Quantifying Affective Bias in Low-Resource Media: Large-Scale Emotion Profiling of Bengali Headlines

Abstract:News media can influence readers not only through the events they report but also through the emotional tone used to present them. This issue is especially important in digital news environments, where headlines often shape first impressions before readers open the full article. This study examines affective framing in Bengali digital journalism through corpus level emotion analysis of news headlines. Using zero shot inference with Gemma 3 4B, we analyzed 300,000 Bengali news headlines to estimate the dominant emotion and overall affective tone of each headline. The results show that negative emotion labels, particularly anger, sadness, disappointment, and fear, appear frequently in the analyzed corpus. A small pilot validation on 200 manually reviewed headlines suggests that the model can provide useful emotion estimates, although the results should be interpreted as computational estimates rather than a complete benchmark. Based on these findings, we propose a conceptual bias sensitive news interface that visualizes emotional cues across news sources and helps readers notice affective framing patterns in daily news.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2510.17252 [cs.CL]

(or arXiv:2510.17252v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2510.17252 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/PECCII70991.2026.11662024 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.