Đo lường hạnh phúc khách quan và chủ quan: các chiều và nguồn dữ liệu

Reinhart, C.M., Reinhart, V.R.: After the fall. Technical report. National Bureau of Economic Research (2010)

Fleurbaey, M.: Beyond gdp: the quest for a measure of social welfare. J. Econ. Lit. 47(4), 1029–75 (2009)

Stiglitz, J.E., Sen, A., Fitoussi, J.P.: Report by the Commission on the Measurement of Economic Performance and Social Progress. The Commission Paris (2009)

Dodge, R., Daly, A.P., Huyton, J., Sanders, L.D.: The challenge of defining wellbeing. Int. J. Wellbeing 2(3), 11 (2012)

Alkire, S.: Dimensions of human development. World Dev. 30(2), 181–205 (2002)

Organisation for Economic Co-operation and Development How’s life? Measuring Well-Being. OECD, Paris (2011)

UNDP Sustainable Development Goals. https://sustainabledevelopment.un.org/sdgs. Accessed Oct 2019 (2015)

Rapporto, BES Il benessere equo e sostenibile in Italia. ISTAT (2015)

Organisation for Economic Co-operation and Development (OECD) OECD Guidelines on Measuring Subjective Well-Being. OECD Publishing (2013)

Veenhoven, R.: Conditions of Happiness, Reidel. Springer, Dordrecht (1984)

Frey, B.S., Stutzer, A.: What can economists learn from happiness research? J. Econ. Lit. 40(2), 402–435 (2002)

Stiglitz, J.E., Sen, A., Fitoussi, J.P.: Measurement of economic performance and social progress. Online document. http://www.bitly/JTwmG Accessed 26 June 2012 (2009)

Bartels, M., Boomsma, D.I.: Born to be happy? The etiology of subjective well-being. Behav. Genet. 39(6), 605 (2009)

Bartels, M., Saviouk, V., De Moor, M.H., Willemsen, G., van Beijsterveldt, T.C., Hottenga, J.J., De Geus, E.J., Boomsma, D.I.: Heritability and genome-wide linkage scan of subjective happiness. Twin Res. Hum. Genet. 13(2), 135–142 (2010)

Nes, R.B., Røysamb, E.: The heritability of subjective well-being: review and meta-analysis. In: The Genetics of Psychological Well-Being: The Role of Heritability and Genetics in Positive Psychology, pp. 75–96 (2015)

Nes, R.B., Czajkowski, N., Tambs, K.: Family matters: happiness in nuclear families and twins. Behav. Genet. 40(5), 577–590 (2010)

Nes, R., Røysamb, E., Tambs, K., Harris, J., Reichborn-Kjennerud, T.: Subjective well-being: genetic and environmental contributions to stability and change. Psychol. Med. 36(7), 1033–1042 (2006)

Røysamb, E., Harris, J.R., Magnus, P., Vittersø, J., Tambs, K.: Subjective well-being. Sex-specific effects of genetic and environmental factors. Personal. Individ. Differ. 32(2), 211–223 (2002)

Røysamb, E., Tambs, K., Reichborn-Kjennerud, T., Neale, M.C., Harris, J.R.: Happiness and health: environmental and genetic contributions to the relationship between subjective well-being, perceived health, and somatic illness. J. Pers. Soc. Psychol. 85(6), 1136 (2003)

Schnittker, J.: Happiness and success: genes, families, and the psychological effects of socioeconomic position and social support. Am. J. Sociol. 114(S1), S233–S259 (2008)

Pleeging, E., Burger, M., van Exel, J.: The relations between hope and subjective well-being: a literature overview and empirical analysis. Appl. Res. Qual. Life 1, 1–23 (2020)

Kenrick, D.T., Griskevicius, V., Neuberg, S.L., Schaller, M.: Renovating the pyramid of needs: contemporary extensions built upon ancient foundations. Perspect. Psychol. Sci. 5(3), 292–314 (2010)

Ryan, R.M., Deci, E.L.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55(1), 68 (2000)

Tay, L., Diener, E.: Needs and subjective well-being around the world. J. Pers. Soc. Psychol. 101(2), 354 (2011)

Clark, A.E., Oswald, A.J.: Satisfaction and comparison income. J. Public Econ. 61(3), 359–381 (1996)

Shields, M.A., Price, S.W., Wooden, M.: Life satisfaction and the economic and social characteristics of neighbourhoods. J. Popul. Econ. 22(2), 421–443 (2009)

Powdthavee, N.: How much does money really matter? Estimating the causal effects of income on happiness. Empir. Econ. 39(1), 77–92 (2010)

Nikolaev, B.: Living with mom and dad and loving it... or are you? J. Econ. Psychol. 51, 199–209 (2015)

Dolan, P., Peasgood, T., White, M.: Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. J. Econ. Psychol. 29(1), 94–122 (2008)

Easterlin, R.A.: Does economic growth improve the human lot? Some empirical evidence. In: Nations and Households in Economic Growth, pp 89–125. Elsevier (1974)

Veenhoven, R.: Is happiness relative? Soc. Indic. Res. 24(1), 1–34 (1991)

Diener, E., Tay, L., Oishi, S.: Rising income and the subjective well-being of nations. J. Pers. Soc. Psychol. 104(2), 267 (2013)

Veenhoven, R., Vergunst, F.: The Easterlin illusion: economic growth does go with greater happiness. Int. J. Happiness Dev. 1(4), 311–343 (2014)

Sacks, D.W., Stevenson, B., Wolfers, J.: The new stylized facts about income and subjective well-being. Emotion 12(6), 1181 (2012)

Radcliff, B., Shufeldt, G.: Direct democracy and subjective well-being: the initiative and life satisfaction in the American states. Soc. Indic. Res. 128(3), 1405–1423 (2016)

Veenhoven, R.: Social conditions for human happiness: a review of research. Int. J. Psychol. 50(5), 379–391 (2015)

Deaton, A.: The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. The World Bank (1997)

European Project.: SoBigData. http://sobigdata.eu/index. Accessed Oct 2019 (2015)

Shi, Z.R., Wang, C., Fang, F.: Artificial Intelligence for Social Good: A Survey. arXiv preprint arXiv:2001.01818 (2020)

Solomon, D.J.: Conducting web-based surveys. Pract. Assess. Res. Eval. 7(19), 12 (2001)

Daas, P.J., Puts, M.J., Buelens, B., Van den Hurk, P.A.: Big data and official statistics. In: Proceedings of the NTTS, pp. 5–7. New Techniques and Technologies for Statistics (2013)

Struijs, P., Daas, P.: Quality approaches to big data in official statistics. In: European Conference on Quality in Official Statistics (2014)

Jahani, E., Sundsøy, P., Bjelland, J., Bengtsson, L., de Montjoye, Y.A., et al.: Improving official statistics in emerging markets using machine learning and mobile phone data. EPJ Data Sci. 6(1), 3 (2017)

Blumenstock, J.E.: Fighting poverty with data. Science 353(6301), 753–754 (2016)

United Nations.: A world that counts: mobilizing the data revolution for sustainable development. Technical report (2014)

Sustainable Development Solutions Network: Indicators and a Monitoring Framework for the Sustainable Development Goals. Launching a Data Revolution for the SDGs, United Nations, New York (2015)

WHO, World Health Organization: Geneva Macroeconomics and health: investing in health for economic development-report of the commission on macroeconomics and health. Commission on Macroeconomics and Health (2001)

European Commission: The Lisbon strategy for growth and jobs (2000)

OECD.: OECD Better Life Index: Health. http://www.oecdbetterlifeindex.org/topics/health/. Accessed Oct 2019 (2011)

OECD.: OECD Better Life Index: Jobs. http://www.oecdbetterlifeindex.org/topics/jobs/. Accessed Oct 2019 (2011a)

OECD.: OECD Better Life Index: Income. http://www.oecdbetterlifeindex.org/topics/income/. Accessed Oct 2019 (2011b)

OECD.: OECD Better Life Index: Environment. http://www.oecdbetterlifeindex.org/topics/environment/. Accessed Oct 2019 (2011c)

OECD.: OECD Better Life Index: Safety. http://www.oecdbetterlifeindex.org/topics/safety/. Accessed Oct 2019 (2011d)

Amerio, P., Roccato, M.: Psychological reactions to crime in Italy: 2002–2004. J. Commun. Psychol. 35(1), 91–102 (2007)

OECD.: OECD Better Life Index: Civic Engagement. http://www.oecdbetterlifeindex.org/topics/civic-engagement/. Accessed Oct 2019 (2011)

Blondel, V.D., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4(1), 10 (2015)

Eagle, N., Pentland, A.S.: Eigenbehaviors: identifying structure in routine. Behav. Ecol. Sociobiol. 63(7), 1057–1066 (2009)

Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., Barabási, A.L.: Returners and explorers dichotomy in human mobility. Nat. Commun. 6, 8166 (2015)

Pappalardo, L., Rinzivillo, S., Simini, F.: Human mobility modelling: exploration and preferential return meet the gravity model. Proc. Comput. Sci. 83, 934–939 (2016). https://doi.org/10.1016/j.procs.2016.04.188

Pellungrini, R., Pappalardo, L., Pratesi, F., Monreale, A.: A data mining approach to assess privacy risk in human mobility data. ACM Trans. Intell. Syst. Technol. 9(3), 31:1–31:27 (2017). https://doi.org/10.1145/3106774

Pappalardo, L., Simini, F.: Data-driven generation of spatio-temporal routines in human mobility. Data Min. Knowl. Disc. 32(3), 787–829 (2018)

Giannotti, F., Pappalardo, L., Pedreschi, D., Wang, D.: A Complexity Science Perspective on Human Mobility, pp. 297–314. Cambridge University Press, Cambridge (2013). https://doi.org/10.1017/CBO9781139128926.016

Ranjan, G., Zang, H., Zhang, Z.L., Bolot, J.: Are call detail records biased for sampling human mobility? ACM SIGMOBILE Mob. Comput. Commun. Rev. 16(3), 33–44 (2012)

Iovan, C., Olteanu-Raimond, A.M., Couronné, T., Smoreda, Z,: Moving and calling: mobile phone data quality measurements and spatiotemporal uncertainty in human mobility studies. In: Geographic Information Science at the Heart of Europe, pp. 247–265. Springer (2013)

Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779 (2008)

Barabasi, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435(7039), 207 (2005)

Oliver, N., Matic, A., Frias-Martinez, E.: Mobile network data for public health: opportunities and challenges. Front. Public Health 3, 189 (2015)

Finger, F., Genolet, T., Mari, L., de Magny, G.C., Manga, N.M., Rinaldo, A., Bertuzzo, E.: Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. Proc. Nat. Acad. Sci. 113(23), 6421–6426 (2016)

Kafsi, M., Kazemi, E., Maystre, L., Yartseva, L., Grossglauser, M., Thiran, P.: Mitigating epidemics through mobile micro-measures. arXiv preprint arXiv:1307.2084 (2013)

Lima, A., De Domenico, M., Pejovic, V., Musolesi, M.: Disease containment strategies based on mobility and information dissemination. Sci. Rep. 5, 10650 (2015)

Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social sensing for epidemiological behavior change. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 291–300. ACM (2010)

Pappalardo, L., Pedreschi, D., Smoreda, Z., Giannotti, F.: Using big data to study the link between human mobility and socio-economic development. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 871–78 (2015) https://doi.org/10.1109/BigData.2015.7363835

Toole, J.L., Lin, Y.R., Muehlegger, E., Shoag, D., González, M.C., Lazer, D.: Tracking employment shocks using mobile phone data. J. R. Soc. Interface 12(107), 20150185 (2015)

Sundsøy, P., Bjelland, J., Reme, B.A., Jahani, E., Wetter, E., Bengtsson, L.: Towards real-time prediction of unemployment and profession. In: International Conference on Social Informatics, pp. 14–23. Springer (2017)

Eagle, N., Macy, M., Claxton, R.: Network diversity and economic development. Science 328(5981), 1029–1031 (2010)

Steele, J.E., Sundsøy, P.R., Pezzulo, C., Alegana, V.A., Bird, T.J., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.A., Iqbal, A.M., et al.: Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14(127), 20160690 (2017)

Mao, H., Shuai, X., Ahn, Y.Y., Bollen, J.: Quantifying socio-economic indicators in developing countries from mobile phone communication data: applications to côte d’ivoire. EPJ Data Sci. 4(1), 15 (2015)

Gutierrez, T., Krings, G., Blondel, V.D.: Evaluating socio-economic state of a country analyzing airtime credit and mobile phone datasets. arXiv preprint arXiv:1309.4496 (2013)

Blumenstock, J.: Calling for better measurement: estimating an individual’s wealth and well-being. ACM KDD (Data Mining for Social Good) (2014)

Blumenstock, J., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata. Science 350(6264), 1073–1076 (2015)

Frias-Martinez, V., Virseda, J.: On the relationship between socio-economic factors and cell phone usage. In: Proceedings of the Fifth International Conference on Information and Communication Technologies and Development, pp. 76–84. ACM (2012)

Soto, V., Frias-Martinez, V., Virseda, J., Frias-Martinez, E.: Prediction of socioeconomic levels using cell phone records. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 377–388. Springer (2011)

Frias-Martinez, V., Soguero-Ruiz, C., Frias-Martinez, E., Josephidou, M.: Forecasting socioeconomic trends with cell phone records. In: Proceedings of the 3rd ACM Symposium on Computing for Development, p. 15. ACM (2013)

Hernandez, M., Hong, L., Frias-Martinez, V., Frias-Martinez, E.: Estimating poverty using cell phone data: evidence from Guatemala. The World Bank (2017)

Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., Giannotti, F.: An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal. 2(1), 75–92 (2016). https://doi.org/10.1007/s41060-016-0013-2

Lotero, L., Cardillo, A., Hurtado, R., Gómez-Gardeñes, J.: Several multiplexes in the same city: the role of socioeconomic differences in urban mobility. In: Interconnected Networks, pp. 149–164. Springer (2016)

Amini, A., Kung, K., Kang, C., Sobolevsky, S., Ratti, C.: The impact of social segregation on human mobility in developing and industrialized regions. EPJ Data Sci. 3(1), 6 (2014)

Smith-Clarke, C., Mashhadi, A., Capra, L.: Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 511–520. , ACM (2014)

Picornell, M., Ruiz, T., Borge, R., García-Albertos, P., de la Paz, D., Lumbreras, J.: Population dynamics based on mobile phone data to improve air pollution exposure assessments. J. Expos. Sci. Environ. Epidemiol. 29(2), 278 (2019)

Lu, X., Wrathall, D.J., Sundsøy, P.R., Nadiruzzaman, M., Wetter, E., Iqbal, A., Qureshi, T., Tatem, A.J., Canright, G.S., Engø-Monsen, K., et al.: Detecting climate adaptation with mobile network data in bangladesh: anomalies in communication, mobility and consumption patterns during cyclone mahasen. Clim. Change 138(3–4), 505–519 (2016)

Lu, X., Bengtsson, L., Holme, P.: Predictability of population displacement after the 2010 haiti earthquake. Proc. Nat. Acad. Sci. 109(29), 11576–11581 (2012)

Bengtsson, L., Lu, X., Thorson, A., Garfield, R., Von Schreeb, J.: Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in haiti. PLoS Med. 8(8), e1001083 (2011)

Wilson, R., Zu Erbach-Schoenberg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., Guthrie, S., Chamberlain, H., Brooks, C., Hughes, C., et al.: Rapid and near real-time assessments of population displacement using mobile phone data following disasters: the 2015 Nepal earthquake. PLoS Curr. 8, 1 (2016)

Nyarku, M., Mazaheri, M., Jayaratne, R., Dunbabin, M., Rahman, M.M., Uhde, E., Morawska, L.: Mobile phones as monitors of personal exposure to air pollution: Is this the future? PLoS ONE 13(2), e0193150 (2018)

Liu, H.Y., Skjetne, E., Kobernus, M.: Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment. Environ. Health 12(1), 93 (2013)

Decuyper, A., Rutherford, A., Wadhwa, A., Bauer, J.M., Krings, G., Gutierrez, T., Blondel, V.D., Luengo-Oroz, M.A.: Estimating food consumption and poverty indices with mobile phone data. arXiv preprint arXiv:1412.2595 (2014)

Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., Pentland, A.: Once upon a crime: towards crime prediction from demographics and mobile data. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 27–434. ACM (2014)

Ferrara, E., De Meo, P., Catanese, S., Fiumara, G.: Detecting criminal organizations in mobile phone networks. Expert Syst. Appl. 41(13), 5733–5750 (2014)

Elgethun, K., Fenske, R.A., Yost, M.G., Palcisko, G.J.: Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument. Environ. Health Perspect. 111(1), 115–122 (2003)

Dias, D., Tchepel, O.: Modelling of human exposure to air pollution in the urban environment: a GPS-based approach. Environ. Sci. Pollut. Res. 21(5), 3558–3571 (2014)

Beekhuizen, J., Kromhout, H., Huss, A., Vermeulen, R.: Performance of gps-devices for environmental exposure assessment. J. Eposure Sci. Environ. Epidemiol. 23(5), 498 (2013)

Pappalardo, L., Simini, F., Barlacchi, G., Pellungrini, R.: Scikit-mobility: a python library for the analysis, generation and risk assessment of mobility data. arXiv:1907.07062 (2019)

Jankowska, M.M., Schipperijn, J., Kerr, J.: A framework for using GPS data in physical activity and sedentary behavior studies. Exerc. Sport Sci. Rev. 43(1), 48 (2015)

Kelly, P., Krenn, P., Titze, S., Stopher, P., Foster, C.: Quantifying the difference between self-reported and global positioning systems-measured journey durations: a systematic review. Transp. Rev. 33(4), 443–459 (2013)

Meurs, H., Haaijer, R.: Spatial structure and mobility. Transp. Res. Part D Transp. Environ. 6(6), 429–446 (2001)

Oliver, M., Badland, H., Mavoa, S., Duncan, M.J., Duncan, S.: Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors. J. Phys. Activity Health 7(1), 102–108 (2010)

Adams, S.A., Matthews, C.E., Ebbeling, C.B., Moore, C.G., Cunningham, J.E., Fulton, J., Hebert, J.R.: The effect of social desirability and social approval on self-reports of physical activity. Am. J. Epidemiol. 161(4), 389–398 (2005)

Pappalardo, L., Rinzivillo, S., Qu, Z., Pedreschi, D., Giannotti, F.: Understanding the patterns of car travel. Eur. Phys. J. Spec. Top. 215(1), 61–73 (2013). https://doi.org/10.1140/epjst/e2013-01715-5

Chaix, B., Kestens, Y., Duncan, D.T., Brondeel, R., Méline, J., El Aarbaoui, T., Pannier, B., Merlo, J.: A GPS-based methodology to analyze environment-health associations at the trip level: case-crossover analyses of built environments and walking. Am. J. Epidemiol. 184(8), 579–589 (2016)

Kerr, J., Duncan, S., Schipperjin, J.: Using global positioning systems in health research: a practical approach to data collection and processing. Am. J. Prev. Med. 41(5), 532–540 (2011)

Saelens, B.E., Vernez Moudon, A., Kang, B., Hurvitz, P.M., Zhou, C.: Relation between higher physical activity and public transit use. Am. J. Public Health 104(5), 854–859 (2014)

Rundle, A.G., Sheehan, D.M., Quinn, J.W., Bartley, K., Eisenhower, D., Bader, M.M., Lovasi, G.S., Neckerman, K.M.: Using GPS data to study neighborhood walkability and physical activity. Am. J. Prev. Med. 50(3), e65–e72 (2016)

Sadler, R.C., Gilliland, J.A.: Comparing children’s GPS tracks with geospatial proxies for exposure to junk food. Spat. Spat. Temp. Epidemiol. 14, 55–61 (2015)

Canzian, L., Musolesi, M.: Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1293–1304. ACM (2015)

Marchetti, S., Giusti, C., Pratesi, M., Salvati, N., Giannotti, F., Pedreschi, D., Rinzivillo, S., Pappalardo, L., Gabrielli, L.: Small area model-based estimators using big data sources. J. Off. Stat. 31(2), 263–281 (2015)

Smith, C., Quercia, D., Capra, L.: Finger on the pulse: identifying deprivation using transit flow analysis. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 683–692. ACM (2013)

Lathia, N., Quercia, D., Crowcroft, J.: The hidden image of the city: sensing community well-being from urban mobility. In: International Conference on Pervasive Computing, pp. 91–98. Springer (2012)

Robinson, A.I., Carnes, F., Oreskovic, N.M.: Spatial analysis of crime incidence and adolescent physical activity. Prev. Med. 85, 74–77 (2016)

Ariel, B., Partridge, H.: Predictable policing: measuring the crime control benefits of hotspots policing at bus stops. J. Quant. Criminol. 33(4), 809–833 (2017)

Spinsanti, L., Berlingerio, M., Pappalardo, L.: Mobility and Geo-Social Networks, pp. 315–333. Cambridge University Press, Cambridge (2013). https://doi.org/10.1017/CBO9781139128926.017

Olteanu, A., Castillo, C., Diaz, F., Kiciman, E.: Social data: biases, methodological pitfalls, and ethical boundaries. Front. Big Data 2, 13 (2019)

Rost, M., Barkhuus, L., Cramer, H., Brown, B.: Representation and communication: challenges in interpreting large social media datasets. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 357–362. ACM (2013)

Eichstaedt, J.C., Schwartz, H.A., Kern, M.L., Park, G., Labarthe, D.R., Merchant, R.M., Jha, S., Agrawal, M., Dziurzynski, L.A., Sap, M., et al.: Psychological language on twitter predicts county-level heart disease mortality. Psychol. Sci. 26(2), 159–169 (2015)

De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. ICWSM 13, 1–10 (2013)

Signorini, A., Segre, A.M., Polgreen, P.M.: The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PLoS ONE 6(5), e19467 (2011)

Paul, M.J., Dredze, M., Broniatowski, D.: Twitter improves influenza forecasting. PLoS Curr. 6, 12 (2014)

Lampos, V., Cristianini, N.: Tracking the flu pandemic by monitoring the social web. In: 2010 2nd International Workshop on Cognitive Information Processing, pp. 411–416. IEEE (2010)

Lampos, V., Cristianini, N.: Nowcasting events from the social web with statistical learning. ACM Trans. Intell. Syst. Technol. 3(4), 72 (2012)

Chen, X., Yang, X.: Does food environment influence food choices? A geographical analysis through “tweets”. Appl. Geogr. 51, 82–89 (2014)

Llorente, A., Garcia-Herranz, M., Cebrian, M., Moro, E.: Social media fingerprints of unemployment. PLoS ONE 10(5), e0128692 (2015)

Antenucci, D., Cafarella, M., Levenstein, M., Ré, C., Shapiro, M.D.: Using social media to measure labor market flows. Technical report. National Bureau of Economic Research (2014)

Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

Bar-Haim, R., Dinur, E., Feldman, R., Fresko, M., Goldstein, G. Identifying and following expert investors in stock microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp 1310–1319. Association for Computational Linguistics (2011)

De Choudhury, M., Sundaram, H., John, A., Seligmann, D.D.: Can blog communication dynamics be correlated with stock market activity? In: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, pp. 55–60. ACM (2008)

Cresci, S., Lillo, F., Regoli, D., Tardelli, S., Tesconi, M.: \$FAKE: Evidence of spam and bot activity in stock microblogs on Twitter. In: Proceedings of the 12th International Conference on Web and Social Media (ICWSM’18), pp. 580–583. AAAI (2018)

Cresci, S., Lillo, F., Regoli, D., Tardelli, S., Tesconi, M.: Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on twitter. ACM Trans. Web (TWEB) 13(2), 11 (2019)

Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., Tesconi, M.: Predictability or early warning: using social media in modern emergency response. IEEE Internet Comput. 20(6), 4–6 (2016)

Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., Cebrian, M.: Rapid assessment of disaster damage using social media activity. Sci. Adv. 2(3), e1500779 (2016)

Avvenuti, M., Cresci, S., La Polla, M.N., Meletti, C., Tesconi, M.: Nowcasting of earthquake consequences using big social data. IEEE Internet Comput. 6, 37–45 (2017)

Mendoza, M., Poblete, B., Valderrama, I.: Nowcasting earthquake damages with twitter. EPJ Data Sci. 8(1), 3 (2019)

Avvenuti, M., Cresci, S., Del Vigna, F., Tesconi, M.: Impromptu crisis mapping to prioritize emergency response. Computer 49(5), 28–37 (2016)

Avvenuti, M., Cresci, S., Del Vigna, F., Fagni, T., Tesconi, M.: CrisMap: a big data crisis mapping system based on damage detection and geoparsing. Inf. Syst. Front. 1, 1–19 (2018)

Preis, T., Moat, H.S., Bishop, S.R., Treleaven, P., Stanley, H.E.: Quantifying the digital traces of hurricane sandy on flickr. Sci. Rep. 3, 3141 (2013)

Chen, X., Cho, Y, Jang, S.Y.: Crime prediction using twitter sentiment and weather. In: 2015 Systems and Information Engineering Design Symposium, pp. 63–68. IEEE (2015)

Al Boni, M., Gerber, M.S.: Predicting crime with routine activity patterns inferred from social media. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 001233–001238. IEEE (2016)

Kadar, C., Brüngger, R.R., Pletikosa, I.: Measuring ambient population from location-based social networks to describe urban crime. In: International Conference on Social Informatics, pp. 521–535. Springer (2017)

Chen, F., Neill, D.B.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1166–1175. ACM (2014)

Nobles, M., Neill, D.B., Flaxman, S.: Predicting and Preventing Emerging Outbreaks of Crime (2014)

Neill, D.B., Gorr, W.L.: Detecting and preventing emerging epidemics of crime. Adv. Dis. Surveill. 4(13), 18 (2007)

Colleoni, E., Rozza, A., Arvidsson, A.: Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. J. Commun. 64(2), 317–332 (2014)

Goh, T.T., Xin, Z., Jin, D.: Habit formation in social media consumption: a case of political engagement. Behav. Inf. Technol. 38(3), 273–288 (2019)

Ferrara, E.: Manipulation and abuse on social media. ACM SIGWEB Newsl. 2015(Spring), 4 (2015)

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp 963–972 (2017)

Goldstein, B.A., Navar, A.M., Pencina, M.J., Ioannidis, J.: Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 24(1), 198–208 (2017)

Wilson, P.W., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H., Kannel, W.B.: Prediction of coronary heart disease using risk factor categories. Circulation 97(18), 1837–1847 (1998)

Sultana, J., Leal, I., de Wilde, M., de Ridder, M., van der Lei, J., Sturkenboom, M., et al.: Identifying data elements to measure frailty in a dutch nationwide electronic medical record database for use in postmarketing safety evaluation: an exploratory study. Drug Saf. 12, 1–7 (2019)

Ghaderighahfarokhi, S., Sadeghifar, J.: A model to predict low birth weight infants and affecting factors using data mining techniques. J. Basic Res. Med. Sci. 5(3), 1–8 (2018)

Metzger, M.H., Tvardik, N., Gicquel, Q., Bouvry, C., Poulet, E., Potinet-Pagliaroli, V.: Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a french pilot study. Int. J. Methods Psychiatric Res. 26(2), e1522 (2017)

Mhaskar, H.N., Pereverzyev, S.V., van der Walt, M.D.: A deep learning approach to diabetic blood glucose prediction. Front. Appl. Math. Stat. 3, 14 (2017)

Santillana, M., Nsoesie, E.O., Mekaru, S.R., Scales, D., Brownstein, J.S.: Using clinicians’ search query data to monitor influenza epidemics. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 59(10), 1446 (2014)

Althoff, T., Hicks, J.L., King, A.C., Delp, S.L., Leskovec, J., et al.: Large-scale physical activity data reveal worldwide activity inequality. Nature 547(7663), 336 (2017)

Hayeri, A.: Predicting future glucose fluctuations using machine learning and wearable sensor data. Diabetes (2018). https://doi.org/10.2337/db18-738-P

Leetaru, K.: The GDELT Project. https://www.gdeltproject.org/. Accessed Oct 2019 (2013)

Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., Van Der Goot, E., Halkia, M., Pouliquen, B., Belyaeva, J.: Sentiment analysis in the news. arXiv preprint arXiv:1309.6202 (2013)

Dehghan, A., Montgomery, L., Arciniegas-Mendez, M., Ferman-Guerra, M.: Predicting News Bias (2016)

Grein, T.W., Kamara, K., Rodier, G., Plant, A.J., Bovier, P., Ryan, M.J., Ohyama, T., Heymann, D.L.: Rumors of disease in the global village: outbreak verification. Emerg. Infect. Dis. 6(2), 97 (2000)

Heymann, D.L., Rodier, G.R., et al.: Hot spots in a wired world: Who surveillance of emerging and re-emerging infectious diseases. Lancet. Infect. Dis 1(5), 345–353 (2001)

Brownstein, J.S., Freifeld, C.C., Reis, B.Y., Mandl, K.D.: Surveillance sans frontieres: Internet-based emerging infectious disease intelligence and the healthmap project. PLoS Med. 5(7), e151 (2008)

Wilson, K., Brownstein, J.S.: Early detection of disease outbreaks using the internet. CMAJ 180(8), 829–831 (2009)

Chunara, R., Andrews, J.R., Brownstein, J.S.: Social and news media enable estimation of epidemiological patterns early in the 2010 haitian cholera outbreak. Am. J. Trop. Med. Hyg. 86(1), 39–45 (2012)

Alanyali, M., Moat, H.S., Preis, T.: Quantifying the relationship between financial news and the stock market. Sci. Rep. 3, 3578 (2013)

Lillo, F., Miccichè, S., Tumminello, M., Piilo, J., Mantegna, R.N.: How news affects the trading behaviour of different categories of investors in a financial market. Quant. Finance 15(2), 213–229 (2015)

Kleinschmit, D., Sjöstedt, V.: Between science and politics: Swedish newspaper reporting on forests in a changing climate. Environ. Sci. Policy 35, 117–127 (2014)

Boykoff, M.T.: Lost in translation? united states television news coverage of anthropogenic climate change, 1995–2004. Clim. Change 86(1–2), 1–11 (2008)

Van Aelst, P., De Swert, K.: Politics in the News: Do Campaigns Matter? A Comparison of Political News During Election Periods and Routine Periods in Flanders (Belgium). Walter de Gruyter GmbH & Co, KG, Belgium (2009)

Eurostat Practical Guide for Processing Supermarket Scanner Data (2017)

Griffith, R., O’Connell, M.: The use of scanner data for research into nutrition. Fiscal Stud. 30(3–4), 339–365 (2009)

Baron, S., Lock, A.: The challenges of scanner data. J. Oper. Res. Soc. 46(1), 50–61 (1995)

Eurostat Practical Guide for Processing Supermarket Scanner Data. https://circabc.europa.eu/sd/a/8e1333df-ca16-40fc-bc6a-1ce1be37247c/Practical-Guide-Supermarket. Accessed Oct 2019 (2017)

Diewert, W.E.: Harmonized indexes of consumer prices: their conceptual foundations (2002)

Magruder, S.: Evaluation of over-the-counter pharmaceutical sales as a possible early warning indicator of human disease. Johns Hopkins Univ. APL Tech. Dig. 24(4), 349–353 (2003)

Bonnet, C., Dubois, P., Réquillart, V.: The dynamics of satured fat consumption in france. Technical. report. Toulouse mimeo (2008)

Griffith, R., Leibtag, E., Leicester, A., Nevo, A.: Consumer shopping behavior: how much do consumers save? J. Econ. Perspect. 23(2), 99–120 (2009)

Janssen, A., Parslow, E.: Pregnancy and alcohol purchases: evidence from scanner data. Avail. SSRN 3446559, 12 (2019)

Rider, J., Berck, P., Villas-Boas, S.B.: Eating Healthy in Lean Times: The Relationship Between Unemployment and Grocery Purchasing Patterns (2012)

Van der Grient, H.A., de Haan, J.: The use of supermarket scanner data in the dutch cpi. In: Joint ECE/ILO Workshop on Scanner Data, vol. 10 (2010)

Silver, M., Heravi, S.: Scanner data and the measurement of inflation. Econ. J. 111(472), 383–404 (2001)

Pennacchioli, D., Coscia, M., Rinzivillo, S., Giannotti, F., Pedreschi, D.: The retail market as a complex system. EPJ Data Sci. 3(1), 33 (2014)

Sobolevsky, S., Massaro, E., Bojic, I., Arias, J.M., Ratti, C.: Predicting regional economic indices using big data of individual bank card transactions. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 1313–1318. IEEE (2017)

Panzone, L.A., Wossink, A., Southerton, D.: The design of an environmental index of sustainable food consumption: a pilot study using supermarket data. Ecol. Econ. 94, 44–55 (2013)

Gadema, Z., Oglethorpe, D.: The use and usefulness of carbon labelling food: a policy perspective from a survey of uk supermarket shoppers. Food Policy 36(6), 815–822 (2011)

Brancoli, P., Rousta, K., Bolton, K.: Life cycle assessment of supermarket food waste. Resour. Conserv. Recycl. 118, 39–46 (2017)

Scholz, K., Eriksson, M., Strid, I.: Carbon footprint of supermarket food waste. Resour. Conserv. Recycl. 94, 56–65 (2015)

Goel, S., Hofman, J.M., Lahaie, S., Pennock, D.M., Watts, D.J.: Predicting consumer behavior with web search. Proc. Nat. Acad. Sci. 107(41), 17486–17490 (2010)

Cooper, C.P., Mallon, K.P., Leadbetter, S., Pollack, L.A., Peipins, L.A.: Cancer internet search activity on a major search engine, united states 2001–2003. J. Med. Internet Res. 7(3), e36 (2005)

Polgreen, P.M., Chen, Y., Pennock, D.M., Nelson, F.D., Weinstein, R.A.: Using internet searches for influenza surveillance. Clin. Infect. Dis. 47(11), 1443–1448 (2008)

Hulth, A., Rydevik, G., Linde, A.: Web queries as a source for syndromic surveillance. PLoS ONE 4(2), e4378 (2009)

Yuan, Q., Nsoesie, E.O., Lv, B., Peng, G., Chunara, R., Brownstein, J.S.: Monitoring influenza epidemics in china with search query from baidu. PLoS ONE 8(5), e64323 (2013)

Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012 (2009)

Google: Google Flu Trends. http://www.google.org/flutrends. Accessed Oct 2019 (2008)

Nsoesie, E., Mararthe, M., Brownstein, J.: Forecasting peaks of seasonal influenza epidemics. PLoS Curr. 5, 8 (2013)

Yang, W., Lipsitch, M., Shaman, J.: Inference of seasonal and pandemic influenza transmission dynamics. Proc. Nat. Acad. Sci. 112(9), 2723–2728 (2015)

Wilson, N., Mason, K., Tobias, M., Peacey, M., Huang, Q., Baker, M.: Interpreting “google flu trends” data for pandemic h1n1 influenza: the new zealand experience. Eurosurveillance 14(44), 19386 (2009)

Chan, E.H., Sahai, V., Conrad, C., Brownstein, J.S.: Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Neglect. Trop. Dis. 5(5), e1206 (2011)

Althouse, B.M., Ng, Y.Y., Cummings, D.A.: Prediction of dengue incidence using search query surveillance. PLoS Neglect. Trop. Dis. 5(8), e1258 (2011)

Dukic, V.M., David, M.Z., Lauderdale, D.S.: Internet queries and methicillin-resistant staphylococcus aureus surveillance. Emerg. Infect. Dis. 17(6), 1068 (2011)

Ocampo, A.J., Chunara, R., Brownstein, J.S.: Using search queries for malaria surveillance, Thailand. Malaria J. 12(1), 390 (2013)

Yang, A.C., Tsai, S.J., Huang, N.E., Peng, C.K.: Association of internet search trends with suicide death in taipei city, taiwan, 2004–2009. J. Affect. Disord. 132(1–2), 179–184 (2011)

McCarthy, M.J.: Internet monitoring of suicide risk in the population. J. Affect. Disord. 122(3), 277–279 (2010)

Kristoufek, L., Moat, H.S., Preis, T.: Estimating suicide occurrence statistics using google trends. EPJ Data Sci. 5(1), 32 (2016)

Adler, N., Cattuto, C., Kalimeri, K., Paolotti, D., Tizzoni, M., Verhulst, S., Yom-Tov, E., Young, A.: How search engine data enhance the understanding of determinants of suicide in india and inform prevention: observational study. J. Med. Internet Res. 21(1), e10179 (2019). https://doi.org/10.2196/10179

Ettredge, M., Gerdes, J., Karuga, G.: Using web-based search data to predict macroeconomic statistics. Commun. ACM 48(11), 87–92 (2005)

Askitas, N., Zimmermann, K.: Google econometrics and unemployment forecasting. Appl. Econ. Quart. 55(2), 107–120 (2009)

Francesco/FD D, Marcucci J “google it!” forecasting the us unemployment rate with a google job search index. Mpra paper. University Library of Munich, Germany. https://EconPapers.repec.org/RePEc:pra:mprapa:18248 (2009)

Suhoy, T., et al.: Query indices and a 2008 downturn: Israeli data. Technical report. Bank of Israel (2009)

Baker, S., Fradkin, A., et al.: What drives job search? evidence from google search data. Discussion Papers, pp. 10–20 (2011)

McLaren, N., Shanbhogue, R.: Using internet search data as economic indicators. Bank Engl. Quart. Bull. 51(2), 134–140 (2011)

Choi, H., Varian, H.: Predicting initial claims for unemployment benefits. Google Inc, pp. 1–5 (2009)

Choi, H., Varian, H.: Predicting the present with google trends. Econ. Rec. 88, 2–9 (2012)

Koop, G., Onorante, L.: Macroeconomic nowcasting using google probabilities. In: First International Conference on Advanced Research Methods and Analytics, CARMA2016. https://doi.org/10.4995/CARMA2016.2016.4213 (2016)

Guzman, G.: Internet search behavior as an economic forecasting tool: the case of inflation expectations. J. Econ. Soc. Meas. 36(3), 119–167 (2011)

Preis, T., Reith, D., Stanley, H.E.: Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368(1933), 5707–5719 (2010). https://doi.org/10.1098/rsta.2010.0284

Preis, T., Moat, H.S., Stanley, H.E.: Quantifying trading behavior in financial markets using google trends. Sci. Rep. (2013). https://doi.org/10.1038/srep01684

Curme, C., Preis, T., Stanley, H.E., Moat, H.S.: Quantifying the semantics of search behavior before stock market moves. Proc. Natl. Acad. Sci. 111(32), 11600–11605 (2014). https://doi.org/10.1073/pnas.1324054111

Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., Weber, I.: Web search queries can predict stock market volumes. PLoS ONE 7(7), e40014 (2012)

Moat, H.S., Curme, C., Avakian, A., Kenett, D.Y., Stanley, H.E., Preis, T.: Quantifying wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013)

Qi, H., Manrique, P., Johnson, D., Restrepo, E., Johnson, N.F.: Open source data reveals connection between online and on-street protest activity. EPJ Data Sci. 5(1), 18 (2016a)

Qi, H., Manrique, P., Johnson, D., Restrepo, E., Johnson, N.F.: Association between volume and momentum of online searches and real-world collective unrest. Results Phys. 6, 414–419 (2016b)

Chykina, V., Crabtree, C.: Using google trends to measure issue salience for hard-to-survey populations. Socius 4, 2378023118760414 (2018)

Reilly, S., Richey, S., Taylor, J.B.: Using google search data for state politics research: an empirical validity test using roll-off data. State Polit. Policy Quart. 12(2), 146–159 (2012)

Kleemann, F., Voß, G.G., Rieder, K.: Un (der) paid innovators: the commercial utilization of consumer work through crowdsourcing. Sci. Technol. Innov. Stud. 4(1), 5–26 (2008)

Behrend, T.S., Sharek, D.J., Meade, A.W., Wiebe, E.N.: The viability of crowdsourcing for survey research. Behav. Res. Methods 43(3), 800 (2011)

Paolotti, D., Carnahan, A., Colizza, V., Eames, K., Edmunds, J., Gomes, G., Koppeschaar, C., Rehn, M., Smallenburg, R., Turbelin, C., et al.: Web-based participatory surveillance of infectious diseases: the influenzanet participatory surveillance experience. Clin. Microbiol. Infect. 20(1), 17–21 (2014)

Dalton, C., Durrheim, D., Fejsa, J., Francis, L., Carlson, S., d’Espaignet, E.T., Tuyl, F., et al.: Flutracking: a weekly australian community online survey of influenza-like illness in 2006, 2007 and 2008. Commun. Dis. Intell. Quart. Rep. 33(3), 316 (2009)

Smolinski, M.S., Crawley, A.W., Baltrusaitis, K., Chunara, R., Olsen, J.M., Wójcik, O., Santillana, M., Nguyen, A., Brownstein, J.S.: Flu near you: crowdsourced symptom reporting spanning 2 influenza seasons. Am. J. Public Health 105(10), 2124–2130 (2015)

Hashemian, M., Knowles, D., Calver, J., Qian, W., Bullock, MC., Bell, S., Mandryk, R.L., Osgood, N., Stanley, K.G.: iepi: an end to end solution for collecting, conditioning and utilizing epidemiologically relevant data. In: Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare. pp. 3–8. ACM (2012)

Madan, A., Cebrian, M., Moturu, S., Farrahi, K., et al.: Sensing the “health state” of a community. IEEE Pervasive Comput. 11(4), 36–45 (2011)

Martinucci, I., Natilli, M., Lorenzoni, V., Pappalardo, L., Monreale, A., Turchetti, G., Pedreschi, D., Marchi, S., Barale, R., de Bortoli, N.: Gastroesophageal reflux symptoms among italian university students: epidemiology and dietary correlates using automatically recorded transactions. BMC Gastroenterol. 18(1), 116 (2018)

Green, T.C., Huang, R., Wen, Q., Zhou, D.: Crowdsourced employer reviews and stock returns. J. Financ. Econ. 2, 18 (2019)

Dabirian, A., Kietzmann, J., Diba, H.: A great place to work!? understanding crowdsourced employer branding. Bus. Horiz. 60(2), 197–205 (2017)

Könsgen, R., Schaarschmidt, M., Ivens, S., Munzel, A.: Finding meaning in contradiction on employee review sites-effects of discrepant online reviews on job application intentions. J. Interact. Mark. 43, 165–177 (2018)

Tingzon, I., Orden, A., Sy, S., Sekara, V., Weber, I., Fatehkia, M., Herranz, M.G., Kim, D.: Mapping Poverty in the Philippines Using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information (missing year)

OpenStreetMap Community Openstreetmap. https://www.openstreetmap.org/#map=5/42.088/12.564. Accessed Oct 2019 (2004)

Piaggesi, S., Gauvin, L., Tizzoni, M., Cattuto, C., Adler, N., Verhulst, S., Young, A., Price, R., Ferres, L., Panisson, A.: Predicting city poverty using satellite imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 90–96 (2019)

Abelson, B., Varshney, K.R., Sun, J.: Targeting direct cash transfers to the extremely poor. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1563–1572. ACM (2014)

Hersman, E., Okolloh, O., Rotich, J., Kobia, D.: Ushahidi. https://www.ushahidi.com. Accessed Oct 2019 (2008)

Meier, P.: Digital Humanitarians: How Big Data is Changing the Face of Humanitarian Response. Routledge, London (2015)

European Commission Citizens’ Observatories. https://www.ushahidi.com. Accessed Oct 2019 (2016)

Grainger, A.: Citizen observatories and the new earth observation science. Remote Sens. 9(2), 153 (2017)

Schneider, P., Castell, N., Vogt, M., Lahoz W., Bartonova A.: Making sense of crowdsourced observations: data fusion techniques for real-time mapping of urban air quality. In: EGU General Assembly Conference Abstracts, p. 17 (2015)

Meier, F., Fenner, D., Grassmann, T., Jänicke, B., Otto, M., Scherer, D.: Challenges and benefits from crowd sourced atmospheric data for urban climate research using Berlin, Germany, as testbed. In: ICUC9–9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment (2015)

Chapman, L., Bell, C., Bell, S.: Can the crowdsourcing data paradigm take atmospheric science to a new level? a case study of the urban heat island of london quantified using netatmo weather stations. Int. J. Climatol. 37(9), 3597–3605 (2017)

Lea, S.G., D’Silva, E., Asok, A.: Women’s strategies addressing sexual harassment and assault on public buses: an analysis of crowdsourced data. Crime Prev. Commun. Saf. 19(3–4), 227–239 (2017)

Gosselt, J.F., Van Hoof, J.J., Gent, B.S., Fox, J.P.: Violent frames: analyzing internet movie database reviewers’ text descriptions of media violence and gender differences from 39 years of us action, thriller, crime, and adventure movies. Int. J. Commun. 9, 547–567 (2015)

Ozkan, T., Worrall, J.L., Zettler, H.: Validating media-driven and crowdsourced police shooting data: a research note. J. Crime Justice 41(3), 334–345 (2018)

Avvenuti, M., Bellomo, S., Cresci, S., La Polla, M.N., Tesconi, M.: Hybrid crowdsensing: A novel paradigm to combine the strengths of opportunistic and participatory crowdsensing. In: Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp. 1413–1421 (2017)

Dennis, J.: United by what divides us: 38 degrees and the eu referendum. In: EU Referendum Analysis 2016: Media, Voters and the Campaign. Bournemouth University, p. 100 (2016)

Yasseri, T., Bright, J.: Wikipedia traffic data and electoral prediction: towards theoretically informed models. EPJ Data Sci. 5(1), 22 (2016)

Gellers, J.C.: Crowdsourcing global governance: sustainable development goals, civil society, and the pursuit of democratic legitimacy. Int. Environ. Agreements Polit. Law Econ. 16(3), 415–432 (2016)

Burger, R.: Aristotle’s Dialogue with Socrates: On the “Nicomachean Ethics”. University of Chicago Press, Chicago (2009)

Diener, E.: Subjective well-being. Psychol. Bull. 95(3), 542 (1984)

Veenhoven, R.: How do we assess how happy we are? tenets, implications and tenability of three theories. Happiness Econ. Polit. 25, 45–69 (2009)

Alesina, A., Di Tella, R., MacCulloch, R.: Inequality and happiness: are europeans and americans different? J. Public Econ. 88(9–10), 2009–2042 (2004)

Watson, D., Clark, L.A., Tellegen, A.: Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Pers. Soc. Psychol. 54(6), 1063 (1988)

Watson, D., Clark, L.A.: The Panas-x: Manual for the Positive and Negative Affect Schedule-Expanded Form. Psychology Publications, New York (1999)

Diener, E., Oishi, S., Tay, L.: Advances in subjective well-being research. Nat. Hum. Behav. 2, 1 (2018)

Hudson, N.W., Anusic, I., Lucas, R.E., Donnellan, M.B.: Comparing the reliability and validity of global self-report measures of subjective well-being with experiential day reconstruction measures. Assessment 2, 26 (2017)

Anusic, I., Schimmack, U.: Stability and change of personality traits, self-esteem, and well-being: introducing the meta-analytic stability and change model of retest correlations. J. Pers. Soc. Psychol. 110(5), 766 (2016)

Tay, L., Chan, D., Diener, E.: The metrics of societal happiness. Soc. Indic. Res. 117(2), 577–600 (2014)

Deaton, A.: Income, health, and well-being around the world: evidence from the gallup world poll. J. Econ. Perspect. 22(2), 53–72 (2008)

Easterlin, R.A., Angelescu, L.: Happiness and growth the world over: time series evidence on the happiness-income paradox. Technical report. Institute of Labor Economics (IZA) (2009)

Kahneman, D., Deaton, A.: High income improves evaluation of life but not emotional well-being. Proc. Nat. Acad. Sci. 107(38), 16489–16493 (2010)

Frijters, P., Beatton, T.: The mystery of the u-shaped relationship between happiness and age. J. Econ. Behav. Organ. 82(2–3), 525–542 (2012)

Stevenson, B., Wolfers, J.: The paradox of declining female happiness. Am. Econ. J. Econ. Policy 1(2), 190–225 (2009)

Deaton, A., Stone, A.A.: Understanding context effects for a measure of life evaluation: how responses matter. Oxf. Econ. Pap. 68(4), 861–870 (2016)

Yap, S.C., Wortman, J., Anusic, I., Baker, S.G., Scherer, L.D., Donnellan, M.B., Lucas, R.E.: The effect of mood on judgments of subjective well-being: nine tests of the judgment model. J. Pers. Soc. Psychol. 113(6), 939 (2017)

Lucas, R.E., Lawless, N.M.: Does life seem better on a sunny day? Examining the association between daily weather conditions and life satisfaction judgments. J. Pers. Soc. Psychol. 104(5), 872 (2013)

Kahneman, D., Diener, E., Schwarz, N.: Well-Being: Foundations of Hedonic Psychology. Russell Sage Foundation, New York (1999)

Kahneman, D., Krueger, A.B., Schkade, D.A., Schwarz, N., Stone, A.A.: A survey method for characterizing daily life experience: the day reconstruction method. Science 306(5702), 1776–1780 (2004)

Courvoisier, D.S., Eid, M., Lischetzke, T.: Compliance to a cell phone-based ecological momentary assessment study: the effect of time and personality characteristics. Psychol. Assess. 24(3), 713 (2012)

Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008)

Eid, M.E., Diener, E.E.: Handbook of Multimethod Measurement in Psychology. American Psychological Association, New York (2006)

Diener, E., Seligman, M.E.: Beyond money: toward an economy of well-being. Psychol. Sci. Public Interest 5(1), 1–31 (2004)

Costa, P.T., McCrae, R.R.: Influence of extraversion and neuroticism on subjective well-being: happy and unhappy people. J. Pers. Soc. Psychol. 38(4), 668 (1980)

Zweig, J.S.: Are women happier than men? Evidence from the Gallup World Poll. J. Happiness Stud. 16(2), 515–541 (2015)

Deaton, A.S., Tortora, R.: People in Sub-Saharan Africa rate their health and health care among the lowest in the world. Health Aff. 34(3), 519–527 (2015)

Veenhoven, R., Ehrhardt, J.: The cross-national pattern of happiness: test of predictions implied in three theories of happiness. Soc. Indic. Res. 34(1), 33–68 (1995)

Cuñado, J., de Gracia, F.P.: Does education affect happiness? Evidence for spain. Soc. Indic. Res. 108(1), 185–196 (2012)

Nikolaev, B.: Does higher education increase hedonic and eudaimonic happiness? J. Happiness Stud. 19(2), 483–504 (2018)

Rehdanz, K., Maddison, D.: Climate and happiness. Ecol. Econ. 52(1), 111–125 (2005)

Hudson, J.: Institutional trust and subjective well-being across the eu. Kyklos 59(1), 43–62 (2006)

Hayo, B. Happiness in Eastern Europe. Marburg Economic Working Paper No 12 (2004)

Ferrer-i Carbonell, A., Gowdy, J.M.: Environmental degradation and happiness. Ecol. Econ. 60(3), 509–516 (2007)

Gardner, J., Oswald, A.J.: Money and mental wellbeing: a longitudinal study of medium-sized lottery wins. J. Health Econ. 26(1), 49–60 (2007)

Tay, L., Zyphur, M., Batz, C.: Income and Subjective Well-Being: Review, Synthesis, and Future Research. Handbook of Well-Being. DEF Publishers, Salt Lake City (2017)

Wijngaards, I., Hendriks, M., Burger, M.J.: Steering towards happiness: an experience sampling study on the determinants of happiness of truck drivers. Transp. Res. Part A Policy Pract. 128, 131–148 (2019)

van der Zwan, P., Hessels, J., Burger, M.: Happy free willies? Investigating the relationship between freelancing and subjective well-being. Small Bus. Econ. 8, 1–17 (2019)

Blanchflower, D.G., Bell, D.N., Montagnoli, A., Moro, M.: The happiness trade-off between unemployment and inflation. J. Money Credit Bank. 46(S2), 117–141 (2014)

Knabe, A., Schöb, R., Weimann, J.: Partnership, gender, and the well-being cost of unemployment. Soc. Indic. Res. 129(3), 1255–1275 (2016)

Brulé, G., Veenhoven, R.: Why are Latin Europeans less happy? Polyphonic Anthropology-Theoretical and Empirical Cross-Cultural Fieldwork. The Impact of Hierarchy. InTech (2012)

Bartolini, S., Mikucka, M., Sarracino, F.: Money, trust and happiness in transition countries: evidence from time series. Soc. Indic. Res. 130(1), 87–106 (2017)

Ott, J.C.: Good governance and happiness in nations: technical quality precedes democracy and quality beats size. J. Happiness Stud. 11(3), 353–368 (2010)

Fowler, J.H., Christakis, N.A.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study. BMJ 337, a2338 (2008)

Luhmann, M.: Using big data to study subjective well-being. Curr. Opin. Behav. Sci. 18, 28–33 (2017)

Nederhof, A.J.: Methods of coping with social desirability bias: a review. Eur. J. Soc. Psychol. 15(3), 263–280 (1985)

Quercia, D., Ellis, J., Capra, L., Crowcroft, J.: Tracking gross community happiness from tweets. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 965–968. ACM (2012)

Bollen, J., Gonçalves, B., van de Leemput, I., Ruan, G.: The happiness paradox: your friends are happier than you. EPJ Data Sci. 6(1), 4 (2017)

Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of hlt/emnlp on Interactive Demonstrations. Association for Computational Linguistics, pp. 34–35 (2005)

Bollen, J., Gonçalves, B., Ruan, G., Mao, H.: Happiness is assortative in online social networks. Artif. Life 17(3), 237–251 (2011)

Kramer, A.D., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks. In: Proceedings of the National Academy of Sciences, p. 201320040 (2014)

Lim, K.H., Lee, K.E., Kendal, D., Rashidi, L., Naghizade, E., Winter, S., Vasardani, M.: The grass is greener on the other side: Understanding the effects of green spaces on twitter user sentiments. In: Companion of the The Web Conference 2018 on The Web Conference 2018, International World Wide Web Conferences Steering Committee, pp. 275–282 (2018)

Mitchell, L., Frank, M.R., Harris, K.D., Dodds, P.S., Danforth, C.M.: The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE 8(5), e64417 (2013)

Golder, S.A., Macy, M.W.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011)

Lansdall-Welfare, T., Lampos, V., Cristianini, N.: Nowcasting the mood of the nation. Significance 9(4), 26–28 (2012)

Cresci, S., La Polla, M.N., Mazza, M., Tesconi, M., Del Vigna, F.: #selfie: mapping the phenomenon. Consiglio Nazioonale delle Ricerche IIT TR-08/2016 Technical Report (2016)

Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: twitter sentiment and socio-economic phenomena. ICWSM 11, 450–453 (2011)

Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., Danforth, C.M.: Temporal patterns of happiness and information in a global social network: hedonometrics and twitter. PLoS ONE 6(12), e26752 (2011)

Iacus, S.M., Porro, G., Salini, S., Siletti, E.: Social networks, happiness and health: from sentiment analysis to a multidimensional indicator of subjective well-being. arXiv preprint arXiv:1512.01569 (2015)

Ceron, A., Curini, L., Iacus, S.M.: Social Media e Sentiment Analysis: L’evoluzione dei fenomeni sociali attraverso la Rete, vol. 9. Springer, New York (2014)

Ceron, A., Curini, L., Iacus, S.M.: ISA: a fast, scalable and accurate algorithm for sentiment analysis of social media content. Inf. Sci. 367, 105–124 (2016)

Curini, L., Iacus, S., Canova, L.: Measuring idiosyncratic happiness through the analysis of twitter: an application to the italian case. Soc. Indic. Res. 121(2), 525–542 (2015)

Durahim, A.O., Coşkun, M.: # iamhappybecause: gross national happiness through twitter analysis and big data. Technol. Forecast. Soc. Change 99, 92–105 (2015)

Coviello, L., Sohn, Y., Kramer, A.D., Marlow, C., Franceschetti, M., Christakis, N.A., Fowler, J.H.: Detecting emotional contagion in massive social networks. PLoS ONE 9(3), e90315 (2014)

Algan, Y., Beasley, E., Guyot, F., Higa, K., Murtin, F., Senik, C., et al. Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the United States. OECD Statistics Working Papers 2016 (2016)

Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)

Staiano, J., Lepri, B., Aharony, N., Pianesi, F., Sebe, N., Pentland, A.: Friends don’t lie: inferring personality traits from social network structure. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 321–330. ACM (2012)

Li, G., Zheng, Y., Fan, J., Wang, J., Cheng, R.: Crowdsourced data management: overview and challenges. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1711–1716. ACM (2017)

Lathia, N., Sandstrom, G.M., Mascolo, C., Rentfrow, P.J.: Happier people live more active lives: using smartphones to link happiness and physical activity. PLoS ONE 12(1), e0160589 (2017)

Asai, A., Evensen, S., Golshan, B., Halevy, A., Li, V., Lopatenko, A., Stepanov, D., Suhara, Y., Tan, W.C., Xu, Y. Happydb: a corpus of 100,000 crowdsourced happy moments. arXiv preprint arXiv:1801.07746 (2018)

Bogomolov, A., Lepri, B., Pianesi, F.: Happiness recognition from mobile phone data. In: Social Computing (SocialCom), 2013 International Conference on Social Computing, pp. 790–795. IEEE (2013)

Goldberg, L.R.: An alternative “description of personality”: the big-five factor structure. J. Pers. Soc. Psychol. 59(6), 1216 (1990)

Carlquist, E., Nafstad, H.E., Blakar, R.M., Ulleberg, P., Delle Fave, A., Phelps, J.M.: Well-being vocabulary in media language: an analysis of changing word usage in Norwegian newspapers. J. Positive Psychol. 12(2), 99–109 (2017)

Seligman, M.E.: Flourish: A New Understanding of Happiness and Well-Being and How to Achieve Them. Nicholas Brealey, Boston (2011)

Greco, M., Stenner, P.: Happiness and the art of life: diagnosing the psychopolitics of wellbeing. Health Cult. Soc. 5(1), 1–19 (2013)

Coulton, C.J., Goerge, R., Putnam-Hornstein, E., de Haan, B.: Harnessing Big Data for Social Good: A Grand Challenge for Social Work, pp. 1–20. American Academy of Social Work and Social Welfare, Cleveland (2015)

Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., Oliver, N.: The tyranny of data? The bright and dark sides of data-driven decision-making for social good. In: Transparent Data Mining for Big and Small Data, pp. 3–24. Springer (2017)

Floridi, L., Taddeo, M.: What is data ethics? The Royal Society (2016)

Hand, D.J.: Aspects of data ethics in a changing world: where are we now? Big Data 6(3), 176–190 (2018)