Polarization and the marketplace for impressions have conspired to make
navigating information online difficult for users, and while there has been a
significant effort to detect false or misleading text, multimodal datasets have
received considerably less attention. To complement existing resources, we
present multimodal Video Misleading Headline (VMH), a dataset that consists of
videos and whether annotators believe the headline is representative of the
video's contents. After collecting and annotating this dataset, we analyze
multimodal baselines for detecting misleading headlines. Our annotation process
also focuses on why annotators view a video as misleading, allowing us to
better understand the interplay of annotators' background and the content of
the videos.

通过收集和注释 Multimodal Video Misleading Headline（VMH）数据集，我们分析了侦测误导性标题的多模态基线，同时注重了注释者对视频误导性的看法和背景与视频内容之间的相互作用。

不是所有的假新闻都是文字：一个关于误导性视频标题的数据集和分析

Not all Fake News is Written: A Dataset and Analysis of Misleading Video  Headlines

Accuracy is one of the basic principles of journalism. However, it is
increasingly hard to manage due to the diversity of news media. Some editors of
online news tend to use catchy headlines which trick readers into clicking.
These headlines are either ambiguous or misleading, degrading the reading
experience of the audience. Thus, identifying inaccurate news headlines is a
task worth studying. Previous work names these headlines "clickbaits" and
mainly focus on the features extracted from the headlines, which limits the
performance since the consistency between headlines and news bodies is
underappreciated. In this paper, we clearly redefine the problem and identify
ambiguous and misleading headlines separately. We utilize class sequential
rules to exploit structure information when detecting ambiguous headlines. For
the identification of misleading headlines, we extract features based on the
congruence between headlines and bodies. To make use of the large unlabeled
data set, we apply a co-training method and gain an increase in performance.
The experiment results show the effectiveness of our methods. Then we use our
classifiers to detect inaccurate headlines crawled from different sources and
conduct a data analysis.

本研究通过重新定义问题、识别模糊和具有误导性的标题，并利用类序贯规则和共同训练的方法，成功筛选出来自不同来源的不准确标题，并进行数据分析。