Evaluating the Trustworthiness of User-Generated Content on Social Media

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.