Evaluating the Trustworthiness of User-Generated Content on Social Media
Bibliographic record
Abstract
Despite the extensive research on digital consumer engagement, the dimension of trust in user-generated content (UGC) on social media has been relatively less studied. This study examines the psychological and content-based factors that affect the trustworthiness of UGC on a food brand’s Instagram page, based on data analysis spanning seven years. Employing natural language processing (NLP) techniques, we identified key marketing aspects in both UGC and promotional texts. Specifically, BERT (Bidirectional Encoder Representations from Transformers) was utilized for sentiment analysis, while RoBERTa (A Robustly Optimized BERT Pretraining Approach) was employed for analyzing promotional texts. Through these analytic techniques, we were able to discern subtle nuances in how UGC is perceived and its direct impact on the perceived trustworthiness of the content itself. However, a divergence was observed in the focus areas. Promotional texts leaned towards product features, while UGC highlighted personal experiences and emotional connections. This underscores the importance of brand authenticity and emotional engagement for building trust through UGC. Furthermore, a hierarchical framework of consumer motivations was derived from the UGC through the analysis of promotional messages and the integration of Aristotle’s rhetorical appeals with Trust Theory. This framework reveals a complex interplay between reflective, formative, and overall motives behind consumer engagement.
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.
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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".