Discovering similar Twitter accounts using semantics

被引:16
作者
Razis, Gerasimos [1 ]
Anagnostopoulos, Ioannis [1 ]
机构
[1] Univ Thessaly, Dept Comp Sci & Biomed Informat, Lamia 35100, Greece
关键词
Similarity network; Social semantics; Twitter entities;
D O I
10.1016/j.engappai.2016.01.015
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be. Towards this direction, we introduce a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of Twitter entities used. The methodology is based exclusively on semantic representation protocols and related technologies. An ontological schema is also described towards the semantification of the Twitter accounts and their entities. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:37 / 49
页数:13
相关论文
共 25 条
[11]  
Cha M, 2010, MEASURING USER INFLU
[12]  
CHEN JL, 2010, SHORT TWEET EXPT REC, P1185
[13]  
Cheng Z., 2010, P 19 ACM INT C INF K, P759
[14]  
Dan Brickley, 2014, FOAF VOCABULARY SPEC
[15]   An index to quantify an individual's scientific research output [J].
Hirsch, JE .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2005, 102 (46) :16569-16572
[16]  
Joshua Shinavier, 2010, LINKED DATA WEB
[17]   Twitter and the health reforms in the English National Health Service [J].
King, Dominic ;
Ramirez-Cano, Daniel ;
Greaves, Felix ;
Vlaev, Ivo ;
Beales, Steve ;
Darzi, Ara .
HEALTH POLICY, 2013, 110 (2-3) :291-297
[18]  
Naveed Nasir, 2011, BAD NEWS TRAVEL FAST, P8
[19]  
Phelan O., 2011, TERMS FEATHER CONTEN, P448
[20]  
Razis G., 2014, INFLUENCETRACKER RAT, P184