构建新闻图像耸动性检测数据集，助力识别易传播的煽情内容
Sens-VisualNews: A Benchmark Dataset for Sensational Image Detection
- 基于新闻图像构建包含9576张图的标注数据集
- 发现多模态大模型在零样本与微调下对耸动内容敏感度差异
- 适合媒体安全、虚假信息检测方向的研究者参考
媒体中耸动内容的检测可作为识别值得核查信息和标记潜在虚假信息的关键过滤机制，因其能触发生理唤醒，绕过批判性思考并加速病毒式传播。本文提出耸动图像检测任务，旨在判断图像是否含有令人震惊、挑衅或情绪化特征以吸引注意并引发强烈情感反应。为支持该任务研究，我们创建了一个新基准数据集Sens-VisualNews，包含来自新闻条目的9,576张图像，依据其视觉内容中是否存在各类耸动概念与事件进行标注。最后，利用Sens-VisualNews，我们研究了多种开源前沿多模态大模型在零样本与微调设置下的提示敏感性、性能及鲁棒性。
原文摘要 · Abstract (English)
The detection of sensational content in media items can be a critical filtering mechanism for identifying check-worthy content and flagging potential disinformation, since such content triggers physiological arousal that often bypasses critical evaluation and accelerates viral sharing. In this paper we introduce the task of sensational image detection, which aims to determine whether an image contains shocking, provocative, or emotionally charged features to grab attention and trigger strong emotional responses. To support research on this task, we create a new benchmark dataset (called Sens-VisualNews) that contains 9,576 images from news items, annotated based on the (in-)existence of various sensational concepts and events in their visual content. Finally, using Sens-VisualNews, we study the prompt sensitivity, performance and robustness of a wide range of open SotA Multimodal LLMs, across both zero-shot and fine-tuned settings.
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