arXiv subjects

Eshwar Chandrasekharan

Publications and source records attributed to Eshwar Chandrasekharan.

43 records · Page 3Linked to original sources

arXiv2024

Creator Hearts: Investigating the Impact Positive Signals from YouTube Creators in Shaping Comment Section Behavior

Much of the research in online moderation focuses on punitive actions. However, emerging research has shown that positive reinforcement is effective at encouraging desirable behavior on online platforms. We extend this research by studying the "creator heart" feature on YouTube, quantifying their primary effects on comments that receive hearts and on videos where hearts have been given. We find that creator hearts increased the visibility of comments, and increased the amount of positive engagement they received from other users. We also find that the presence of a creator hearted comment soon after a video is published can incentivize viewers to comment, increasing the total engagement with the video over time. We discuss the potential for creators to use hearts to shape behavior in their communities by highlighting, rewarding, and incentivizing desirable behaviors from users. We discuss avenues for extending our study to understanding positive signals from moderators on other platforms.

cs.HC↗

arXiv2022

Harmonizing the Cacophony with MIC: An Affordance-aware Framework for Platform Moderation

Social platforms, and the online communities that use them, are evolving at a rapid pace. As a result, research and development regarding how to moderate online communities is being out-paced. In this paper, we present a novel framework that will allow moderation researchers and practitioners to not only keep-up with the diverse landscape of available platforms and affordances, but also comprehensively represent and analyze moderation on these platforms. The MIC framework represents a social platform's moderation ecosystem using a base-set of 12 platform-level affordances, along with a notion of the inter-affordance relationships that can exist between them. These affordances fall into the three categories -- Members, Infrastructure, and Content -- that are derived from Grimmelmann's taxonomy of moderation, a framework that is already widely accepted and used by the moderation research community. To show how MIC serves as an insightful augmentation of Grimmelmann's lens, we begin by describing how its components have already been shown to impact Grimmelmann's techniques for moderation. Then, we demonstrate the advantages of using an affordance-aware framework like MIC by analyzing several social platforms over the course of two case studies. First, we analyze individual platforms using MIC and demonstrate how MIC can be used to examine the effects of platform changes on the moderation ecosystem and identify potential new challenges in moderation. Next, use MIC to systematically compare three platforms and propose potential moderation mechanisms that each can adapt. Moderation researchers and stakeholders can use such comparisons to uncover where platforms can emulate established, successful and better-studied platforms, as well as learn from the pitfalls other platforms have encountered.

cs.HC↗

arXiv2021

Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit

Should social media platforms override a community's self-policing when it repeatedly break rules? What actions can they consider? In light of this debate, platforms have begun experimenting with softer alternatives to outright bans. We examine one such intervention called quarantining, that impedes direct access to and promotion of controversial communities. Specifically, we present two case studies of what happened when Reddit quarantined the influential communities r/TheRedPill (TRP) and r/The_Donald (TD). Using over 85M Reddit posts, we apply causal inference methods to examine the quarantine's effects on TRP and TD. We find that the quarantine made it more difficult to recruit new members: new user influx to TRP and TD decreased by 79.5% and 58%, respectively. Despite quarantining, existing users' misogyny and racism levels remained unaffected. We conclude by reflecting on the effectiveness of this design friction in limiting the influence of toxic communities and discuss broader implications for content moderation.

cs.SI↗

arXiv2021

Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations

Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them-is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing a relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.

cs.CY↗

arXiv2019

Hybrid Approaches to Detect Comments Violating Macro Norms on Reddit

In this dataset paper, we present a three-stage process to collect Reddit comments that are removed comments by moderators of several subreddits, for violating subreddit rules and guidelines. Other than the fact that these comments were flagged by moderators for violating community norms, we do not have any other information regarding the nature of the violations. Through this procedure, we collect over 2M comments removed by moderators of 100 different Reddit communities, and publicly release the data. Working with this dataset of removed comments, we identify 8 macro norms---norms that are widely enforced on most parts of Reddit. We extract these macro norms by employing a hybrid approach---classification, topic modeling, and open-coding---on comments identified to be norm violations within at least 85 out of the 100 study subreddits. Finally, we label over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated, and make this dataset publicly available. By breaking down a collection of removed comments into more granular types of macro norm violation, our dataset can be used to train more nuanced machine learning classifiers for online moderation.

cs.SI↗

arXiv2019

A Just and Comprehensive Strategy for Using NLP to Address Online Abuse

Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities.

cs.SI↗

arXiv2019

Still out there: Modeling and Identifying Russian Troll Accounts on Twitter

There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter - often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral and linguistic features, we show that it is possible to distinguish between a troll and a non-troll with a precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the model to out-of-sample accounts still active today, we find that up to 2.6% of top journalists' mentions are occupied by Russian trolls. These findings imply that the Russian trolls are very likely still active today. Additional analysis shows that they are not merely software-controlled bots, and manage their online identities in various complex ways. Finally, we argue that if it is possible to discover these accounts using externally - accessible data, then the platforms - with access to a variety of private internal signals - should succeed at similar or better rates.

cs.SI↗