Three Essays on Understanding Consumer Engagement with Brand Posts on Social Media
Bibliographic record
Abstract
The rapid proliferation of social media over the last ten years has revolutionized the way that brands and consumers connect, communicate, and interact with each other.Nowadays, approaching creative brand posts in a way that maximizes consumer engagement has become a growing challenge to social media marketers.This thesis addresses this challenge and aims to understand how to enhance consumer engagement with brand posts on social media through the lens of linguistics.To achieve this goal, three independent but related papers were conducted to 1) investigate how the linguistic styles of brand social media posts influence consumer engagement; 2) examine the interactive impacts of consumer comment valence and brand response language style on consumers' evaluation of brand and their intention for future engagement with the brand; and 3) explore whether and how incorporating emojis in brand social media posts affects consumer engagement with the posts.The findings of this thesis reveal that, for brands, using proper language when communicating with consumers on social media can improve consumers' perceptions of brands and enhance their engagement with brands.The results of this thesis deepen our understanding of the role that brand language plays in influencing consumer engagement on social media as well as provide many practical guidelines for social media marketers regarding the language they use in communicating with consumers on social media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".