当前位置:
X-MOL 学术
›
arXiv.cs.IR
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
BaitWatcher: A lightweight web interface for the detection of incongruent news headlines
arXiv - CS - Information Retrieval Pub Date : 2020-03-23 , DOI: arxiv-2003.11459 Kunwoo Park, Taegyun Kim, Seunghyun Yoon, Meeyoung Cha, and Kyomin Jung
arXiv - CS - Information Retrieval Pub Date : 2020-03-23 , DOI: arxiv-2003.11459 Kunwoo Park, Taegyun Kim, Seunghyun Yoon, Meeyoung Cha, and Kyomin Jung
In digital environments where substantial amounts of information are shared
online, news headlines play essential roles in the selection and diffusion of
news articles. Some news articles attract audience attention by showing
exaggerated or misleading headlines. This study addresses the \textit{headline
incongruity} problem, in which a news headline makes claims that are either
unrelated or opposite to the contents of the corresponding article. We present
\textit{BaitWatcher}, which is a lightweight web interface that guides readers
in estimating the likelihood of incongruence in news articles before clicking
on the headlines. BaitWatcher utilizes a hierarchical recurrent encoder that
efficiently learns complex textual representations of a news headline and its
associated body text. For training the model, we construct a million scale
dataset of news articles, which we also release for broader research use. Based
on the results of a focus group interview, we discuss the importance of
developing an interpretable AI agent for the design of a better interface for
mitigating the effects of online misinformation.
中文翻译:
BaitWatcher:用于检测不一致新闻标题的轻量级 Web 界面
在大量信息在线共享的数字环境中,新闻标题在新闻文章的选择和传播中起着至关重要的作用。一些新闻文章通过显示夸张或误导性的标题来吸引观众的注意力。这项研究解决了 \textit{headline incongruity} 问题,其中新闻标题提出与相应文章的内容无关或相反的声明。我们展示了 \textit{BaitWatcher},这是一个轻量级的网络界面,可指导读者在点击标题之前估计新闻文章中不一致的可能性。BaitWatcher 利用分层循环编码器有效地学习新闻标题及其相关正文的复杂文本表示。为了训练模型,我们构建了百万规模的新闻文章数据集,我们也将其发布用于更广泛的研究用途。根据焦点小组访谈的结果,我们讨论了开发可解释的 AI 代理以设计更好的界面以减轻在线错误信息影响的重要性。
更新日期:2020-03-26
中文翻译:
BaitWatcher:用于检测不一致新闻标题的轻量级 Web 界面
在大量信息在线共享的数字环境中,新闻标题在新闻文章的选择和传播中起着至关重要的作用。一些新闻文章通过显示夸张或误导性的标题来吸引观众的注意力。这项研究解决了 \textit{headline incongruity} 问题,其中新闻标题提出与相应文章的内容无关或相反的声明。我们展示了 \textit{BaitWatcher},这是一个轻量级的网络界面,可指导读者在点击标题之前估计新闻文章中不一致的可能性。BaitWatcher 利用分层循环编码器有效地学习新闻标题及其相关正文的复杂文本表示。为了训练模型,我们构建了百万规模的新闻文章数据集,我们也将其发布用于更广泛的研究用途。根据焦点小组访谈的结果,我们讨论了开发可解释的 AI 代理以设计更好的界面以减轻在线错误信息影响的重要性。