The spread of misinformation or fake-news is a global concern that undermines
progress on issues such as protecting democracy and public health. Past
research aiming to combat its spread has largely focused on identifying its
semantic content and media outlets publishing such news. In contrast, we aim to
identify individuals who are more likely to share fake-news by studying the
language of actors in the fake-news ecosystem (such as fake-news sharers,
fact-check sharers and random twitter users), and creating a linguistic profile
of them. Fake-news sharers and fact-check sharers use significantly more
high-arousal negative emotions in their language, but fake-news sharers express
more existentially-based needs than other actors. Incorporating
psycholinguistic cues as inferred from their tweets into a model of
socio-demographic predictors considerably improves classification accuracy of
fake-news sharers. The finding that fake-news sharers differ in important ways
from other actors in the fake-news ecosystem (such as in their existential
needs), but are also similar to them in other ways (such as in their anger
levels), highlights the importance of studying the entire fake-news ecosystem
to increase accuracy in identification and prediction. Our approach can help
mitigate fake-news sharing by enabling platforms to pre-emptively screen
potential fake-news sharers' posts.

该研究旨在通过研究虚假新闻生态系统中的语言学家（例如虚假新闻分享者，谣言核实分享者和随机 Twitter 用户）和创造其语言特征剖面来确定更可能分享虚假新闻的个人，并将所推断的心理语言学提示结合社会人口预测因子包含在一个模型中，从而显著提高虚假新闻分享者的分类准确性，这可以帮助在线平台及时屏蔽潜在的虚假新闻分享者。

谁会在 Twitter 上分享虚假新闻？在线帖子历史中的心理语言暗示区分误传生态系统中的参与者

Who will share Fake-News on Twitter? Psycholinguistic cues in online post histories discriminate Between actors in the misinformation ecosystem

With the growth of the internet, the number of fake-news online has been
proliferating every year. The consequences of such phenomena are manifold,
ranging from lousy decision-making process to bullying and violence episodes.
Therefore, fact-checking algorithms became a valuable asset. To this aim, an
important step to detect fake-news is to have access to a credibility score for
a given information source. However, most of the widely used Web indicators
have either been shut-down to the public (e.g., Google PageRank) or are not
free for use (Alexa Rank). Further existing databases are short-manually
curated lists of online sources, which do not scale. Finally, most of the
research on the topic is theoretical-based or explore confidential data in a
restricted simulation environment. In this paper we explore current research,
highlight the challenges and propose solutions to tackle the problem of
classifying websites into a credibility scale. The proposed model automatically
extracts source reputation cues and computes a credibility factor, providing
valuable insights which can help in belittling dubious and confirming trustful
unknown websites. Experimental results outperform state of the art in the
2-classes and 5-classes setting.

本论文探讨当前研究的挑战，并提出解决方案来解决将网站分类为可信度范围的问题。所提出的模型自动提取来源信誉线索并计算信誉因素，实验结果在 2 类和 5 类设置中均优于现有技术水平。