User Taglines: Alternative Presentations of Expertise and Interest in Social Media

TL;DR

This paper introduces three novel methods for automatically generating concise expertise taglines for Twitter users, improving user profiling and presentation in social media applications.

Contribution

It proposes knowledge-enhanced algorithms for expertise summary generation and evaluates their effectiveness through user studies, outperforming existing methods.

Findings

Achieved 92.8% good summary quality in best case

Outperformed state-of-the-art methods by up to 88%

Demonstrated effective automatic expertise summarization for social media

Abstract

Web applications are increasingly showing recommended users from social media along with some descriptions, an attempt to show relevancy - why they are being shown. For example, Twitter search for a topical keyword shows expert twitterers on the side for 'whom to follow'. Google+ and Facebook also recommend users to follow or add to friend circle. Popular Internet newspaper- The Huffington Post shows Twitter influencers/ experts on the side of an article for authoritative relevant tweets. The state of the art shows user profile bios as summary for Twitter experts, but it has issues with length constraint imposed by user interface (UI) design, missing bio and sometimes funny profile bio. Alternatively, applications can use human generated user summary, but it will not scale. Therefore, we study the problem of automatic generation of informative expertise summary or taglines for Twitter experts in space constraint imposed by UI design. We propose three methods for expertise summary generation- Occupation-Pattern based, Link-Triangulation based and User-Classification based, with use of knowledge-enhanced computing approaches. We also propose methods for final summary selection for users with multiple candidates of generated summaries. We evaluate the proposed approaches by user-study using a number of experiments. Our results show promising quality of 92.8% good summaries with majority agreement in the best case and 70% with majority agreement in the worst case. Our approaches also outperform the state of the art up to 88%. This study has implications in the area of expert profiling, user presentation and application design for engaging user experience.