How News Feels: Understanding Affective Bias in Multilingual Headlines for Human-Centered Media Design

TL;DR

This paper investigates emotional biases in Bengali news headlines, revealing dominant negative emotions and outlet variations, and proposes visualization tools for more transparent, human-centered news consumption.

Contribution

It introduces large-scale emotion analysis of Bengali headlines using zero-shot inference and offers novel design ideas for visualizing affective framing in news media.

Findings

Negative emotions dominate headlines, especially anger, fear, and disappointment.

Significant variation exists in emotional portrayal across news outlets.

Proposed visualization ideas aim to reveal affective biases to readers.

Abstract

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.