How News Feels: Understanding Affective Bias in Multilingual Headlines for Human-Centered Media Design
This addresses the issue of affective bias in media for readers and designers, but it is incremental as it applies an existing method to a new dataset.
The researchers tackled the problem of emotional bias in news headlines by analyzing 300,000 Bengali headlines using zero-shot inference with Gemma-3 4B, finding a clear dominance of negative emotions like anger, fear, and disappointment, and significant variation in emotional portrayal across outlets.
News media often shape the public mood not only by what they report but by how they frame it. The same event can appear calm in one outlet and alarming in another, reflecting subtle emotional bias in reporting. Negative or emotionally charged headlines tend to attract more attention and spread faster, which in turn encourages outlets to frame stories in ways that provoke stronger reactions. This research explores that tendency through large-scale emotion analysis of Bengali news. Using zero-shot inference with Gemma-3 4B, we analyzed 300000 Bengali news headlines and their content to identify the dominant emotion and overall tone of each. The findings reveal a clear dominance of negative emotions, particularly anger, fear, and disappointment, and significant variation in how similar stories are emotionally portrayed across outlets. Based on these insights, we propose design ideas for a human-centered news aggregator that visualizes emotional cues and helps readers recognize hidden affective framing in daily news.