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Record W4365935217 · doi:10.47611/jsrhs.v11i4.3600

Getting a Feel of Instagram Reels: The Effects of Posting Format on Online Engagement

2022· article· en· W4365935217 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 Student Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsConestoga College
Fundersnot available
KeywordsComputer scienceSocial mediaScope (computer science)Content (measure theory)User-generated contentAdvertisingMultimediaInternet privacyWorld Wide WebBusinessMathematics

Abstract

fetched live from OpenAlex

This study analyzed the effects of posting format on an Instagram post’s engagement. Posting formats, including pictures, videos, and Reels, are different ways of sharing content on Instagram. Prior research shows that short-form content has grown in recent years, spurring formats such as Instagram Reels. Nevertheless, a comparison of short-form content and traditional posting formats had not yet been observed. Quantitative data was collected through the content analysis method, which analyzed Instagram posts from small jewelry business accounts. Reels were found to receive the highest average engagement, allowing posts to gain more likes and comments than pictures and videos. However, limitations included the presence of outliers and the narrow scope of studied accounts. By utilizing these findings, struggling business accounts on Instagram may be able to increase the likes and comments of their posts more efficiently, ultimately increasing the chances of their success on the platform.

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)0.018
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.166
GPT teacher head0.488
Teacher spread0.322 · 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