In recent years, the reliability of information on the Internet has emerged
as a crucial issue of modern society. Social network sites (SNSs) have
revolutionized the way in which information is spread by allowing users to
freely share content. As a consequence, SNSs are also increasingly used as
vectors for the diffusion of misinformation and hoaxes. The amount of
disseminated information and the rapidity of its diffusion make it practically
impossible to assess reliability in a timely manner, highlighting the need for
automatic hoax detection systems.
As a contribution towards this objective, we show that Facebook posts can be
classified with high accuracy as hoaxes or non-hoaxes on the basis of the users
who "liked" them. We present two classification techniques, one based on
logistic regression, the other on a novel adaptation of boolean crowdsourcing
algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users,
we obtain classification accuracies exceeding 99% even when the training set
contains less than 1% of the posts. We further show that our techniques are
robust: they work even when we restrict our attention to the users who like
both hoax and non-hoax posts. These results suggest that mapping the diffusion
pattern of information can be a useful component of automatic hoax detection
systems.

该论文介绍了两种基于逻辑回归和布尔众包算法的分类技术，使用用户 “like” 信息对 Facebook 帖子进行分类，能够高准确地区分谣言和非谣言，这些结果表明，信息传播模式的映射可以是自动虚假消息检测系统的有用组成部分。

社交网络中的自动化假新闻检测

Some Like it Hoax: Automated Fake News Detection in Social Networks

Recent studies have shown that information disclosed on social network sites
(such as Facebook) can be used to predict personal characteristics with
surprisingly high accuracy. In this paper we examine a method to give online
users transparency into why certain inferences are made about them by
statistical models, and control to inhibit those inferences by hiding
("cloaking") certain personal information from inference. We use this method to
examine whether such transparency and control would be a reasonable goal by
assessing how difficult it would be for users to actually inhibit inferences.
Applying the method to data from a large collection of real users on Facebook,
we show that a user must cloak only a small portion of her Facebook Likes in
order to inhibit inferences about their personal characteristics. However, we
also show that in response a firm could change its modeling of users to make
cloaking more difficult.

本文通过分析社交网站（如 Facebook）上可被利用用以预测个人特征的信息，提出了一种能够提供对某些统计模型中对用户的推断的透明度以及控制来隐瞒个人信息以控制信息泄露的方法，并利用该方法检查用户是否能够真正实现这一隐私保护的目标。在应用于大量真实用户 Facebook 数据的过程中，我们发现仅需隐藏用户 “Facebook Likes” 数据的一小部分即可抑制有关其个人特征的推断，然而，我们也发现企业可以通过改变用户模型来增加隐瞒的难度。