<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0013_s_006_w2aab3b8b1b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>In this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics.</p></sec><sec id="j_jdis-2017-0013_s_007_w2aab3b8b1b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>Articles in the field of scientometrics were chosen as research cases in this study. A time-sliced co-word network was generated and then clustered. Afterwards, we generated an alluvial diagram to show dynamic changes of hot topics, including their merges and splits over time.</p></sec><sec id="j_jdis-2017-0013_s_008_w2aab3b8b1b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>After analyzing the dynamic changes in the field of scientometrics from 2011 to 2015, we found that two clusters being merged did not mean that the old topics had disappeared and a totally new one had emerged. The topics were possibly still active the following year, but the newer topics had drawn more attention. The changes of hot topics reflected the shift in researchers’ interests. Research topics in scientometrics were constantly subdivided and re-merged. For example, a cluster involving “industry” was divided into several topics as research progressed.</p></sec><sec id="j_jdis-2017-0013_s_009_w2aab3b8b1b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>When examining longer time periods, we encounter the problem of dealing with bigger data sets. Analyzing data year by year would be tedious, but if we combine, e.g. two years into one time slice, important details would be missed.</p></sec><sec id="j_jdis-2017-0013_s_010_w2aab3b8b1b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>This method can be applied to any research field to illustrate the dynamics of hot topics. It can indicate the promising directions for researchers and provide guidance to decision makers.</p></sec><sec id="j_jdis-2017-0013_s_011_w2aab3b8b1b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>The use of alluvial diagrams is a distinctive and meaningful approach to detecting hot topics and especially to illustrating their dynamics.</p></sec></abstract>

PurposeIn this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics.Design/methodology/approachArticles in the field of scientometrics were chosen as research cases in this study. A time-sliced co-word network was generated and then clustered. Afterwards, we generated an alluvial diagram to show dynamic changes of hot topics, including their merges and splits over time.FindingsAfter analyzing the dynamic changes in the field of scientometrics from 2011 to 2015, we found that two clusters being merged did not mean that the old topics had disappeared and a totally new one had emerged. The topics were possibly still active the following year, but the newer topics had drawn more attention. The changes of hot topics reflected the shift in researchers’ interests. Research topics in scientometrics were constantly subdivided and re-merged. For example, a cluster involving “industry” was divided into several topics as research progressed.Research limitationsWhen examining longer time periods, we encounter the problem of dealing with bigger data sets. Analyzing data year by year would be tedious, but if we combine, e.g. two years into one time slice, important details would be missed.Practical implicationsThis method can be applied to any research field to illustrate the dynamics of hot topics. It can indicate the promising directions for researchers and provide guidance to decision makers.Originality/valueThe use of alluvial diagrams is a distinctive and meaningful approach to detecting hot topics and especially to illustrating their dynamics.

Detecting Dynamics of Hot Topics with Alluvial Diagrams: A Timeline Visualization

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeIn this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their...

Detecting Dynamics of Hot Topics with Alluvial Diagrams: A Timeline Visualization

Article Category: Research Paper

Data publikacji: 22 sie 2017

Zakres stron: 37 - 48

Otrzymano: 15 paź 2016

Przyjęty: 07 lut 2017

DOI: https://doi.org/10.1515/jdis-2017-0013

Słowa kluczowe
Dynamics, Alluvial diagram, Hot topics, Timeline approach

© 2017 Wenjing Ruan, Haiyan Hou & Zhigang Hu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Detecting Dynamics of Hot Topics with Alluvial Diagrams: A Timeline Visualization

Article Category: Research Paper

Data publikacji: 22 sie 2017

Zakres stron: 37 - 48

Otrzymano: 15 paź 2016

Przyjęty: 07 lut 2017

DOI: https://doi.org/10.1515/jdis-2017-0013

Słowa kluczoweDynamics, Alluvial diagram, Hot topics, Timeline approach

© 2017 Wenjing Ruan, Haiyan Hou & Zhigang Hu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Dynamics, Alluvial diagram, Hot topics, Timeline approach