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Record W4405178022 · doi:10.52922/ti77734

Exposure to and sharing of fringe or radical content online

2024· book· en· W4405178022 on OpenAlexaboutno aff
Timothy Cubitt, Anthony Morgan

Bibliographic record

VenueAustralian Institute of Criminology eBooks · 2024
Typebook
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamContent (measure theory)Quarter (Canadian coin)The InternetInternet privacyAdvertisingPsychologyComputer scienceWorld Wide WebBusinessPolitical scienceGeographyMathematics

Abstract

fetched live from OpenAlex

Using a large, national survey of online Australians, we measured unintentional and intentional exposure to fringe or radical content and groups online. Two in five respondents (40.6%) reported being exposed to material they described as fringe, unorthodox or radical. One-quarter of these respondents (23.2%) accessed the content intentionally. One-third (29.9%) said the content they had seen depicted violence. Fringe or radical content was often accessed through messages, discussions and posts online. Mainstream social media and messaging platforms were the platforms most frequently used to share fringe or radical content. Being a member of a group promoting fringe or radical content was associated with increased sharing of that content with other internet users. Efforts to restrict access to radical content and groups online, especially on mainstream platforms, may help reduce intentional and unintentional exposure to and sharing of that content.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.150
GPT teacher head0.298
Teacher spread0.147 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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