  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.
