Human interactions are mediated by social influence. During crises like the COVID-19 pandemic, social influence determines whether life-saving information is adopted or immunization campaigns meet their targets. The literature on online social influence presents notable limitations across disciplines. Psychosocial approaches characterize the nature of influence by measuring how social factors impact these phenomena, but lack computational modeling capabilities and rely on slow, non-scalable measurement methods. Conversely, computational approaches, while data-driven, often fail to incorporate critical social factors. Our work bridges this gap through two main contributions. First, we present a data-driven Generalized Influence Model (GIM) incorporating two novel psychosocial-inspired mechanisms: the conductance of the diffusion network and the influence-capital distribution. GIM 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 by applying GIM to COVID-19 discussions. 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.