Reading In-Between the Lines: An Analysis of Dissenter

TL;DR

This study analyzes Dissenter, a platform enabling anonymous, overlay-based discussions on web pages, examining its user behavior, comment toxicity, and political bias over 14 months with 1.68 million comments.

Contribution

It provides a comprehensive analysis of Dissenter's user interactions, toxicity levels, and political bias, offering insights into unmoderated online discussions.

Findings

High comment toxicity associated with certain websites

Left- and right-leaning content have different toxicity patterns

A core group of highly toxic users identified

Abstract

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.