The paper will be out with the ACL proceedings soon, I’ll keep you posted !

🤝 Human-AI synergy remains essential: AI speeds up analysis and identifies overarching themes. Humans provide interpretative depth, socio-political and historical awareness, and ideological sensitivity crucial in discourse analysis.

💡 Our Suggestion: To maximize the potential of LLMs, combine AI’s scalability with human expertise through culturally-informed training and theory-driven prompting.

❗️ The Problem: AI neutrality often obscures deeper socio-political meanings. The model often misclassifies ideologically charged statements, underplaying their socio-political implications.

🔍 Key Findings:
GPT-4 efficiently identifies broad themes in large datasets but struggles with ideologically charged and culturally sensitive discourse. The model tends to default to neutral categories, failing to explicitly recognize hate speech or exclusionary narratives.