This paper is published in Volume-4, Issue-1, 2018

Area
Social Network Analysis
Author
Jenifa Anna Agness
Org/Univ
Sathyabama University, India
Pub. Date
January 12, 2018
Paper ID
V4I1-1189
Publisher
Keywords
Twitter Lists, Endorsement Graph, Query-dependent PageRank, Focused Crawling, and Topical Experts

Citations

IEEE
Jenifa Anna Agness. Utilizing Twitter Lists to Find Topical Authorities in Twitter using a Combined Approach of Focused Crawling and Query-Dependent PageRank, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Jenifa Anna Agness (2018). Utilizing Twitter Lists to Find Topical Authorities in Twitter using a Combined Approach of Focused Crawling and Query-Dependent PageRank. International Journal of Advance Research, Ideas and Innovations in Technology, 4(1) www.IJARIIT.com.

MLA
Jenifa Anna Agness. "Utilizing Twitter Lists to Find Topical Authorities in Twitter using a Combined Approach of Focused Crawling and Query-Dependent PageRank." International Journal of Advance Research, Ideas and Innovations in Technology 4.1 (2018). www.IJARIIT.com.

Abstract

Discovering topical experts in micro-blogging sites (such as Twitter) is a vital information-seeking task. Here, we explain an expert-finding algorithm for Twitter which combines traditional link analysis with text mining. It relies on Twitter lists (crowd-sourced data) to build a labeled directed graph called the endorsement graph. Given a text query, this algorithm dynamically sets the weights on the edges of the graph followed by applying an improved version of query-dependent PageRank to find important nodes in the graph, which correspond to topical experts. Scalability and performance issues posed by large social networks is addressed by pruning the input graph via a focused-crawling algorithm. This approach is evaluated with a seed set of inputs to show this is competitive with Twitter’s own search system (WTF) while using less than 0.05% of all Twitter accounts.
Paper PDF