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Validating daily social media macroscopes of emotions
Pellert, Max
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Metzler, Hannah
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Matzenberger, Michael
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Garcia, David
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DOI:
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https://doi.org/10.1038/s41598-022-14579-y
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URL:
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https://www.nature.com/articles/s41598-022-14579-y
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Weitere URL:
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https://www.researchgate.net/publication/361741036...
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URN:
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urn:nbn:de:bsz:180-madoc-633222
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Dokumenttyp:
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Zeitschriftenartikel
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Erscheinungsjahr:
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2022
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Titel einer Zeitschrift oder einer Reihe:
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Scientific Reports
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Band/Volume:
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12
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Heft/Issue:
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Article number 11236
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Seitenbereich:
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1-8
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Ort der Veröffentlichung:
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London
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Verlag:
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Macmillan Publishers Limited, part of Springer Nature
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ISSN:
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2045-2322
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Betriebswirtschaftslehre > Data Science in the Economic and Social Sciences (Strohmaier, 2022-)
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Bereits vorhandene Lizenz:
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 Creative Commons Namensnennung 4.0 International (CC BY 4.0)
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Fachgebiet:
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330 Wirtschaft
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Freie Schlagwörter (Englisch):
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computational science , computer science , human behaviour
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Abstract:
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Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.
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 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 | Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation. |
 Suche Autoren in
BASE:
Pellert, Max
;
Metzler, Hannah
;
Matzenberger, Michael
;
Garcia, David
Google Scholar:
Pellert, Max
;
Metzler, Hannah
;
Matzenberger, Michael
;
Garcia, David
ORCID:
Pellert, Max  ORCID: 0000-0002-6557-7607 ; Metzler, Hannah ; Matzenberger, Michael ; Garcia, David
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