TY  - JOUR
AB  - Human interactions are mediated by social influence. During crises like the COVID-19 pandemic, social influence can be critical in determining whether life-saving information is adopted, public health measures are observed, or immunization campaigns meet their targets. The literature on online social influence presents notable limitations across disciplines. Psychosocial approaches effectively characterize the nature of influence by measuring how social factors impact these phenomena, but they lack computational modeling capabilities and rely on slow, non-scalable measurement methods that struggle to generalize to current issues. Conversely, computational approaches, while data-driven and based on network and event analysis, often fail to incorporate the critical social factors that underlie these phenomena. Our work bridges this gap through two main contributions that integrate the strengths of both approaches. First, we present a data-driven Generalized Influence Model that incorporates two novel psychosocial-inspired mechanisms: the conductance of the diffusion network and the influence-capital distribution. This model not only outperforms existing state-of-the-art approaches but also corrects the inherent biases introduced by the widely used follower count metric. Second, we empirically test long-held sociological hypotheses regarding influence, social class, and expertise in the online domain by applying our influence model to discussions around COVID-19. We quantify the influence and content veracity for more than 21.5 million X/Twitter users in relation to their professions. Our model suggests that executives, media, and military figures exert greater influence than pandemic-related experts such as life scientists and healthcare professionals. Worryingly, by leveraging existing COVID-19 misinformation datasets, we show that some of the most influential occupations also spread the most misinformation. These findings raise questions about the effectiveness of information dissemination by experts in situations of crisis.
AU  - Ram, R
AU  - Rizoiu, M-A
DA  - 2026/04/17
DO  - 10.1140/epjds/s13688-026-00650-5
JO  - EPJ DATA SCIENCE
PB  - SPRINGER
PY  - 2026/04/17
TI  - Conductance and influence-capital: modeling online social influence
VL  - 15
Y1  - 2026/04/17
Y2  - 2026/06/27
ER  -