Validate the daily macroscopes of emotions on social networks
Golder, SA & Macy, MW Diurnal and seasonal mood vary with work, sleep, and day length in various cultures. Science 333(6051), 1878 (2011).
Garcia, D. & Rimé, B. Collective emotions and social resilience in digital traces after a terrorist attack. Psychol. Science. 30(4), 617 (2019).
Zheng, S., Wang, J., Sun, C., Zhang, X., and Kahn, ME Air pollution reduces happiness expressed by Chinese city dwellers on social media. Nat. Human behavior. 3(3), 237 (2019).
Burke, M. et al. Higher temperatures increase suicide rates in the United States and Mexico. Nat. Air conditioning To change 8(8), 723 (2018).
Ruths, D. & Pfeffer, J. Social media for large behavioral studies. Science 346(6213), 1063 (2014).
Olteanu, A., Castillo, C., Diaz, F. & Kiciman, E. Social data: biases, methodological pitfalls and ethical limits. Front. big data 213 (2019).
Sen, I., et al., A total error framework for digital traces of humans. arXiv:1907.08228 [cs] (2019).
Ribeiro, FN, Araújo, M., Gonçalves, P., Gonçalves, MA and Benevenuto, F. Sentibench – a benchmark comparison of state-of-the-art sentiment analysis methods. EPJ Data Sci. 5(1), 1 (2016).
Beasley, A. & Mason, W. Emotional states versus emotional words in social media. In ACM Web Science Conference Proceedings pages 1 to 10 (2015).
Kross, E. et al. Does counting emotional words on online social networks open a window into people’s subjective emotional experience? A case study on facebook. Emotion 19(1), 97 (2019).
Jaidka, K. et al. Estimating geographic subjective well-being from Twitter: a comparison of dictionary and data-driven language methods. proc. Natl. Acad. Science. 20201906364 (2020).
Pellert, M., Lasser, J., Metzler, H. & Garcia, D. Austrian Social Media Sentiment Dashboard during COVID-19. Front. big data 325 (2020).
Guhr, O., Schumann, A.-K., Bahrmann, F. and Böhme, HJ In Proceedings of the 12th Conference on Language Resources and Assessment p.p. 1620–1625, Marseille, France May 2020. European Linguistic Resources Association.
Wolf, m. et al. Computergestützte quantitative Textanalysequivalenz und Robustheit der deutschen Version des Linguistic Inquiry and Word Count. Diagnosis 54(2), 85 (2008).
Metzler, H. et al. Collective emotions during the COVID-19 epidemic. Emotion (in the press).
Galesic, M. et al., Nature June 2021.
Garcia-Herranz, M., Moro, E., Cebrian, M., Christakis, NA, and Fowler, JH Using Friends as Sensors to Detect Contagious Epidemics on a Global Scale. PLoS One 9(4), e92413 (2014).
Garcia, D., Pellert, M., Lasser, J. & Metzler, H. Social media emotion macroscopes reflect emotional experiences in society at large. arXiv:2107.13236 [cs] (2021).
Ritchie, H. et al., Our world in data (2020).
Goldenberg, A. & Gross, JJ Contagion of digital emotions. Trends Conn. Science. 24(4), 316 (2020).
Ferrara, E. & Yang, Z. Measuring Emotional Contagion in Social Media. PLoS One ten(11), e0142390 (2015).
Gallagher, RJ et al. Generalized Word Shift Graphs: A method for visualizing and explaining pairwise comparisons between texts in EPJ data. Science ten(1), 4 (2021).
Thelwall, M. Cyberemotions: Collective emotions in cyberspace (2014).
Boucher, J. & Osgood, CE The pollyanna hypothesis. J. Verbal learning. Verbal behavior. 8(1), 1 (1969).
Garcia, D., Garas, A. & Schweitzer, F. Positive words contain less information than negative words. EPJ Data Sci. 1(1), 1 (2012).
Ortiz Suarez, PJ, et al. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics p.p. 1703–1714 Online July 2020. Association for Computational Linguistics.
Brown, VG et al. Language models are few learners. arXiv.2005.14165 [cs] (2020).
Metzler, H., Pellert, M. & Garcia, D. Using social media data to capture emotions before and during COVID-19. world happiness report75–104 (2022).
Niederkrotenthaler, T. et al. Mental health over nine months during the SARS-CoV2 pandemic: Representative cross-sectional survey in twelve waves between April and December 2020 in Austria. J. Affect. Disorder. 29649 (2022).
Kahneman, D., Krueger, AB, Schkade, DA, Schwarz, N. & Stone, AA A survey method for characterizing everyday life experience: the day reconstruction method. Science 306(5702), 1776 (2004).
Krueger, AB & Stone, AA Assessing pain: A community diary-based survey in the United States. Lancet 371(9623), 1519 (2008).
Stone, AA The socioeconomic gradient of everyday colds and flu. Headache Pain Arch. Med. Internal. 170(6), 570 (2010).
Stone, AA, Schwartz, JE, Broderick, JE, and Deaton, A. An overview of the age distribution of psychological well-being in the United States. proc. Natl. Acad. Science. 107(22), 9985 (2010).
Stone, AA, Schneider, S. & Harter, JK Weekday Mood Patterns in the United States: On the Existence of “Blue Monday,” “Thank God It’s Friday,” and Weekday Effects -end. J.Posit. Psychol. seven(4), 306 (2012).
Pennebaker, J.W. et al. Austin: University of Texas at Austin vol 26, 25 (2015).
Chan, Ch. et al. Four best practices for measuring news sentiment using “off the shelf” dictionaries: A large-scale p-hacking experiment. Calculation. Common. Res. 3(1), 1 (2021).
Diedenhofen, B. & Musch, J. cocor: A complete solution for the statistical comparison of correlations. PLoS One ten(4), e0121945 (2015).
Hittner, JB, May, K. & Silver, NC A Monte Carlo evaluation of tests for comparing dependent correlations. J. Gen. Psychol. 130(2), 149 (2003).
Core team R. R: a language and an environment for statistical computing (R Foundation for Statistical Computing, ***, 2017).
Zeileis, A. Econometric calculation with HC and HAC covariance matrix estimators. J. Stat. Software 11(10), 1 (2004).
Zeileis, A., Köll, S. & Graham, N. Multipurpose Variances: An Object-Oriented Implementation of Clustered Covariances in R. J. Stat. Software 95(1), 1 (2020).