分析30万条孟加拉语新闻标题，发现负面情绪主导报道风格。
Quantifying Affective Bias in Low-Resource Media: Large-Scale Emotion Profiling of Bengali Headlines
- 用Gemma 3 4B模型进行零样本推理，批量分析孟加拉语新闻标题情感
- 30万标题中愤怒、悲伤、失望和恐惧等负面情绪标签频繁出现
- 提出可视化界面帮助读者识别新闻中的情感倾向，适合关注媒体偏见的研究者
新闻媒体不仅通过报道事件影响读者，还通过情感基调塑造认知。在数字新闻环境中，标题常决定读者的第一印象。本研究通过大规模语料库分析，对30万条孟加拉语新闻标题进行情感建模。采用Gemma 3 4B进行零样本推理，估算每条标题的主导情绪与整体情感基调。结果显示，愤怒、悲伤、失望和恐惧等负面情绪标签在语料库中高频出现。对200条人工标注标题的小规模验证表明，模型可提供有参考价值的情感估计，但结果仍为计算推断，非完整基准。基于此，我们提出一种感知偏见的新闻界面，通过可视化不同来源的情绪线索，帮助读者察觉日常新闻中的情感框架模式。
原文摘要 · Abstract (English)
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.
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