  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.
