The authors describe a social media index for measuring subjective well-being, relying on the availability of big data sources provided by the social networking sites, and on one of the most recent techniques for sentiment analysis. This approach disentangles the main methodological issues raised in the literature on well-being measurement.
The authors describe a social media index for measuring subjective well-being, relying on the availability of big data sources provided by the social networking sites, and on one of the most recent techniques for sentiment analysis. This approach disentangles the main methodological issues raised in the literature on well-being measurement.
Stefano M. Iacus is full professor of Statistics at the University of Milan, on leave at the Joint Research Centre of the European Commission. Former R-core member (1999-2017) and R Foundation Member. Giuseppe Porro is full professor of Economic Policy at the University of Insubria. An earlier version of this project was awarded the Italian Institute of Statistics-Google prize for "official statistics and big data".
Inhaltsangabe
1. Subjective and Social Well being. 2. Text and Sentiment Analysis. 3. Extracting Subjective Well Being from Textual Data. 4. How to Control for Bias in Social Media. 5. Subjective Well Being and the COVID Pandemic.
1. Subjective and Social Well being. 2. Text and Sentiment Analysis. 3. Extracting Subjective Well Being from Textual Data. 4. How to Control for Bias in Social Media. 5. Subjective Well Being and the COVID Pandemic.
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