World Happiness Report 2023 161 Gibbons, J., Malouf, R., Spitzberg, B., Martinez, L., Appleyard, B., Thompson, C., Nara, A., & Tsou, M.-H. (2019). Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE, 14(7), e0219550. https://doi.org/10.1371/journal.pone.0219550 Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014. Giorgi, S., Eichstaedt, J. C., Preotiuc-Pietro, D., Gardner, J.R., Schwartz, H. A., Ungar, L. (under review). Filling in the White Space: Spatial Interpolation with Gaussian Processes and Social Media Data. Giorgi, S., Lynn, V. E., Gupta, K., Ahmed, F., Matz, S., Ungar, L. H., & Schwartz, H. A. (2022). Correcting Sociodemographic Selection Biases for Population Prediction from Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 16, 228–240. Giorgi, S., Preotiuc-Pietro, D., Buffone, A., Rieman, D., Ungar, L., & Schwartz, H. A. (2018). The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 1167-1172). Giorgi, S., Ungar, L., & Schwartz, H. A. (2021). Characterizing Social Spambots by their Human Traits. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 5148–5158. Glenn, J. J., Nobles, A. L., Barnes, L. E., & Teachman, B. A. (2020). Can text messages identify suicide risk in real time? A within-subjects pilot examination of temporally sensitive markers of suicide risk. Clinical Psychological Science, 8(4), 704–722. Global Partnership for Sustainable Development Data, The World Bank, United Nations, & Sustinable Development Solution Network. (2022). REAL-TIME DATA FOR THE SDGS: Accelerating progress through timely information. https://www. data4sdgs.org/sites/default/files/2019-05/Real-time%20 Data%20for%20the%20SDGs_Concept%20Note.pdf Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051), 1878–1881. Hedonometer. Average Happiness for Twitter. (2022, December 19). Retrieved from https://hedonometer.org/. Hogan, B. (2010). The presentation of self in the age of social media: Distinguishing performances and exhibitions online. Bulletin of Science, Technology & Society, 30(6), 377–386. Hsu, T. W., Niiya, Y., Thelwall, M., Ko, M., Knutson, B., & Tsai, J. L. (2021). Social media users produce more affect that supports cultural values, but are more influenced by affect that violates cultural values. Journal of Personality and Social Psychology. Iacus, S. M., Porro, G., Salini, S., & Siletti, E. (2020). An Italian composite subjective well-being index: The voice of Twitter users from 2012 to 2017. Social Indicators Research, 1–19. INEGI (2022, December 19). Map of the state of mind of Twitter users in Mexico. Retrieved from https://www.inegi.org.mx/app/ animotuitero/#/app/map. Jaggi, M., Uzdilli, F., & Cieliebak, M. (2014). Swiss-chocolate: Sentiment detection using sparse SVMs and part-of-speech n-grams. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 601–604. Jaidka, K., Chhaya, N., & Ungar, L. (2018). Diachronic degradation of language models: Insights from social media. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 195–200. https://doi.org/10.18653/v1/P18-2032 Jaidka, K., Giorgi, S., Schwartz, H. A., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2020). Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods. Proceedings of the National Academy of Sciences, 117(19), 10165–10171. Jose, R., Matero, M., Sherman, G., Curtis, B., Giorgi, S., Schwartz, H. A., & Ungar, L. H. (2022). Using Facebook language to predict and describe excessive alcohol use. Alcoholism: Clinical and Experimental Research. Kramer, A. D. (2010). An unobtrusive behavioral model of” gross national happiness”. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 287–290. Lavertu, A., Hamamsy, T., & Altman, R. B. (2021). Monitoring the opioid epidemic via social media discussions. MedRxiv. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–1205. Little, R. J. (1993). Post-stratification: A modeler’s perspective. Journal of the American Statistical Association, 88(423), 1001–1012. Liu, T., Meyerhoff, J., Eichstaedt, J. C., Karr, C. J., Kaiser, S. M., Kording, K. P., Mohr, D. C., & Ungar, L. H. (2022). The relationship between text message sentiment and self-reported depression. Journal of Affective Disorders, 302, 7–14. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. ArXiv Preprint ArXiv:1907.11692. Lomas, T., Lai, A., Shiba, K., Diego-Rosell, P., Uchida, Y., & VanderWeele, T. J. (2022). Insights from the first global survey of balance and harmony. World Happiness Report 2022, 127–154. Luhmann, M. (2017). Using big data to study subjective well-being. Current Opinion in Behavioral Sciences, 18, 28–33. Mangalik, S., Eichstaedt, J. C., Giorgi, S., Mun, J., Ahmed, F., Gill, G., ... & Schwartz, H. A. (2023). Robust language-based mental health assessments in time and space through social media. arXiv preprint arXiv:2302.12952. Metzler, H., Pellert, M., & Garcia, D. (2022). Using social media data to capture emotions before and during COVID-19. World Happiness Report 2022, 75–104. Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PloS One, 8(5), e64417. Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). Semeval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17.
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