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Twitter Frames

2013· book-chapter· en· W2480451289 on OpenAlexaff
Aziz Douai, Mohamed Ben Moussa

Bibliographic record

VenueAdvances in human and social aspects of technology book series · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsMcGill UniversityOntario Tech University
Fundersnot available
KeywordsFraming (construction)Social mediaProfiling (computer programming)DemocracyPolitical scienceMedia studiesSociologyComputer scienceGeographyLawPolitics

Abstract

fetched live from OpenAlex

This chapter reports preliminary findings from a larger investigation of the role of social media and communication technologies in the “Arab Democracy Spring.” The goal of the study is to analyze how Egyptian activists used Twitter during the 2011 protests. This stage of the project specifically outlines ways of identifying and classifying some of the most influential Egyptian Twitter users during these events. In addition to profiling the “influentials,” this study applies a framing perspective to understanding Twitter’s use among Egyptian activists.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.165
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0040.001
Scholarly communication0.0070.007
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1650.059

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 head0.015
GPT teacher head0.298
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations3
Published2013
Admission routes1
Has abstractyes

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