Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence

• URL: http://arxiv.org/abs/2110.12569v1
• Date: Mon, 25 Oct 2021 01:05:49 GMT
• Title: Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence
• Authors: Rohit Ram and Marian-Andrei Rizoiu
• Abstract summary: Social influence pervades our everyday lives and lays the foundation for complex social phenomena. Existing literature studying online social influence suffers from several drawbacks. This work bridges the gap and presents three contributions towards modeling and empirically quantifying online influence.
• Score: 9.556358888163983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social influence pervades our everyday lives and lays the foundation for complex social phenomena. In a crisis like the COVID-19 pandemic, social influence can determine whether life-saving information is adopted. Existing literature studying online social influence suffers from several drawbacks. First, a disconnect appears between psychology approaches, which are generally performed and tested in controlled lab experiments, and the quantitative methods, which are usually data-driven and rely on network and event analysis. The former are slow, expensive to deploy, and typically do not generalize well to topical issues (such as an ongoing pandemic); the latter often oversimplify the complexities of social influence and ignore psychosocial literature. This work bridges this gap and presents three contributions towards modeling and empirically quantifying online influence. The first contribution is a data-driven Generalized Influence Model that incorporates two novel psychosocial-inspired mechanisms: the conductance of the diffusion network and the social capital distribution. The second contribution is a framework to empirically rank users' social influence using a human-in-the-loop active learning method combined with crowdsourced pairwise influence comparisons. We build a human-labeled ground truth, calibrate our generalized influence model and perform a large-scale evaluation of influence. We find that our generalized model outperforms the current state-of-the-art approaches and corrects the inherent biases introduced by the widely used follower count. As the third contribution, we apply the influence model to discussions around COVID-19. We quantify users' influence, and we tabulate it against their professions. We find that the executives, media, and military are more influential than pandemic-related experts such as life scientists and healthcare professionals.

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