MétaCan
Menu
Back to cohort
Record W2896682018 · doi:10.1002/jcpy.1073

How Readability Shapes Social Media Engagement

2018· article· en· W2896682018 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Consumer Psychology · 2018
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsYork UniversityUniversité du Québec en Outaouais
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReadabilityFluencyProcessing fluencySocial mediaValence (chemistry)PsychologyContext (archaeology)Social psychologyCognitive psychologyAdvertisingComputer scienceWorld Wide WebMathematics education

Abstract

fetched live from OpenAlex

We suggest that text readability plays an important role in driving consumer engagement on social media. Consistent with a processing fluency account, we find that easy‐to‐read posts are more liked, commented on, and shared on social media. We analyze over 4,000 Facebook posts from Humans of New York , a popular photography blog on social media, over a 3‐year period to see how readability shapes social media engagement. The results hold when controlling for photo features, story valence, and other content‐related characteristics. Experimental findings further demonstrate the causal impact of readability and the processing fluency mechanism in the context of a fictitious brand community. This research articulates the impact of processing fluency on brief word‐of‐mouth transmissions in the real world while empirically demonstrating that readability as a message feature matters. It also extends the impact of processing fluency to a novel behavioral outcome: commenting and sharing actions.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.074
GPT teacher head0.356
Teacher spread0.282 · 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