an amalgamation of a picture, text, or video-content that spreads like wildfire on the internet, usually shared for amusement, cultural expression, or commentary. They are very much similar to an inside joke or a cultural snapshot that reflects shared ideas, emotions, or social commentary, remodulated and reformed by communities. Some of them carry harmful content, such as misogyny. A misogynistic meme is social commentary that espouses negative stereotypes, prejudice, or hatred against women. The detection and addressing of such content will help make the online space inclusive and respectful. The work focuses on developing a multimodal approach for categorizing misogynistic and non-misogynistic memes through the use of pretrained XLM-RoBERTa to draw text features and Vision Transformer to draw image features. The combination of both text and images features are processed into a machine learning and deep learning model which have attained F1-scores 0.77 and 0.88, respectively Tamil and Malayalam for misogynist Meme Dataset.