Why do scientists sometimes avoid using the word happiness when referring to subjective well-being group of answer choices?

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

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

    Google Scholar 

  3. 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)

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

    Google Scholar 

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

    Google Scholar 

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

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

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

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

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

    Google Scholar 

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

    Google Scholar 

  12. 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)

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

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

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

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  26. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  35. 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)

    Google Scholar 

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

    Google Scholar 

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

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

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

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

    Google Scholar 

  41. 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)

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

  43. 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)

    Google Scholar 

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

    Google Scholar 

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

  46. 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)

  47. 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)

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

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

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

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

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

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

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

    Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

  58. 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)

    Google Scholar 

  59. 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

    Article  Google Scholar 

  60. 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

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

  62. 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

    Book  Google Scholar 

  63. 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)

    Google Scholar 

  64. 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)

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  68. 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)

    Google Scholar 

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

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

    Google Scholar 

  71. 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)

  72. 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

  73. 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)

    Google Scholar 

  74. 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)

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

    MathSciNet  MATH  Google Scholar 

  76. 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)

    Google Scholar 

  77. 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)

    Google Scholar 

  78. 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)

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

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

    Google Scholar 

  81. 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)

  82. 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)

  83. 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)

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

  85. 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

    Article  Google Scholar 

  86. 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)

  87. 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)

    Google Scholar 

  88. 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)

  89. 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)

    Google Scholar 

  90. 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)

    Google Scholar 

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

    Google Scholar 

  92. 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)

    Google Scholar 

  93. 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)

    Google Scholar 

  94. 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)

    Google Scholar 

  95. 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)

    Google Scholar 

  96. 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)

  97. 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)

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

    Google Scholar 

  99. 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)

    Google Scholar 

  100. 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)

    Google Scholar 

  101. 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)

    Google Scholar 

  102. 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)

  103. 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)

    Google Scholar 

  104. 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)

    Google Scholar 

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

    Google Scholar 

  106. 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)

    Google Scholar 

  107. 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)

    Google Scholar 

  108. 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

    Article  Google Scholar 

  109. 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)

    Google Scholar 

  110. 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)

    Google Scholar 

  111. 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)

    Google Scholar 

  112. 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)

    Google Scholar 

  113. 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)

    Google Scholar 

  114. 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)

  115. 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)

    Google Scholar 

  116. 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)

  117. 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)

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

    Google Scholar 

  119. 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)

    Google Scholar 

  120. 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

    Book  Google Scholar 

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

    Google Scholar 

  122. 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)

  123. 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)

    Google Scholar 

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

    Google Scholar 

  125. 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)

    Google Scholar 

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

    Google Scholar 

  127. 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)

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  131. 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)

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

    Google Scholar 

  133. 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)

  134. 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)

  135. 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)

  136. 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)

    Google Scholar 

  137. 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)

    Google Scholar 

  138. 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)

    Google Scholar 

  139. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  142. 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)

    Google Scholar 

  143. 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)

    Google Scholar 

  144. 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)

  145. 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)

  146. 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)

  147. 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)

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

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

    Google Scholar 

  150. 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)

    Google Scholar 

  151. 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)

    Google Scholar 

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

    Google Scholar 

  153. 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)

  154. 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)

    Google Scholar 

  155. 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)

    Google Scholar 

  156. 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)

    Google Scholar 

  157. 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)

    Google Scholar 

  158. 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)

    Google Scholar 

  159. 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)

    Google Scholar 

  160. 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)

    Google Scholar 

  161. 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)

    Google Scholar 

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

    Article  Google Scholar 

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

  164. 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)

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

  166. 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)

    Google Scholar 

  167. 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)

    Google Scholar 

  168. 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)

    Google Scholar 

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

    Google Scholar 

  170. 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)

    Google Scholar 

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

    Google Scholar 

  172. 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)

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  174. 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)

    Google Scholar 

  175. 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)

    Google Scholar 

  176. Eurostat Practical Guide for Processing Supermarket Scanner Data (2017)

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

    Google Scholar 

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

    MATH  Google Scholar 

  179. 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)

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

  181. 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)

    Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

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

  186. 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)

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

    Google Scholar 

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

    Google Scholar 

  189. 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)

  190. 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)

    Google Scholar 

  191. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  194. 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)

    Google Scholar 

  195. 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)

    Google Scholar 

  196. 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)

    Google Scholar 

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

    Google Scholar 

  198. 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)

    Google Scholar 

  199. 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 Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

  203. 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)

    Google Scholar 

  204. 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)

    Google Scholar 

  205. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  208. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  211. 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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  214. 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)

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

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

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

    Google Scholar 

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

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

    Google Scholar 

  220. 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)

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

    Google Scholar 

  222. 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

    Article  MATH  Google Scholar 

  223. 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

    Article  Google Scholar 

  224. 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

    Article  Google Scholar 

  225. 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)

    Google Scholar 

  226. 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)

    Google Scholar 

  227. 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)

    Google Scholar 

  228. 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)

    Google Scholar 

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

    Google Scholar 

  230. 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)

    Google Scholar 

  231. 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)

    Google Scholar 

  232. 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)

    Google Scholar 

  233. 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)

    Google Scholar 

  234. 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)

    Google Scholar 

  235. 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)

    Google Scholar 

  236. 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)

  237. 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)

    Google Scholar 

  238. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  241. 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)

    Google Scholar 

  242. 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)

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

  244. 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)

  245. 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)

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

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

    Google Scholar 

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

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

    Google Scholar 

  250. 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)

  251. 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)

  252. 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)

    Google Scholar 

  253. 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)

    Google Scholar 

  254. 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)

    Google Scholar 

  255. 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)

    Google Scholar 

  256. 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)

  257. 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)

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

    Google Scholar 

  259. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  264. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  267. 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)

    Google Scholar 

  268. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  271. 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)

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

    Google Scholar 

  273. 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)

    Google Scholar 

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

    Google Scholar 

  275. 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)

    Google Scholar 

  276. 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)

    Google Scholar 

  277. 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)

    Google Scholar 

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

    Google Scholar 

  279. 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)

    Google Scholar 

  280. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  284. 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)

    Google Scholar 

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

    Google Scholar 

  286. 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)

    Google Scholar 

  287. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

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

    Google Scholar 

  294. 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)

    Google Scholar 

  295. 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)

    Google Scholar 

  296. 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)

    Google Scholar 

  297. 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)

    Google Scholar 

  298. 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)

    Google Scholar 

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

    Google Scholar 

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

  301. 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)

    Google Scholar 

  302. 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)

    Google Scholar 

  303. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  306. 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)

  307. 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)

    Google Scholar 

  308. 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)

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

    Google Scholar 

  310. 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)

  311. 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)

  312. 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)

    Google Scholar 

  313. 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)

    Google Scholar 

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

    Google Scholar 

  315. 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)

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

    Google Scholar 

  317. 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)

    Google Scholar 

  318. 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)

  319. 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)

    Google Scholar 

  320. 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)

    Google Scholar 

  321. 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)

    Google Scholar 

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

    Google Scholar 

  323. 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)

    Google Scholar 

  324. 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)

  325. 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)

    Google Scholar 

  326. 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)

  327. 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)

  328. 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)

    Google Scholar 

  329. 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)

  330. 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)

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

    Google Scholar 

  332. 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)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  335. 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)

    Google Scholar 

  336. 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)

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

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

    Google Scholar 


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From: Measuring objective and subjective well-being: dimensions and data sources

Data source Pros Cons
CDRs Temporal and social dimensions, world wide diffusion, repeatability Not publicly available, sparsity, geographically imprecise
GPS and transportation Coverage of rural areas, unbiased and classified, real-time monitoring Privacy issues, indoor spatial inaccuracy
Social Media Measuring social dynamics, publicly available Privacy issues, overrepresentation, social desirability bias
Health and Fitness Cost effective, applicable for multiple studies, prediction of near-term risk of events Not publicly available, not necessarily representative of the population, limited time slots
News Variety of subject domains, range of targets, 24/h updated, archived historical news Gatekeeping bias, coverage bias, statement bias
Retail Scanners Modeling of dynamic household behavior, control time-invariant characteristics, long term coverage, quality improvement of HICP Dependency on retailer’s permission, legal constraints
Web Search Publicly available, speed, convenience, flexibility, ease of analysis Population size varies across domains, hard identifying relevant queries
Crowdsourcing Large number of data, speed, relative low cost Risk of low-quality results, trade-off between quality and cost