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Abstract Research shows that certain external factors can affect the mental health of many people in a community. Moreover, the importance of mental health has significantly increased in recent years due to the COVID-19 pandemic. Many people communicate and express their emotions through social media platforms, which provide researchers with opportunities to examine insights into their opinions and mental state. While social sensing studies using social media data have flourished in the last decade, many studies using social media data to detect and predict mental health status have focused on the individual level. In this study, we aim to generate a social sensing index for mental health to monitor emotional well-being, which is closely related to mental health, and to identify daily trends in negative emotions at the city level. We conduct sentiment analysis on Twitter data and compute entropy of the degree of sentiment change to develop the index. We observe sentiment trends fluctuate significantly in response to unusual events. It is found that the social sensing index for mental health reflects both city-wide and local events that trigger negative emotions, as well as areas where negative emotions persist. The study contributes to the growing body of research that uses social media data to examine mental health at a city-level. We focus on mental health at the city-level rather than individual, which provides a broader perspective on the mental health of a population. Social sensing index for mental health allows public health professionals to monitor and identify persistent negative sentiments and potential areas where mental health issues may emerge.
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Twitter Sentiment Geographical Index Dataset
Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period.
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- Award ID(s):
- 1841403
- PAR ID:
- 10492125
- Publisher / Repository:
- Scientific Data
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41597-023-02572-7
