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Evaluation of the evolution of relationships between topics over time

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Abstract

Topics that attract public attention can originate from current events or developments, might be influenced by situations in the past, and often continue to be of interest in the future. When respective information is made available textually, one possibility of detecting such topics of public importance consists in scrutinizing, e.g., appropriate press articles using—given the continual growth of information—text processing techniques enriched by computer routines which examine present-day textual material, check historical publications, find newly emerging topics, and are able to track topic trends over time. Information clustering based on content-(dis)similarity of the underlying textual material and graph-theoretical considerations to deal with the network of relationships between content-similar topics are described and combined in a new approach. Explanatory examples of topic detection and tracking in online news articles illustrate the usefulness of the approach in different situations.

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Authors and Affiliations

  1. Institute of Information Systems and Marketing, Karlsruhe Institute of Technology, Zirkel 2, 76131, Karlsruhe, Germany

    Wolfgang Gaul & Dominique Vincent

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  1. Wolfgang Gaul
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  2. Dominique Vincent
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Correspondence to Wolfgang Gaul.

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Gaul, W., Vincent, D. Evaluation of the evolution of relationships between topics over time. Adv Data Anal Classif 11, 159–178 (2017). https://doi.org/10.1007/s11634-016-0241-2

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  • DOI: https://doi.org/10.1007/s11634-016-0241-2

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