Abstract
Social community question answering (SCQA) sites not only provide regular question answering (QA) service but also form a social network where users can follow each other. Identifying topical opinion leaders who are both expert and influential in SCQA becomes a hot research topic. However, existing works focus on either using knowledge expertise to find experts for improving the quality of answers, or measuring user influence to identify influential ones. In this paper, we propose QALeaderRank, a topical opinion leader identification framework, incorporating both the topic-sensitive influence and the topical knowledge expertise. To measure a user’s topic-sensitive influence, we design a novel ranking algorithm that exploits both the social and QA features of SCQA, taking account of the network structure, topical similarity and knowledge authority. Besides, we incorporate three topic-relevant metrics to infer the topical expertise. Extensive experiments along with a user study demonstrate that QALeaderRank outperforms the compared state-of-the-art methods. QALeaderRank can also be used to identify multi-topic opinion leaders.
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Notes
- 1.
Opinion leaders give influential comments and opinions, put forward guiding ideas, agitate and guide the public to understand social problems [12].
- 2.
Besides 6 child topics of the root topic, we select another representative topic (“Science & Technology”) that had not been edited into the topic structure due to some mistakes from Zhihu topic organization.
- 3.
The t-test result depends on the extent of the dataset normality. Skewness and kurtosis of these two samples are 1.19, 2.14 and 1.21, 2.09, which are considered acceptable in order to prove normal distribution [7].
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Zhao, T., Huang, H., Fu, X. (2018). Identifying Topical Opinion Leaders in Social Community Question Answering. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_25
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