AI Insight
This study analyzed 1,470 Brazilian Instagram users to identify distinct behavioral profiles related to phubbing (snubbing others by using one's phone). Using machine learning on personality traits, problematic Instagram use, and phubbing behaviors, researchers identified five overlapping profiles ranging from low digital engagement to digitally vulnerable users. The findings demonstrate that phubbing occurs within heterogeneous personality and digital use patterns rather than representing a single psychological type.
Why it matters
Understanding the diverse profiles of phubbing behavior could inform more targeted interventions for problematic phone use and social media engagement. The results suggest that one-size-fits-all approaches may be less effective than tailored strategies that account for individual differences in personality and digital behavior patterns.
Understand the Science
Phubbing is a heterogeneous digital behavior that may occur within different configurations of personality and problematic social media use. This study identified multivariate profiles of Brazilian adults on the basis of Big Five personality traits, problematic Instagram use, and the communication disturbance and phone obsession dimensions of phubbing. The initial sample included 1,551 adults; the primary analysis included 1,470 current Instagram users. Eight standardized indicators were analyzed using Gaussian Mixture Models with one to six components and alternative covariance structures. Model selection prioritized the Bayesian information criterion (BIC), with the Akaike information criterion (AIC) used as complementary evidence. The five-component diagonal model had the lowest BIC, whereas the AIC favored a six-component diagonal solution. The BIC-preferred model identified five partially overlapping profiles: Regulated Low-Digital (16.9%), Introverted Low-Digital (15.6%), Moderate Digital (21.1%), Socially Engaged Digital (20.8%), and Digitally Vulnerable (25.6%). The average maximum posterior membership probability was 0.803, indicating meaningful classification uncertainty. The profiles differed most strongly in problematic Instagram use, Phone Obsession, Communication Disturbance, Conscientiousness, and Extraversion. The findings indicate that phubbing-related behaviors occur within heterogeneous personality-digital configurations rather than a single psychological pattern. The profiles should be interpreted as probabilistic and descriptive, not as fixed psychological types or diagnostic categories. Longitudinal and independent replication studies are needed to examine their stability and practical relevance.
Source: Profiling phubbing practitioners using machine learning