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Record W3212269243 · doi:10.1111/grow.12573

What drives people to repost social media messages during the COVID‐19 pandemic? Evidence from the Weibo news microblog

2021· article· en· W3212269243 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

VenueGrowth and Change · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Victoria
FundersFundamental Research Funds for the Central UniversitiesSocial Science Foundation of Jiangsu Province
KeywordsMicrobloggingSocial mediaPandemicCoronavirus disease 2019 (COVID-19)HeuristicInformation DisseminationInternet privacyComputer scienceData scienceWorld Wide WebArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

COVID-19 poses an unprecedented challenge to human society. To cope with the pandemic, people seek information from various communication channels. Microblog websites are highly influential information channels for the public to get timely information during the pandemic. Building on the heuristic-systematic processing model, this study identifies the multiple characteristics (content, author, and social features) that may play a role in triggering long cascades of reposts of COVID-19-related news microblogs. With a large-scale news microblog database collected from Weibo and an innovative information gain method, we find that heuristic thinking plays a dominant role in COVID-19 pandemic-related news microblog reposting decisions and further discloses the specific influencing factors of such behavior.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.105
GPT teacher head0.330
Teacher spread0.224 · 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