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15 - Collecting Digital Footprints in the Wild

from Part III - Deep Dives on Methods and Tools for Testing Your Question of Interest

Published online by Cambridge University Press:  12 December 2024

Harry T. Reis
Affiliation:
University of Rochester, New York
Tessa West
Affiliation:
New York University
Charles M. Judd
Affiliation:
University of Colorado Boulder
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Summary

As people migrate to digital environments they produce an enormous amount of data, such as images, videos, data from mobile sensors, text, and usage logs. These digital footprints documenting people’s spontaneous behaviors in natural environments are a gold mine for social scientists, offering novel insights; more diversity; and more reliable, replicable, and ecologically valid results.

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Publisher: Cambridge University Press
Print publication year: 2024

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References

Bachrach, Y., Graepel, T., Kohli, P., Kosinski, M., and Stillwell, D. J. (2014). Your digital image: factors behind demographic and psychometric predictions from social network profiles. Proceedings of the 2014 International Conference on Autonomous Agents and Multiagent Systems.Google Scholar
Bakker, M., van Dijk, A., and Wicherts, J. M. (2012). The rules of the game called psychological science. Perspectives on Psychological Science, 7(6), 543554.CrossRefGoogle ScholarPubMed
Barker, R. G., and Wright, H. F. (1951). One Boy’s Day. A Specimen Record of Behavior. Harper & Row.Google Scholar
Beasley, C., and Holmes, M. (2021). Internet Dating: Intimacy and Social Change. Routledge.CrossRefGoogle Scholar
Beauchamp, N. (2022). “This candle has no smell”: Detecting the effect of COVID anosmia on Amazon reviews using Bayesian vector autoregression. In Proceedings of the International AAAI Conference on Web and Social Media, 16, 13631367.CrossRefGoogle Scholar
Bi, B., Kosinski, M., Shokouhi, M., and Graepel, T. (2013). Inferring the Demographics of Search Users Social Data Meets Search Queries. Proceedings of the International WWW Conference.Google Scholar
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., and Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295298.CrossRefGoogle ScholarPubMed
Breza, E., Stanford, F. C., Alsan, M., Alsan, B., Banerjee, A., Chandrasekhar, A. G., … Duflo, E. (2021). Effects of a large-scale social media advertising campaign on holiday travel and COVID-19 infections: A cluster randomized controlled trial. Nature Medicine, 27(9), 16221628.CrossRefGoogle ScholarPubMed
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 18771901.Google Scholar
Cadwalladr, C. (2016). Google, democracy and the truth about internet search. The Guardian, 4(12), 2016.Google Scholar
Cao, X. and Kosinski, M. (2024) Large Language Models Know How the Personality of Public Figures is Perceived by the General Public. Scientific Reports.CrossRefGoogle Scholar
Chen, E. E., and Wojcik, S. P. (2016). A practical guide to big data research in psychology. Psychological Methods, 21(4), 458474.CrossRefGoogle ScholarPubMed
Christensen, H. T. (1947). Student views on mate selection. Marriage and Family Living, 9(4), 8588.CrossRefGoogle ScholarPubMed
de Montjoye, Y. A., Hidalgo, C., Verleysen, M., Blondel, V. D. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports 3, 1376.CrossRefGoogle Scholar
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 7887.CrossRefGoogle Scholar
Eagle, N., Pentland, A., and Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 1527415278.CrossRefGoogle ScholarPubMed
Egebark, J., Ekström, M., Plug, E., and van Praag, M. (2021). Brains or beauty? Causal evidence on the returns to education and attractiveness in the online dating market. Journal of Public Economics, 196, 104372, DOI:10.1016/j.jpubeco.2021.104372.CrossRefGoogle Scholar
Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G. J., Labarthe, D. R., Merchant, R. M., … Seligman, M. E. P. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26(2), 159169.CrossRefGoogle ScholarPubMed
Ellard-Gray, A., Jeffrey, N. K., Choubak, M., and Crann, S. E. (2015). Finding the hidden participant: Solutions for recruiting hidden, hard-to-reach, and vulnerable populations. International Journal of Qualitative Methods, 14(5), 1609406915621420.CrossRefGoogle Scholar
Fraley, R. C., and Marks, M. J. (2007). The null hypothesis significance testing debate and its implications for personality research. In Robins, R. W., Fraley, R. C., and Krueger, R. F. (eds.) Handbook of Research Methods in Personality Psychology. Guilford Press.Google Scholar
Gerlach, T. M., Arslan, R. C., Schultze, T., Reinhard, S. K., & Penke, L. (2019). Predictive validity and adjustment of ideal partner preferences across the transition into romantic relationships. Journal of Personality and Social Psychology, 116(2), 313.CrossRefGoogle Scholar
Gordon, A. M., and Mendes, W. B. (2021). A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform. Proceedings of the National Academy of Sciences of the United States of America, 118(31), e2105573118.CrossRefGoogle ScholarPubMed
Gosling, S. D., and Mason, W. (2015). Internet research in psychology. Annual Review of Psychology, 66, 877902.CrossRefGoogle ScholarPubMed
Götz, F. M., Gosling, S. D., and Rentfrow, P. J. (2022). Small effects: The indispensable foundation for a cumulative psychological science. Perspectives on Psychological Science, 17(1), 205215.CrossRefGoogle ScholarPubMed
Graff, M. (2022). Online dating fatigue – why some people are turning to face-to-face apps first. The Conversation.Google Scholar
Hanel, P. H., and Vione, K. C. (2016). Do student samples provide an accurate estimate of the general public? PLOS ONE, 11(12), e0168354.CrossRefGoogle ScholarPubMed
Haynes, L., Goldacre, B., and Torgerson, D. (2012). Test, Learn, Adapt: Developing Public Policy with Randomised Controlled Trials. Cabinet Office Behavioural Insights Team.Google Scholar
Henrich, J., Heine, S. J., and Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 6183CrossRefGoogle ScholarPubMed
Hill, K. (2012, February 16). How Target figured out a teen girl was pregnant before her father did. Forbes, www.forbes.com.Google Scholar
Hinds, J., Brown, O., Smith, L. G. E., Piwek, L., Ellis, D. A., and Joinson, A. N. (2022). Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science. Current Directions in Psychological Science, 31(1), 8895.CrossRefGoogle Scholar
Hitsch, G. J., Hortaçsu, A., and Ariely, D. (2010). Matching and sorting in online dating. American Economic Review, 100(1), 130163.CrossRefGoogle Scholar
Jones, N. M., Wojcik, S. P., Sweeting, J., and Silver, R. C. (2016). Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses. Psychological Methods, 21(4), 526541.CrossRefGoogle ScholarPubMed
Kennedy, B., Ashokkumar, A., Boyd, R. L., and Dehghani, M. (2022). Text analysis for psychology: Methods, principles, and practices. In Dehghani, M. and Boyd, R. L. (eds.) The Handbook of Language Analysis in Psychology. Guilford Press.Google Scholar
Kern, M. L., Park, G. J., Eichstaedt, J. C., Schwartz, A. H., Sap, M., Smith, L. K., and Ungar, L. H. (2016). Gaining insights from social media language: Methodologies and challenges. Psychological Methods, 21(4), 507525.CrossRefGoogle ScholarPubMed
Kohavi, R., Tang, D., Xu, Y., Hemkens, L. G., and Ioannidis, J. (2020). Online randomized controlled experiments at scale: lessons and extensions to medicine. Trials, 21(1), 19.CrossRefGoogle ScholarPubMed
Kosinski, M. (2017). Facial width-to-height ratio does not predict self-reported behavioral tendencies. Psychological Science, 28(11), 16751682.CrossRefGoogle Scholar
Kosinski, M. (2021). Facial recognition technology can expose political orientation from naturalistic facial images. Scientific Reports, 11(1), 17.Google ScholarPubMed
Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D. J., and Graepel, T. (2013). Manifestations of User Personality in Website Choice and Behaviour on Online Social Networks. Machine Learning.Google Scholar
Kosinski, M., Khambatta, P., and Wang, Y. (2024). Facial recognition technology can infer political orientation from stable facial features. American Psychologist.Google Scholar
Kosinski, M., Matz, S. C., Gosling, S. D., Popov, V., and Stillwell, D. J. (2015). Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. American Psychologist, 70(6), 543556.CrossRefGoogle ScholarPubMed
Kosinski, M., Stillwell, D., and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 58025805.CrossRefGoogle ScholarPubMed
Kosinski, M., Wang, Y., Lakkaraju, H., and Leskovec, J. (2016). Mining big data to extract patterns and predict real-life outcomes. Psychological Methods, 21(4), 493506.CrossRefGoogle ScholarPubMed
Landers, R., and Behrend, T. (2015). An inconvenient truth: Arbitrary distinctions between organizational, mechanical Turk, and other convenience samples. Industrial and Organizational Psychology, 8(2), 142164.CrossRefGoogle Scholar
Landers, R. N., Brusso, R. C., Cavanaugh, K. J., and Collmus, A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. Psychological Methods, 21(4), 475492.CrossRefGoogle ScholarPubMed
Levy, S. (2020). Facebook: The inside story. Penguin; UK.Google ScholarPubMed
Matz, S. C., Gladstone, J. J., and Stillwell, D. J. (2016). Money buys happiness when spending fits our personality. Psychological Science, 27(5), 715725.CrossRefGoogle ScholarPubMed
Matz, S. C., Kosinski, M., Nave, G., and Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 1271412719.CrossRefGoogle ScholarPubMed
Mehl, M. R., Eid, M., Wrzus, C., Harari, G. M., and Ebner-Priemer, U. (eds.) (2023). Handbook of Mobile Sensing in Psychology: Methods and Applications. Guilford Press.Google Scholar
Mestyán, M., Yasseri, T., and Kertész, J. (2013). Early prediction of movie box office success based on Wikipedia activity big data. PLOS ONE, 8(8), e71226.CrossRefGoogle ScholarPubMed
Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies, R. R., Eisman, E. J., and Reed, G. M. (2001). Psychological testing and psychological assessment: A review of evidence and issues. American Psychologist, 56(2), 128165.CrossRefGoogle ScholarPubMed
Murphy, S. C. (2017). A hands-on guide to conducting psychological research on Twitter. Social Psychological and Personality Science, 8 (4), 396412.CrossRefGoogle Scholar
Nave, G., Minxha, J., Greenberg, D. M., Kosinski, M., Stillwell, D. J. and Rentfrow, J. (2018). Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes. Psychological Science.CrossRefGoogle Scholar
Park, G. J., Schwartz, A. H., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L., Seligman, M. E. P. (2014). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934952.CrossRefGoogle ScholarPubMed
Phan, T. Q., and Airoldi, E. M. (2015). A natural experiment of social network formation and dynamics. Proceedings of the National Academy of Sciences, 112(21), 65956600.CrossRefGoogle ScholarPubMed
Quercia, D., Kosinski, M., Stillwell, D. J., and Crowcroft, J. (2011). Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. Proceedings of the IEEE International Conference on Social Computing.CrossRefGoogle Scholar
Raento, M., Oulasvirta, A., and Eagle, N. (2009). Smartphones: An Emerging Tool for Social Scientists. Sociological Methods & Research, 37(3), 426454.CrossRefGoogle Scholar
Rosenfeld, M. J., Thomas, R. J., and Hausen, S. (2019). Disintermediating your friends: How online dating in the United States displaces other ways of meeting. Proceedings of the National Academy of Sciences, 116(36), 1775317758.CrossRefGoogle ScholarPubMed
Rudder, C. (2009). How your race affects the messages you get. OK Cupid blog, www.gwern.net/docs/psychology/okcupid/howyourraceaffectsthemessagesyouget.html.Google Scholar
Rust, J., Kosinski, M., and Stillwell, D. (2020). Modern Psychometrics. Routledge.CrossRefGoogle Scholar
Stachl, C., Hilbert, S., Au, J. Q., Buschek, D., De Luca, A., Bischl, B., Hussmann, H., and Bühner, M. (2017). Personality traits predict smartphone usage. European Journal of Personality, 31(6), 701722.CrossRefGoogle Scholar
Schultze, U., and Mason, R. O. (2012). Studying Cyborgs: Re-Examining Internet Studies As Human Subjects Research. Journal of Internet Technology, 27(4), 301312.Google Scholar
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Kosinski, M., Stillwell, D., Seligman, M. E. P., and Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLOS ONE.CrossRefGoogle Scholar
Stephens-Davidowitz, S. (2014). The cost of racial animus on a black candidate: Evidence using Google search data. Journal of Public Economics, 118, 2640.CrossRefGoogle Scholar
Sweeney, L. (1997). Weaving technology and policy together to maintain confidentiality. Journal of Law, Medicine & Ethics, 25(2–3), 98110.CrossRefGoogle ScholarPubMed
Todorov, A., Olivola, C. Y., Dotsch, R., and Mende-Siedlecki, P. (2015). Social attributions from faces: Determinants, consequences, accuracy, and functional significance. Annual Review of Psychology, 66, 519545.CrossRefGoogle ScholarPubMed
Vazire, S., and Gosling, S. D. (2004). e-Perceptions: personality impressions based on personal websites. Journal of personality and social psychology, 87(1), 123.CrossRefGoogle Scholar
Wang, Y., and Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology, 144(2), 246257.CrossRefGoogle Scholar
Wojcik, S., and Hughes, A. (2019). Sizing up Twitter users. PEW Research Center, 24, 123.Google Scholar
Yarkoni, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of research in personality, 44(3), 363373.CrossRefGoogle ScholarPubMed
Yarkoni, T., and Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 11001122.CrossRefGoogle ScholarPubMed
Youyou, W., Kosinski, M., and Stillwell, D. J. (2015). Computer-based personality judgements are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112(4), 10361040.CrossRefGoogle ScholarPubMed
Youyou, W., Stillwell, D. J., Schwartz, A. H., and Kosinski, M. (2017). Birds of a feather do flock together: Behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychological Science, 28(3), 276284.CrossRefGoogle Scholar
Zebrowitz, L. A., and Montepare, J. M. (2008). Social psychological face perception: Why appearance matters. Social and Personality Psychology Compass, 2, 14971517.CrossRefGoogle ScholarPubMed

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