CrowdSTAR: A Social Task Routing Framework for Online Communities

TL;DR

CrowdSTAR is a framework that efficiently routes tasks within online communities by leveraging topic expertise and social features, demonstrated through question-answering experiments on Twitter and Quora.

Contribution

It introduces a novel routing algorithm that balances knowledge and availability to optimize task dispatch in diverse online crowds.

Findings

Effective task routing improves answer quality and response time.

Framework successfully applied to Twitter and Quora for question answering.

Routing algorithm adapts to dynamic expertise and social availability.

Abstract

The online communities available on the Web have shown to be significantly interactive and capable of collectively solving difficult tasks. Nevertheless, it is still a challenge to decide how a task should be dispatched through the network due to the high diversity of the communities and the dynamically changing expertise and social availability of their members. We introduce CrowdSTAR, a framework designed to route tasks across and within online crowds. CrowdSTAR indexes the topic-specific expertise and social features of the crowd contributors and then uses a routing algorithm, which suggests the best sources to ask based on the knowledge vs. availability trade-offs. We experimented with the proposed framework for question and answering scenarios by using two popular social networks as crowd candidates: Twitter and Quora.