Geo-location Identification of Facebook Pages

被引：0

作者：

Lin, Yu-Cheng ^{[1
]}

Lai, Chun-Ming ^{[1
]}

Chapman, Jon William ^{[1
]}

Wu, S. Felix ^{[1
]}

Barnett, George A. ^{[1
]}

机构：

[1] Univ Calif Davis, Davis, CA 95616 USA

来源：

2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Online Social Network (OSN) communities serve as different platforms for multiple users' interaction - people behaving diversely among distinctive communities - such as entertainment, global and local discussion communities. However, attribute identification among online discussion communities remain largely unexplored. In this paper, we describe and analyze the geo-location property of large-scale Facebook public pages (15M pages). We propose a framework utilizing the connectivity of the page-like graph to predict the missing geo-location information based on Breadth-First Search (BFS). Our method achieves a satisfyingly high accuracy (89%) on identifying the state location attribute of unknown United States (US) pages. Our empirical results offer a better understanding of regional social analysis and target audience broadcasting.

引用

页码：441 / 446

页数：6

共 20 条

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