Identifying the Topic-Specific Influential Users in Twitter

Social Influence can be described as the ability to have an effect on the thoughts or actions of others. Influential members in online communities are becoming the new media to market products and sway opinions. Also, their guidance and recommendations can save some people the search time and assist their selective decision making. The objective of this research is to detect the influential users in a specific topic on Twitter. From a collection of tweets matching a specified query, the influential users are to be detected in an online fashion. In order to address this, the issue of which set of features can best lead us to the topic-specific influential users is investigated along with how these features can be expressed in a model to produce a list of ranked influential users.

[1]  Isabel Anger,et al.  Measuring influence on Twitter , 2011, i-KNOW '11.

[2]  Taher H. Haveliwala Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[3]  Junying Yu,et al.  Notice of RetractionTypology and influence analysis of opinion leader: A case study on fashion online shopping , 2011, 2011 International Conference on E-Business and E-Government (ICEE).

[4]  Panayiotis Bozanis,et al.  Identifying the Productive and Influential Bloggers in a Community , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Will Webberley,et al.  Retweeting: A study of message-forwarding in twitter , 2011, 2011 Workshop on Mobile and Online Social Networks.

[6]  Xue Li,et al.  Notice of RetractionThe topology analyze of blogosphere through social network method , 2011, 2011 Seventh International Conference on Natural Computation.

[7]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[8]  Amit P. Sheth,et al.  Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter , 2011 .

[9]  R. Rosenfeld,et al.  Two decades of statistical language modeling: where do we go from here? , 2000, Proceedings of the IEEE.

[10]  Virgílio A. F. Almeida,et al.  Detecting Evangelists and Detractors on Twitter , 2010 .

[11]  Freimut Bodendorf,et al.  Detecting opinion leaders and trends in online social networks , 2009, CIKM-SWSM.

[12]  Philip S. Yu,et al.  Identifying the influential bloggers in a community , 2008, WSDM '08.

[13]  Dhiraj Murthy,et al.  Understanding Cancer-Based Networks in Twitter Using Social Network Analysis , 2011, 2011 IEEE Fifth International Conference on Semantic Computing.

[14]  Daniel W. Drezner,et al.  The power and politics of blogs , 2007 .

[15]  Qinghua Zhu,et al.  Study on the Impacts of Opinion Leader in Online Consuming Decision , 2011, 2011 International Joint Conference on Service Sciences.

[16]  Mor Naaman,et al.  Is it really about me?: message content in social awareness streams , 2010, CSCW '10.

[17]  A. Faisal,et al.  Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users , 2013, PloS one.

[18]  N. Kokash An introduction to heuristic algorithms , 2005 .

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Panayiotis Bozanis,et al.  Identifying Influential Bloggers: Time Does Matter , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[21]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[22]  Ahmed Rafea,et al.  IDENTIFYING THE TOPIC-SPECIFIC INFLUENTIAL USERS AND OPINION LEADERS IN TWITTER , 2013 .

[23]  Chen Jing-min,et al.  The social network analysis of political blogs in people: Based on centrality , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).

[24]  Susan T. Dumais,et al.  Mark my words!: linguistic style accommodation in social media , 2011, WWW.

[25]  Junlan Feng,et al.  Measuring User Influence on Twitter Using Modified K-Shell Decomposition , 2011, The Social Mobile Web.

[26]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[27]  Sun Wen-jun,et al.  A social network analysis on Blogospheres , 2008, 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings.

[28]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[29]  Daniel Dajun Zeng,et al.  Finding leaders from opinion networks , 2009, 2009 IEEE International Conference on Intelligence and Security Informatics.

[30]  Emre Kiciman,et al.  Language Differences and Metadata Features on Twitter , 2010 .

[31]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[32]  Alex Baron,et al.  Using Signals from Text to Identify Roles within a Group , 2012, 2012 IEEE Sixth International Conference on Semantic Computing.

[33]  Andreas Stolcke,et al.  SRILM at Sixteen: Update and Outlook , 2011 .

[34]  J. Berry The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy , 2003 .

[35]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[36]  Yuval Shavitt,et al.  A model of Internet topology using k-shell decomposition , 2007, Proceedings of the National Academy of Sciences.

[37]  Jie Tang,et al.  Detecting Community Kernels in Large Social Networks , 2011, 2011 IEEE 11th International Conference on Data Mining.

[38]  Lei Peng,et al.  Evaluating User Influence Based on the Properties of User in Social Networks , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[39]  Devin Gaffney Algorithmic transparency and the Klout score , 2012 .

[40]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[41]  Daniel M. Romero,et al.  Influence and Passivity in Social Media , 2011, ECML/PKDD.

[42]  Zheng Li,et al.  Measuring User Influence Based on Multiple Metrics on YouTube , 2015, 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP).

[43]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[44]  Daniele Quercia,et al.  In the Mood for Being Influential on Twitter , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[45]  José del Campo-Ávila,et al.  Analizying Factors to Increase the Influence of a Twitter User , 2011, PAAMS.

[46]  F. Jelinek,et al.  Perplexity—a measure of the difficulty of speech recognition tasks , 1977 .

[47]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[48]  Daniel M. Romero,et al.  Influence and passivity in social media , 2010, ECML/PKDD.