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Topic evolution and social interactions: how authors effect research

Published: 06 November 2006 Publication History

Abstract

We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact.

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      cover image ACM Conferences
      CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management
      November 2006
      916 pages
      ISBN:1595934332
      DOI:10.1145/1183614
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 06 November 2006

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      Author Tags

      1. clustering
      2. markov chains
      3. social network analysis
      4. text data mining

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      CIKM06
      CIKM06: Conference on Information and Knowledge Management
      November 6 - 11, 2006
      Virginia, Arlington, USA

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      • (2024)Collaboration and topic switches in scienceScientific Reports10.1038/s41598-024-51606-614:1Online publication date: 13-Jan-2024
      • (2024)Inferring social networks from unstructured text data: A proof of concept detection of hidden communities of interestData & Policy10.1017/dap.2023.486Online publication date: 26-Jan-2024
      • (2023)Multiview Deep Online Clustering: An Application to Online Research Topic Modeling and RecommendationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318734210:5(2566-2578)Online publication date: Oct-2023
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