Understanding the Bystander Effect on Toxic Twitter Conversations

11/19/2022
by   Ana Aleksandric, et al.
0

In this study, we explore the power of group dynamics to shape the toxicity of Twitter conversations. First, we examine how the presence of others in a conversation can potentially diffuse Twitter users' responsibility to address a toxic direct reply. Second, we examine whether the toxicity of the first direct reply to a toxic tweet in conversations establishes the group norms for subsequent replies. By doing so, we outline how bystanders and the tone of initial responses to a toxic reply are explanatory factors which affect whether others feel uninhibited to post their own abusive or derogatory replies. We test this premise by analyzing a random sample of more than 156k tweets belonging to  9k conversations. Central to this work is the social psychological research on the "bystander effect" documenting that the presence of bystanders has the power to alter the dynamics of a social situation. If the first direct reply reaffirms the divisive tone, other replies may follow suit. We find evidence of a bystander effect, with our results showing that an increased number of users participating in the conversation before receiving a toxic tweet is negatively associated with the number of Twitter users who responded to the toxic reply in a non-toxic way. We also find that the initial responses to toxic tweets within conversations is of great importance. Posting a toxic reply immediately after a toxic comment is negatively associated with users posting non-toxic replies and Twitter conversations becoming increasingly toxic.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/07/2022

User Engagement and the Toxicity of Tweets

Twitter is one of the most popular online micro-blogging and social netw...
research
05/25/2021

The Structure of Toxic Conversations on Twitter

Social media platforms promise to enable rich and vibrant conversations ...
research
01/07/2019

Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure

Analysing how people react to rumours associated with news in social med...
research
06/04/2019

Joint Effects of Context and User History for Predicting Online Conversation Re-entries

As the online world continues its exponential growth, interpersonal comm...
research
06/01/2020

Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations

Automated ways to extract stance (denying vs. supporting opinions) from ...
research
10/24/2022

Twitter Users' Behavioral Response to Toxic Replies

Online toxic attacks, such as harassment, trolling, and hate speech have...
research
06/04/2021

Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions

Cannabis legalization has been welcomed by many U.S. states but its role...

Please sign up or login with your details

Forgot password? Click here to reset