Fake news detection is a critical yet challenging problem in Natural Language
Processing (NLP). The rapid rise of social networking platforms has not only
yielded a vast increase in information accessibility but has also accelerated
the spread of fake news. Thus, the effect of fake news has been growing,
sometimes extending to the offline world and threatening public safety. Given
the massive amount of Web content, automatic fake news detection is a practical
NLP problem useful to all online content providers, in order to reduce the
human time and effort to detect and prevent the spread of fake news. In this
paper, we describe the challenges involved in fake news detection and also
describe related tasks. We systematically review and compare the task
formulations, datasets and NLP solutions that have been developed for this
task, and also discuss the potentials and limitations of them. Based on our
insights, we outline promising research directions, including more
fine-grained, detailed, fair, and practical detection models. We also highlight
the difference between fake news detection and other related tasks, and the
importance of NLP solutions for fake news detection.

本文介绍了如何应用自然语言处理技术解决社交网络上的虚假新闻检测问题，并着重描述了该问题的挑战以及相关任务的形式、数据集和解决方案，并讨论了其潜力和局限性，并提出了更精细、详细、公平和实用的检测模型等研究方向。

用于虚假新闻检测的自然语言处理综述

A Survey on Natural Language Processing for Fake News Detection

Like sighted people, visually impaired people want to share photographs on
social networking services, but find it difficult to identify and select photos
from their albums. We aimed to address this problem by incorporating
state-of-the-art computer-generated descriptions into Facebook's photo-sharing
feature. We interviewed 12 visually impaired participants to understand their
photo-sharing experiences and designed a photo description feature for the
Facebook mobile application. We evaluated this feature with six participants in
a seven-day diary study. We found that participants used the descriptions to
recall and organize their photos, but they hesitated to upload photos without a
sighted person's input. In addition to basic information about photo content,
participants wanted to know more details about salient objects and people, and
whether the photos reflected their personal aesthetics. We discuss these
findings from the lens of self-disclosure and self-presentation theories and
propose new computer vision research directions that will better support visual
content sharing by visually impaired people.

为了帮助视障人士更好地分享社交网络的照片，我们将最先进的计算机生成描述结合到 Facebook 的图片分享功能中，并通过采访和实验评估了功能。我们从自我揭示和自我呈现理论的角度讨论了这些发现，并提出了能够更好地支持视障人士视觉内容分享的计算机视觉研究方向。