Incentivizing social media users for mobile crowdsourcing.

We focus on the problem of contributor-task matching in mobile crowd-sourcing. The idea is to identify existing social media users who possess domain expertise (e.g., photography) and incentivize them to perform some tasks (e.g., take quality pictures). To this end, we propose a framework that extracts the potential contributors' expertise based on their social media activity and determines incentives for them within the constraint of a budget. This framework does so by preferentially targeting contributors who are likely to offer quality content. We evaluate our framework on Flickr data for the entire city of Barcelona and show that it ensures high levels of task quality and wide geographic coverage, all without compromising fairness. HighlightsWe aim to engage users for mobile crowdsourcing from existing online communities.We study the joint task-to-crowdworker assignment and budget allocation problem.We propose a general data-driven and quality-aware incentive framework.Evaluation with user profile information achieves high quality of accomplished tasks.Results show good coverage avoiding discrimination against less popular tasks.