Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

Mukherjee, Sourav; Widmark, David; DiMascio, Vince; Oates, Tim

doi:10.1109/icdmw53433.2021.00085

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…We validated the classifier on 100 labeled users, where ground-truth labels were derived by two annotators, with disagreement resolved by discussion. The classifier has mean macro-F1 of 0.54, comparable to the classification performance reported by literature 42 .…”

Section: Influence Of Occupations During Covidsupporting

confidence: 80%

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

Ram

Rizoiu

2022

Preprint

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Social influence pervades our everyday lives and lays the foundation for complex social phenomena, such as the spread of misinformation and the polarization of communities. In a crisis like the COVID-19 pandemic, social influence can determine whether life-saving information is adopted, public health measures are observed, or immunization campaigns meet their targets. 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 begins to bridge 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 influence-capital distribution. The second contribution is a framework to empirically rank users' social influence using a human-in-the-loop 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 content veracity, tabulating it against their professions. We find that executives, media, and the military are more influential 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.

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Section: Influence Of Occupations During Covidsupporting

confidence: 80%