Finding Topical Experts in Twitter via Query-dependent Personalized PageRank
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
- Qatar Computing Research Institute
Our approach combines traditional link analysis with text mining. It relies on crowd-sourced data from Twitter lists to build a labeled directed graph called the endorsement graph, which captures topical expertise as perceived by users. Given a text query, our algorithm uses a dynamic topic-sensitive weighting scheme, which sets the weights on the edges of the graph. Then, it uses an improved version of query-dependent PageRank to find important nodes in the graph, which correspond to topical experts. In addition, we address the scalability and performance issues posed by large social networks by pruning the input graph via a focused-crawling algorithm.
Extensive evaluation on a number of different topics demonstrates that the proposed approach significantly improves on query-dependent PageRank, outperforms the current publicly-known state-of-the-art methods, and is competitive with Twitter's own search system, while using less than 0.05% of all Twitter accounts.
|Title of host publication||Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017|
|Publication status||Published - 2017|
|MoE publication type||A4 Article in a conference publication|
|Event||IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - Sydney, Australia|
Duration: 31 Jul 2017 → 3 Aug 2017
|Conference||IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining|
|Period||31/07/2017 → 03/08/2017|