Code

Code for: Mining Twitter to Detect Hotspots in Psychology

Author(s) / Creator(s)

Bittermann, André
Batzdorfer, Veronika
Müller, Sarah Marie
Steinmetz, Holger

Abstract / Description

Code for: Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2021). Mining Twitter to detect hotspots in psychology. Zeitschrift für Psychologie, 229(1), 3–14. https://doi.org/10.1027/2151-2604/a000437
For identifying psychological hotspot topics, a mere focus on bibliometric data suffers from a publication delay. To overcome this issue, we introduce Twitter mining of ongoing online communication among scientists for the detection of psychological research topics. Specifically, we collected the entire 69,963 tweets posted between August 2007 and July 2020 from 139 accounts of psychology professors, departments, and research institutes from the German-speaking countries, as well as sections of the German Psychological Society (DGPs). To examine whether Twitter topics are hotspots in terms of indicating future publication trends, 346,361 references in the PSYNDEX database were extracted. For determining the additional value of our approach in contrast to traditional conference analysis, we gathered all available conference programs of the DGPs and its sections since 2010 and compared dates of topic emergence. Results revealed 21 topics addressing societal issues (e.g., COVID-19), methodology (e.g., machine learning), scientific research (e.g., replication crisis), and different areas of psychological research. Ten topics indicated an increasing publication trend, particularly topics related to methodology or scientific transparency. Seven Twitter topics emerged earlier on Twitter than at conferences. A total of four topics could be expected neither by bibliometric forecasting nor conference contents: “methodological issues in meta-analyses”, “playfulness”, “preregistration”, and “mobile brain/body imaging”. Taken together, Twitter mining is a worthwhile endeavor for identifying psychological hotspot topics, especially regarding societal issues, novel research methods, and research transparency in psychology. In order to get the most comprehensive picture of research hotspots, Twitter mining is recommended in addition to bibliometric analyses of publication trends and monitoring of conference topics.

Keyword(s)

Twitter hot topics research topics biterm topic model publication trends

Persistent Identifier

Date of first publication

2020-11-30

Publisher

PsychArchives

Is referenced by

Citation

Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2020). Code for: Mining Twitter to Detect Hotspots in Psychology. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.4372
  • Author(s) / Creator(s)
    Bittermann, André
  • Author(s) / Creator(s)
    Batzdorfer, Veronika
  • Author(s) / Creator(s)
    Müller, Sarah Marie
  • Author(s) / Creator(s)
    Steinmetz, Holger
  • PsychArchives acquisition timestamp
    2020-11-30T09:08:30Z
  • Made available on
    2020-11-30T09:08:30Z
  • Date of first publication
    2020-11-30
  • Abstract / Description
    Code for: Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2021). Mining Twitter to detect hotspots in psychology. Zeitschrift für Psychologie, 229(1), 3–14. https://doi.org/10.1027/2151-2604/a000437
    en
  • Abstract / Description
    For identifying psychological hotspot topics, a mere focus on bibliometric data suffers from a publication delay. To overcome this issue, we introduce Twitter mining of ongoing online communication among scientists for the detection of psychological research topics. Specifically, we collected the entire 69,963 tweets posted between August 2007 and July 2020 from 139 accounts of psychology professors, departments, and research institutes from the German-speaking countries, as well as sections of the German Psychological Society (DGPs). To examine whether Twitter topics are hotspots in terms of indicating future publication trends, 346,361 references in the PSYNDEX database were extracted. For determining the additional value of our approach in contrast to traditional conference analysis, we gathered all available conference programs of the DGPs and its sections since 2010 and compared dates of topic emergence. Results revealed 21 topics addressing societal issues (e.g., COVID-19), methodology (e.g., machine learning), scientific research (e.g., replication crisis), and different areas of psychological research. Ten topics indicated an increasing publication trend, particularly topics related to methodology or scientific transparency. Seven Twitter topics emerged earlier on Twitter than at conferences. A total of four topics could be expected neither by bibliometric forecasting nor conference contents: “methodological issues in meta-analyses”, “playfulness”, “preregistration”, and “mobile brain/body imaging”. Taken together, Twitter mining is a worthwhile endeavor for identifying psychological hotspot topics, especially regarding societal issues, novel research methods, and research transparency in psychology. In order to get the most comprehensive picture of research hotspots, Twitter mining is recommended in addition to bibliometric analyses of publication trends and monitoring of conference topics.
    en
  • Publication status
    unknown
    en
  • Review status
    unknown
    en
  • Table of contents
    Twitter_preprocessing.R; Twitter_BTM.R; Twitter_forecasting.R; Twitter_additionalResults.R; Source files
    en
  • Citation
    Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2020). Code for: Mining Twitter to Detect Hotspots in Psychology. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.4372
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/3955
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.4372
  • Language of content
    eng
  • Publisher
    PsychArchives
    en
  • Is referenced by
    https://doi.org/10.1027/2151-2604/a000437
  • Is related to
    https://doi.org/10.23668/psycharchives.4373
  • Is related to
    https://doi.org/10.1027/2151-2604/a000437
  • Keyword(s)
    Twitter
    en
  • Keyword(s)
    hot topics
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  • Keyword(s)
    research topics
    en
  • Keyword(s)
    biterm topic model
    en
  • Keyword(s)
    publication trends
    en
  • Dewey Decimal Classification number(s)
    150
  • Title
    Code for: Mining Twitter to Detect Hotspots in Psychology
    en
  • DRO type
    code
    en
  • Leibniz institute name(s) / abbreviation(s)
    ZPID
    de_DE
  • Visible tag(s)
    Hogrefe
    de_DE