Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
The Economic Leverage of the Virtual Community
International Journal of Electronic Commerce
Identifying opinion leaders in the blogosphere
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Herd behavior in purchasing books online
Computers in Human Behavior
Social impact in technologically-mediated communication: An examination of online influence
Computers in Human Behavior
Communication goals and online persuasion: An empirical examination
Computers in Human Behavior
New Product Diffusion with Influentials and Imitators
Marketing Science
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Computational measures for language similarity across time in online communities
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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While many explanations of influence have been proposed there is still debate over which is correct even though most are supported by empirical evidence. This uncertainty has been attributed to there being too little evidence of real-world influence networks, and an inability to separate influence from cognitive similarity, that is, a pre-existing like-mindedness, attitude or way of thinking shared among participants. This paper proposes theme resonance, a new metric for measuring both influence and cognitive similarity between and among participants in the same online conversation. Theme resonance is derived from two textual content analysis systems: Centering Resonance Analysis and qualitative thematic modeling. The use of theme resonance is demonstrated by constructing influence networks using online conversations in ten weblogs, allowing the propagation of new conversational themes to be traced from initiator though subsequent propagators. A method of separating influence from like-mindedness is also demonstrated. Depending on the metric chosen influence and its susceptibility were found both to be opposite ends of the same spectrum, and distinct attributes. In either case the majority of blog participants are close to the low end of each characteristic. However, those at the higher ends are shown to be easily and distinctly identified.