Reading In-Between the Lines: An Analysis of Dissenter
Reading In-Between the Lines: An Analysis of Dissenter
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言外之意：对异议者的分析
DOI：
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Robert Beverly
中科院分区：
文献类型：
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作者：
Erik C. Rye;Jeremy Blackburn;Robert Beverly
内容创作者和社交网络努力执行法律的和基于政策的规范，例如阻止仇恨言论和用户，推动了不受限制的通信平台的兴起。最近的一个这样的努力是Dissenter，一个浏览器和Web应用程序，为任何网页提供会话覆盖。这些对话隐藏在普通的视线中--Dissenter的用户可以看到并参与到这个对话中，而使用其他浏览器的访问者则不知道它们的存在。此外，网站和内容所有者对对话没有任何控制权，因为它位于他们无法控制的覆盖层中。在这项工作中，我们获得了从2019年2月到2020年4月（14个月）的Dissenter评论，用户和正在讨论的网站的历史。我们的语料库由10.1万用户对58.8万个不同URL的评论组成。我们首先分析了网络的宏观特征，包括用户基础，评论分布和增长。然后，我们使用毒性字典，透视API和自然语言处理模型来理解评论的性质，并衡量特定网站和内容的倾向，以引起仇恨和攻击性的异议评论。使用媒体偏见的策划排名，我们研究了左倾和右倾内容的仇恨评论的条件概率。最后，我们将Dissenter作为一个社交网络进行研究，并确定了一个具有高评论毒性的核心用户群。
Efforts by content creators and social networks to enforce legal and policy-based norms, e.g. blocking hate speech and users, has driven the rise of unrestricted communication platforms. One such recent effort is Dissenter, a browser and web application that provides a conversational overlay for any web page. These conversations hide in plain sight -- users of Dissenter can see and participate in this conversation, whereas visitors using other browsers are oblivious to their existence. Further, the website and content owners have no power over the conversation as it resides in an overlay outside their control. In this work, we obtain a history of Dissenter comments, users, and the websites being discussed, from the initial release of Dissenter in Feb. 2019 through Apr. 2020 (14 months). Our corpus consists of approximately 1.68M comments made by 101k users commenting on 588k distinct URLs. We first analyze macro characteristics of the network, including the user-base, comment distribution, and growth. We then use toxicity dictionaries, Perspective API, and a Natural Language Processing model to understand the nature of the comments and measure the propensity of particular websites and content to elicit hateful and offensive Dissenter comments. Using curated rankings of media bias, we examine the conditional probability of hateful comments given left and right-leaning content. Finally, we study Dissenter as a social network, and identify a core group of users with high comment toxicity.
新闻网站上的仇恨言论的衡量和特征
DOI：
10.1145/3394231.3397902
发表时间：
2020
期刊：
ACM Conference on Web Science
影响因子：
--
作者：
Zannettou, Savvas;Elsherief, Mai;Belding, Elizabeth;Nilizadeh, Shirin;Stringhini, Gianluca
通讯作者：
Stringhini, Gianluca