Validating daily social media macroscopes of emotions
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-14579-y |
| _version_ | 1811224110555987968 |
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| author | Max Pellert Hannah Metzler Michael Matzenberger David Garcia |
| author_facet | Max Pellert Hannah Metzler Michael Matzenberger David Garcia |
| author_sort | Max Pellert |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:44:40Z |
| format | Article |
| id | doaj.art-0f0ac31c4af54b948f668c4f423346e4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T08:44:40Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0f0ac31c4af54b948f668c4f423346e42022-12-22T03:39:45ZengNature PortfolioScientific Reports2045-23222022-07-011211810.1038/s41598-022-14579-yValidating daily social media macroscopes of emotionsMax Pellert0Hannah Metzler1Michael Matzenberger2David Garcia3Section for Science of Complex Systems, Center for Medical Statistics Informatics and Intelligent Systems, Medical University of ViennaSection for Science of Complex Systems, Center for Medical Statistics Informatics and Intelligent Systems, Medical University of ViennaDer StandardSection for Science of Complex Systems, Center for Medical Statistics Informatics and Intelligent Systems, Medical University of ViennaAbstract 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.https://doi.org/10.1038/s41598-022-14579-y |
| spellingShingle | Max Pellert Hannah Metzler Michael Matzenberger David Garcia Validating daily social media macroscopes of emotions Scientific Reports |
| title | Validating daily social media macroscopes of emotions |
| title_full | Validating daily social media macroscopes of emotions |
| title_fullStr | Validating daily social media macroscopes of emotions |
| title_full_unstemmed | Validating daily social media macroscopes of emotions |
| title_short | Validating daily social media macroscopes of emotions |
| title_sort | validating daily social media macroscopes of emotions |
| url | https://doi.org/10.1038/s41598-022-14579-y |
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