Validating daily social media macroscopes of emotions


Pellert, Max ; Metzler, Hannah ; Matzenberger, Michael ; Garcia, David


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DOI: https://doi.org/10.1038/s41598-022-14579-y
URL: https://www.nature.com/articles/s41598-022-14579-y
Additional URL: https://www.researchgate.net/publication/361741036...
URN: urn:nbn:de:bsz:180-madoc-633222
Document Type: Article
Year of publication: 2022
The title of a journal, publication series: Scientific Reports
Volume: 12
Issue number: Article number 11236
Page range: 1-8
Place of publication: London
Publishing house: Macmillan Publishers Limited, part of Springer Nature
ISSN: 2045-2322
Publication language: English
Institution: Business School > Data Science in the Economic and Social Sciences (Strohmaier, 2022-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 330 Economics
Keywords (English): computational science , computer science , human behaviour
Abstract: 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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