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Record W3085627079 · doi:10.1177/1461444820958123

Upvoting extremism: Collective identity formation and the extreme right on Reddit

2020· article· en· W3085627079 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Media & Society · 2020
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsExtreme rightMainstreamIdentity (music)VotingCollective identitySociologySocial mediaThematic analysisMedia studiesPublic relationsSocial identity theoryInternet privacyPolitical sciencePoliticsSocial groupLawComputer scienceQualitative researchSocial scienceAesthetics

Abstract

fetched live from OpenAlex

Since the advent of the Internet, right-wing extremists and those who subscribe to extreme right views have exploited online platforms to build a collective identity among the like-minded. Research in this area has largely focused on extremists’ use of websites, forums, and mainstream social media sites, but overlooked in this research has been an exploration of the popular social news aggregation site Reddit. The current study explores the role of Reddit’s unique voting algorithm in facilitating “othering” discourse and, by extension, collective identity formation among members of a notoriously hateful subreddit community, r/The_Donald. The results of the thematic analysis indicate that those who post extreme-right content on r/The_Donald use Reddit’s voting algorithm as a tool to mobilize like-minded members by promoting extreme discourses against two prominent out-groups: Muslims and the Left. Overall, r/The_Donald’s “sense of community” facilitates identity work among its members by creating an environment wherein extreme right views are continuously validated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.222
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it