Thread-based probabilistic models for expert finding in enterprise Microblogs

Abstract

Within the enterprise important conversations are shifting to Social Media Microblogs thus providing a new source for the acquisition of tacit knowledge useful for intelligent agents. While automated retrieval of candidate experts from Web documents has been well-researched, little exists that leverages social aspects of Microblogs for this purpose. We show that the experts and their related expertise can also be identified in the enterprise corpus. Analysis reveals the existence of problem-solving threads where the requestor seeking help often elicits responses from experts which consequently records tacit (experiential) knowledge. Here we present rules to automate the acquisition of this tacit knowledge. The rules are based on probabilistic models enhanced with linguistics to exploit the role patterns in threads. Heuristics also strengthen local evidence about associations between candidates, documents and (expertise) terms. Applying this, we demonstrate that the Enhanced Models can ameliorate the negative impact of Microblogs (such as sparse data, short content, and implicit associations). The experiments show that the candidate models significantly outperform the document models in the Microblog environment. This is different from previous research in Web environments. Examples also illustrate underlying insights and emphasize the ‘additive’ nature of expertise found in Social Media.