Project info
Work package
- Work
Sustainability threat
- Spillovers
Challenge
- Reconfiguring-roles-and-relationships
Study info
Description of Study
In our paper, we delve into the dynamics of toxic interactions in online political discussions, specifically within a subreddit focused on a subculture of internet memes on Reddit. By analyzing a self-created dataset of over 2 million comments, we investigate how factors such as social identity and online anonymity contribute to the prevalence of incivility in comment and response chains online. Our findings reveal that cross-group interactions are more likely to be toxic compared to within-group interactions, particularly when users engage in toxic behavior to defend their in-group or prove their identity. Our study confirms hypotheses related to the likelihood of incivility stemming from out-group members and the amplification of toxicity in response to prior uncivil comments. Interestingly, our research also finds that the proximity to elections does not significantly increase the overall level of toxicity, despite a higher volume of comments during these periods. This work underscores the importance of understanding the mechanisms behind online toxicity to foster healthier online political discourse, as well as contributes to literature of online subcultures and social identity on the internet.
Study research question
Despite the prominent position of SIT in the theorizing of affective polarization, there is relatively little systematic empirical work testing to what extent SIT can also explain empirically observable patterns in online incivility. Using data from online political discussions, we answer whether social identity arguments play a role in how and when people are toxic online.
We use SIT adjacent concepts of in-group defense, identity salience and ideological distances, which are described below, along with the codified and timestamped comments and responses in online discussions. These give us the ability to observe granular contexts, clues and catalysts of toxicity, identifying conditions that trigger or amplify toxicity, and track how toxicity may become self-reinforcing.
Collection provenance
- Collected during project
- -
Collection methods
- Text Analysis
Personal data
Yes
External Source
Source description
Data between 1 August 2020 and 31 January 2021 on the PoliticalCompassMemes Subreddit. Data from comments on posts, as well as upvotes and downvotes are used. In addition, user flairs (political affiliations) and time day the comments are made.
File formats
- json
- .txt
Data types
- Structured
Languages
- English
Coverage start
Coverage end
01/08/2020
31/01/2021
Spatial coverage
Global (Predominantly US and European)
Collection period start
—
Collection period end
—
Variables
Unit
Unit description
Sample size
Sampling method
Individuals
Users of the specified subreddit
350000
API Access
Other
Dyadic comment-response pairs left by users on post on the relevant subreddit.
2 000 000
API Access and text analysis
Hypothesis
Theory
H1: If the previous comment was toxic, the more toxic a reply will be.
Social Identity Theory (Aggravating circumstances), Anonymity online
H2a: As the political distance grows between two interacting individuals, the more likely a reply to a comment will be toxic
Social Identity Theory (Ingroup-Outgroup), Affective Polarization
H2b: If the previous comment was toxic as political distance increases, the more likely a reply to the comment will be toxic.
Social Identity Theory (Ingroup-Outgroup & Aggravating circumstances), Affective Polarization
H3: One will more likely respond in a toxic manner if an uncivil comment was directed towards an individual of the same identity as the responder than another political identity .
Social Identity Theory (Norms and Expectations)
H4: The closer it is to an election, the more likely the comment will be uncivil in cross-group interactions.
Identity Salience, Common In-group
Variable type
Variable name
Variable description
Dependent variable
Toxicity
The toxicity of a piece of text (comments from users), quantified by Detoxify python library.
Independent variable
Political Affiliation - Left-Right
Where the user identifies on a political compass on the left-right axiom
Independent variable
Political Affiliation - Authoritarian-Libertarian
Where the user identifies on a political compass on the Auth-Lib axiom
Independent variable
Prior toxicity
The toxicity of a piece of text (comments from users) being replied to, quantified by Detoxify python library.
Dependent variable
Days away from election
The number of days before and after (standardized) the 2020 US elections, which took place on November 3rd 2020.
Independent variable
Interaction type
Characterizes a 3 comment-response chain depending on the partisan identity of the commenters. The in/out group are all in reference to the user leaving the comment of interest (i.e. ending in ingroup). Ingroup means similar to the person of interest replying, outgroup means different partisan identity. If there are two outgroup members in a chain, they are of the same identity.
Control variable
Is Poster
Binary indicator on whether the one replying is the author of the original post
Control variable
Net Votes
Upvotes minus downvotes on each comment
Dependent variable
Thread Ingroup
Binary indicator on whether the original poster shares the same political affiliation as the replier
Discipline-specific operationalizations
Conflict of interest
Data packages
Publications
Documents
Filename
Description
Date
Ethics
Ethical assessment
Yes
Ethical committee
EC- Sociology Groningen