HASOC-meme (2025)

Overview

Social media sites like Twitter and Facebook, being user-friendly and accessible sources, provide opportunities for people to air their voices. People, irrespective of age group, use these sites to share every moment of their lives, making these sites flooded with data. Apart from these commendable features of social media, they also have downsides. The large fraction of hate speech and other offensive and objectionable content online poses a vast challenge to societies. Offensive language such as insulting, hurtful, derogatory, or obscene content directed from one person to another and open to others undermines objective discussions. Such a type of language can be increasingly found on the web and lead to the radicalization of debates. Public opinion-forming requires rational critical discourse (Habermas 1984). Objectionable content can pose a threat to democracy. At the same time, open societies need to find an acceptable way to react to such content without imposing rigid censorship regimes.
As a consequence, many platforms of social media websites monitor user posts. This leads to a pressing demand for methods to automatically identify suspicious posts. Online communities, social media enterprises, and technology companies have been investing heavily in technology and processes to identify offensive language to prevent abusive behavior on social media. Hate detection from multimodal data is also required because harmful content online often combines images and text to spread hate in subtle or coded ways. Text alone may seem harmless, and images alone may not convey the full message, but together they can create powerful and offensive meanings. By analyzing both modalities, we can better understand the context, detect hidden hate speech, and build more accurate and reliable moderation systems. This helps create safer online environments and prevents the spread of toxic and harmful content across social media and other platforms.