Skip to main content
SpringerLink
Log in

Evaluation of the evolution of relationships between topics over time

  • Regular Article
  • Published:
Advances in Data Analysis and Classification Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Allan J (2002a) Detection as multi-topic tracking. Inf Retr 5(2–3):139–157

    Article  Google Scholar 

  • Allan J (2002b) Introduction to topic detection and tracking. In: Allan J (ed) Topic detection and tracking. Kluwer Academic Publishers, Norwell, pp 1–16

    Chapter  Google Scholar 

  • Allan J (ed) (2002c) Topic detection and tracking: event-based information organization. Kluwer Academic Publishers, Norwell

    MATH  Google Scholar 

  • Allan J, Carbonell J, Doddington G, Yamron J, Yang Y (1998) Topic detection and tracking pilot study: final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. Lansdowne, VA, USA, pp 194–218

  • Allan J, Lavrenko V, Swan R (2002) Exploration within topic tracking and detection. In: Allan J (ed) Topic detection and tracking. Kluwer Academic Publishers, Norwell, pp 197–224

    Chapter  Google Scholar 

  • Benhardus J (2010) Streaming trend detection in Twitter. In: UCCS REU for Artificial Intelligence, Natural Language Processing and Information Retrieval, Final Report

  • Bock HH (1974) Automatische Klassifikation. Theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten (Cluster-Analyse). Vandenhoeck & Ruprecht, Göttingen

    MATH  Google Scholar 

  • Bock HH (1980) Clusteranalyse—Überblick und neuere Entwicklungen. Oper Res Spektrum 1(4):211–232

  • Brandes U, Erlebach T (eds) (2005) Network analysis: methodological foundations, vol 3418. Lecture Notes in Computer Science. Springer-Verlag New York Inc, Secaucus

  • Bun KK, Ishizuka M (2006) Emerging topic tracking system in WWW. Knowl Based Syst 19(3):164–171

    Article  Google Scholar 

  • Gaul W (2011) Web page importance ranking. Adv Data Anal Classif 5:113–128

    Article  MathSciNet  MATH  Google Scholar 

  • Jin Y, Myaeng SH, Jung Y (2007) Use of place information for improved event tracking. Inf Process Manage 43(2):365–378

    Article  Google Scholar 

  • Khy S, Ishikawa Y, Kitagawa H (2008) A novelty-based clustering method for on-line documents. World Wide Web 11(1):1–37

    Article  Google Scholar 

  • Kim P, Myaeng SH (2004) Usefulness of temporal information automatically extracted from news articles for topic tracking. ACM Trans Asian Lang Inf Process 3(4):227–242

    Article  Google Scholar 

  • Kupietz M, Keibel H (2009) The Mannheim German reference corpus (DeReKo) as a basis for empirical linguistic research. In: Minegishi M, Kawaguchi Y (eds) Working Papers in Corpus-Based Linguistics and Language Education, Tokyo University of Foreign Studies (TUFS), 3, pp 53–59

  • Kupietz M, Belica C, Keibel H, Witt A (2010) The German reference corpus DeReKo: A primordial sample for linguistic research. In: Calzolari N, Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D (eds) Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta

  • Li B, Li W, Lu Q (2006) Topic tracking with time granularity reasoning. ACM Trans Asian Lang Inf Process 5(4):388–412

    Article  Google Scholar 

  • Mathioudakis M, Koudas N (2010) Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, SIGMOD ’10, pp 1155–1158

  • Mei Q, Liu C, Su H, Zhai C (2006) A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th International Conference on World Wide Web, ACM, New York, NY, USA, WWW ’06, pp 533–542

  • Oard DW (1999) Topic tracking with the prise information retrieval system. In: Proceedings of the DARPA Broadcast News Workshop, pp 209–211

  • Oliveira M, Gama J (2010) Bipartite graphs for monitoring clusters transitions. In: Cohen P, Adams N, Berthold M (eds) Advances in intelligent data analysis IX, vol 6065., Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 114–124

  • Pons-Porrata A, Berlanga-Llavori R, Ruiz-Shulcloper J (2002) On-line event and topic detection by using the compact sets clustering algorithm. J Intell Fuzzy Syst 12(3,4):185–194

  • Rajaraman K, Tan AH (2001) Topic detection, tracking, and trend analysis using self-organizing neural networks. In: Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer-Verlag, London, UK, UK, PAKDD ’01, pp 102–107

  • Salton G (1989) Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc, Boston

    Google Scholar 

  • Steiner T, van Hooland S, Summers E (2013) MJ no more: using concurrent wikipedia edit spikes with social network plausibility checks for breaking news detection. Computing Research Repository. arXiV:1303.4702

  • Tu YN, Seng JL (2012) Indices of novelty for emerging topic detection. Inf Process Manage 48(2):303–325

    Article  Google Scholar 

  • Walls F, Jin H, Sista S, Schwartz R (1999) Topic detection in broadcast news. In: Proceedings of the DARPA Broadcast News Workshop, Morgan Kaufmann Publishers, Inc, pp 193–198

  • Wayne CL (1998) Topic detection and tracking (tdt)—overview and perspective. In: DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne Conference Resort, Lansdowne Virginia

  • Wei CP, Lee YH (2004) Event detection from online news documents for supporting environmental scanning. Decis Support Syst 36(4):385–401

    Article  Google Scholar 

  • Yang C, Shi X, Wei CP (2009) Discovering event evolution graphs from news corpora. IEEE Trans Syst Man Cybern Part A Syst Hum 39(4):850–863

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Wolfgang Gaul & Dominique Vincent

Authors
  1. Wolfgang Gaul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dominique Vincent
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wolfgang Gaul.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Mathematics Subject Classification

Search

Navigation