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Efforts by content creators and social networks to enforce legal andpolicy-based norms, e.g. blocking hate speech and users, has driven the rise ofunrestricted communication platforms. One such recent effort is Dissenter, abrowser and web application that provides a conversational overlay for any webpage. These conversations hide in plain sight - users of Dissenter can see andparticipate in this conversation, whereas visitors using other browsers areoblivious to their existence. Further, the website and content owners have nopower over the conversation as it resides in an overlay outside their control.  In this work, we obtain a history of Dissenter comments, users, and thewebsites being discussed, from the initial release of Dissenter in Feb. 2019through Apr. 2020 (14 months). Our corpus consists of approximately 1.68Mcomments made by 101k users commenting on 588k distinct URLs. We first analyzemacro characteristics of the network, including the user-base, commentdistribution, and growth. We then use toxicity dictionaries, Perspective API,and a Natural Language Processing model to understand the nature of thecomments and measure the propensity of particular websites and content toelicit hateful and offensive Dissenter comments. Using curated rankings ofmedia bias, we examine the conditional probability of hateful comments givenleft and right-leaning content. Finally, we study Dissenter as a socialnetwork, and identify a core group of users with high comment toxicity.

Reading In-Between the Lines: An Analysis of Dissenter