Analizying Factors to Increase the Influence of a Twitter User

被引:0
作者
del Campo-Avila, Jose [1 ]
Moreno-Vergara, Nathalie [1 ]
Trella-Lopez, Monica [1 ]
机构
[1] Univ Malaga, Dpto Lenguajes & Ciencias Computac, E-29071 Malaga, Spain
来源
HIGHLIGHTS IN PRACTICAL APPLICATIONS OF AGENTS AND MULTIAGENT SYSTEMS | 2011年 / 89卷
关键词
data mining; social networks; Twitter; influence;
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
Twitter is a free social media service that allows anyone to say almost anything to anybody in 140 characters or less. Over the past few months, Twitter has experienced an explosive growth which has aroused the interest of many developers. In consequence, there have appeared many analytic tools which, besides other characteristics, calculate how influential a user is. This meaningful value can be estimated using different metrics and tools. In this paper, we study the reliability of them and show how data mining techniques can help: (a) to identify the actions which can increase the influence of a user (depending on the concrete tool), (b) to discover if those actions are related to different tools (and whether we can increase influence in more than one way), and (c) to advise people (or companies) about how they can get a greater impact.
引用
收藏
页码:69 / 76
页数:8
相关论文
共 8 条
[1]  
Cha M., 2010, MEASURING USER INFLU
[2]  
Kempe D., 2003, Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, P137
[3]   Extracting influential nodes on a social network for information diffusion [J].
Kimura, Masahiro ;
Saito, Kazumi ;
Nakano, Ryohei ;
Motoda, Hiroshi .
DATA MINING AND KNOWLEDGE DISCOVERY, 2010, 20 (01) :70-97
[4]  
Kononenko I., 2007, Machine Learning and Data Mining: Introduction to Principles and Algorithms, DOI DOI 10.1533/9780857099440
[5]  
Leavitt Alex., 2009, INFLUENTIALS NEW APP
[6]  
Quinlan J. R., 1992, Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. AI '92, P343
[7]  
Wang Y., 1997, LNCS, V1224
[8]  
Witten I. H., 2005, DATA MINING, V2, P403