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Record W4387867087 · doi:10.1002/cb.2269

Social media marketing content strategy: A comprehensive framework and empirically supported guidelines for brand posts on Facebook pages

2023· article· en· W4387867087 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

VenueJournal of Consumer Behaviour · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsInteractivitySocial mediaAdvertisingUser engagementBusinessSPARK (programming language)Transformational leadershipSocial media marketingProduct (mathematics)PsychologyDigital marketingComputer scienceWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

Abstract Despite all the marketing power social media marketing has, a major challenge it faces is how to create meaningful content that ignites a spark with audiences. The purpose of this research is to examine in what way brands can produce compelling social media content to engage and connect with target audiences. Eighteen brand Facebook Pages from nine major industries were reviewed to identify characteristics of content associated with higher levels of user engagement. Results show that multimedia content, transformational appeal, low levels of interactivity, and endorser type influence user engagement with brand posts on Facebook. Posts made on weekdays demonstrate higher levels of positive reactions than posts made during the weekends. In addition, consumer engagement is higher for service‐ (vs. product‐based) Facebook brand posts. Furthermore, the length of the message proved to play a key role in prompting users to share a social media post, in that longer posts were more likely to be shared than shorter ones.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.297
GPT teacher head0.434
Teacher spread0.137 · 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