OpenCrowd: A Human-Al Collaborative Approach for Finding Social Influencers via Open-Ended Answers Aggregation

Finding social influencers is a fundamental task in many online applications ranging from brand marketing to opinion mining. Existing methods heavily rely on the availability of expert labels, whose collection is usually a laborious process even for domain experts. Using open-ended questions, crowdsourcing provides a cost-effective way to find a large number of social influencers in a short time. Individual crowd workers, however, only possess fragmented knowledge that is often of low quality. To tackle those issues, we present OpenCrowd, a unified Bayesian framework that seamlessly incorporates machine learning and crowdsourcing for effectively finding social influencers. To infer a set of influencers, OpenCrowd bootstraps the learning process using a small number of expert labels and then jointly learns a feature-based answer quality model and the reliability of the workers. Model parameters and worker reliability are updated iteratively, allowing their learning processes to benefit from each other until an agreement on the quality of the answers is reached. We derive a principled optimization algorithm based on variational inference with efficient updating rules for learning OpenCrowd parameters. Experimental results on finding social influencers in different domains show that our approach substantially improves the state of the art by 11.5% AUC. Moreover, we empirically show that our approach is particularly useful in finding micro-influencers, who are very directly engaged with smaller audiences.