nach oben

Demography

Erschienen in:

01.10.2018

Promises and Pitfalls of Using Digital Traces for Demographic Research

verfasst von: Nina Cesare, Hedwig Lee, Tyler McCormick, Emma Spiro, Emilio Zagheni

Erschienen in: Demography | Ausgabe 5/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The digital traces that we leave online are increasingly fruitful sources of data for social scientists, including those interested in demographic research. The collection and use of digital data also presents numerous statistical, computational, and ethical challenges, motivating the development of new research approaches to address these burgeoning issues. In this article, we argue that researchers with formal training in demography—those who have a history of developing innovative approaches to using challenging data—are well positioned to contribute to this area of work. We discuss the benefits and challenges of using digital trace data for social and demographic research, and we review examples of current demographic literature that creatively use digital trace data to study processes related to fertility, mortality, and migration. Focusing on Facebook data for advertisers—a novel “digital census” that has largely been untapped by demographers—we provide illustrative and empirical examples of how demographic researchers can manage issues such as bias and representation when using digital trace data. We conclude by offering our perspective on the road ahead regarding demography and its role in the data revolution.

Vorheriger Artikel Living on the Edge: Neighborhood Boundaries and the Spatial Dynamics of Violent Crime

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

For a review of these data, see Ruggles (2014) in a past issue of Demography.

See https://developer.twitter.com/en/docs/tweets/filter-realtime/overview for an overview of Twitter’s public streaming APIs.

See Facebook’s Data Policy: https://www.facebook.com/policy.php.

See Twitter’s Privacy Policy for more information: https://twitter.com/en/privacy.

See https://www.opalproject.org/about-opal for more information.

See http://iussp.org/en/panel/big-data-and-population-processes for more information on IUSSP workshop events.

Find more at http://www.facebook.com/business/.

See https://developers.facebook.com/docs/marketing-api/audiences-api.

See https://www.linkedin.com/ad/accounts.

See Preston et al. (2001:28–30) for details on conducting an age decomposition analysis.

Adams, J., & Brueckner, H. (2015). Wikipedia, sociology, and the promise and pitfalls of Big Data. Big Data & Society, 2(2), 1–5. https://doi.org/10.1177/2053951715614332 CrossRef

Alkema, L., Raftery, A. E., Gerland, P., Clark, S. J., & Pelletier, F. (2012). Estimating trends in the total fertility rate with uncertainty using imperfect data: Examples from West Africa. Demographic Research, 26(article 15), 332–361. https://doi.org/10.4054/DemRes.2012.26.15 CrossRef

Andrews, C., Fichet, E., Ding, Y., Spiro, E. S., & Starbird, K. (2016). Keeping up with the Tweet-dashians: The impact of “official” accounts on online rumoring. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 452–465). New York, NY: ACM. https://doi.org/10.1145/2818048.2819986

Ang, C., Bobrowicz, A., Schiano, D., & Nardi, B. (2013). Data in the wild: Some reflections. Interactions, 20(2), 39–43. https://doi.org/10.1145/2427076.2427085 CrossRef

Araújo, M., Mejova, Y., Weber, I., & Benevenuto, F. (2017). Using Facebook ads audiences for global lifestyle disease surveillance: Promises and limitations. In Proceedings of the 2017 ACM on Web Science Conference (pp. 253–257). New York, NY, ACM. https://doi.org/10.1145/3091478.3091513

Barberá, P. (2016). Less is more? How demographic sample weights can improve public opinion estimates based on Twitter data (Working paper). New York: Center for Data Science, New York University. Retrieved from http://pablobarbera.com/static/less-is-more.pdf

Barry, B. (2006). Friends for better or for worse: Interracial friendships in the United States as seen through wedding photos. Demography, 43, 491–510.CrossRef

Belli, R. F., Traugott, M. W., Young, M., & McGonagle, K. A. (1999). Reducing vote overreporting in surveys: Social desirability, memory failure, and source monitoring. Public Opinion Quarterly, 63, 90–108.CrossRef

Berinsky, A. J. (1999). The two faces of public opinion. American Journal of Political Science, 43, 1209–1230.CrossRef

Blei, D. M., & Smyth, P. (2017). Science and data science. Science, 114, 8689–8692.

Billari, F. C., D’Amuri, F., & Marcucci, J. (2013, April). Forecasting births using Google. Paper presented at the annual meeting of the Population Association of America, New Orleans, LA.

Billari, F. C., & Zagheni, E. (2017). Big data and population processes: A revolution? In A. Petrucci & R. Verde (Eds.), SIS 2017. Statistics and Data science: New challenges, new generations. 28–30 June 2017 Florence (Italy). Proceedings of the Conference of the Italian Statistical Society (pp. 167–178). Firenze, Italy: Firenze University Press. https://doi.org/10.17605/OSF.IO/F9VZP

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

Blumenstock, J., & Eagle, N. (2010). Mobile divides: Gender, socioeconomic status, and mobile phone use in Rwanda. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (pp. 6–10). New York, NY: ACM. https://doi.org/10.1145/2369220.2369225

Blumenstock, J. E. (2012). Inferring patterns of internal migration from mobile phone call records: Evidence from Rwanda. Information Technology for Development, 18, 107–125.CrossRef

Blumenstock, J. E., & Eagle, N. (2012). Divided we call: Disparities in access and use of mobile phones in Rwanda. Information Technologies and International Development, 8(2), 1–16.CrossRef

Blumenstock, J. E., & Toomet, O. (2014, May). Segregation and “silent separation”: Using large-scale network data to model the determinants of ethnic segregation. Paper presented at the annual meeting of the Population Association of America, Boston, MA.

Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15, 662–679.CrossRef

Brass, W. (1976). Indirect methods of estimating mortality illustrated by application to Middle East and North African data. In Population Bulletin of the United Nations Economic Commission for Western Asia. Amman, Jordan: UNECWA.

Cesare, N., Spiro, E., & Lee, H. (2015, April). Self-presentation and information disclosure on Twitter: Understanding patterns and mechanisms along demographic lines. Paper presented at the annual meeting of the Population Association of America, San Diego, CA.

Cesare, N., Lee, H., McCormick, T. H., & Spiro, E. S. (2017). Redrawing the silent “color line”: Examining racial segregation in associative networks on Twitter. Unpublished manuscript, University of Washington, Seattle, WA. Retrieved from arXiv:1705.04401.

Couldry, N., & Powell, A. (2014). Big Data from the bottom up. Big Data & Society, 1(2), 1–5. https://doi.org/10.1177/2053951714539277 CrossRef

De Choudhury, M., Counts, S., & Horvitz, E. (2013a). Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3267–3276). New York, NY: ACM.

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013b). Predicting depression via social media. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 128–137). Palo Alto, CA: AAAI Press. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/6124/6351

De Choudhury, M., Sharma, S., & Kiciman, E. (2016). Characterizing dietary choices, nutrition, and language in food deserts via social media. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 1157–1170). New York, NY: ACM. https://doi.org/10.1145/2818048.2819956

Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., . . . Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111, 15853–15854.CrossRef

Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., . . . Seligman, M. E. P. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26, 159–169.CrossRef

Fadnes, L., Taube, A., & Tylleskär, T. (2009). How to identify information bias due to self-reporting in epidemiological research. Internet Journal of Epidemiology, 7(2), 1–8.

Feehan, D., & Cobb, C. (2017, July). How many people have access to the Internet? Estimating Internet adoption around the world using Facebook. Paper presented at the International Conference on Computational Social Science, Cologne, Germany.

Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 1–15. https://doi.org/10.1177/2053951716645828 CrossRef

Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Unpublished manuscript, Department of Statistics, Columbia University, New York, NY. Retrieved from http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep and daylength across diverse cultures. Science 30, 1878–1881.CrossRef

Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129–152.CrossRef

González-Bailón, S. (2013). Social science in the era of big data. Policy and the Internet, 5, 147–160.CrossRef

Graham, M., Hale, S., & Stephens, M. (2012). Featured graphic: Digital divide: The geography of Internet access. Environment and Planning, 44, 1009–1010.CrossRef

Heaivilin, N., Gerbert, B., Page, J. E., & Gibbs, J. L. (2011). Public health surveillance of dental pain via Twitter. Journal of Dental Research, 90, 1047–1051.CrossRef

Holbrook, A. L., & Krosnick, J. A. (2010). Social desirability bias in voter turnout reports: Tests using the item count technique. Public Opinion Quarterly, 74, 37–67.CrossRef

Kashyap, R., Billari, F. C., Cavalli, N., Quian, E., & Weber, I. (2017, April). Ultrasound technology and “missing women” in India: Analyses and now-casts based on Google searches. Paper presented at the annual meeting of the Population Association of America, Chicago, IL.

Keyfitz, N., & Caswell, H. (2005). The matrix model framework. In N. Keyfitz & H. Caswell (Eds.), Applied mathematical demography (3rd ed., pp. 47–70). New York, NY: Springer.

Kikas, R., Dumas, M., & Saabas, A. (2015). Explaining international migration in the Skype network. In SIdEWayS ’15: Proceedings of the 1st ACM Workshop on Social Media World Sensors (pp. 17–22). New York, NY: ACM. https://doi.org/10.1145/2806655.2806658

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. https://doi.org/10.1177/2053951714528481 CrossRef

Latour, B. (2007). Beware, your imagination leaves digital traces. Times Higher Literary Supplement, 6(4). Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Beware+,+your+imagination+leaves+digital+traces#0

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: Traps in Big Data analysis. Science, 343, 1203–1205.CrossRef

Lazer, D., & Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 49, 19–39.CrossRef

Lee, H., Cesare, N., McCormick, T. H., Morris, J., & Shojaie, A. (2014, May). Redrawing the “color line”: Examining racial homophily of associative networks in social media. Paper presented at the annual meeting of the Population Association of America, Boston, MA.

Lewis, K. (2015). Three fallacies of digital footprints. Big Data & Society, 2(2), 1–4. https://doi.org/10.1177/2053951715602496 CrossRef

Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57, 1–33. https://doi.org/10.1080/08838151.2012.761702 CrossRef

Lohr, S. (2012, February 11). The age of Big Data. The New York Times. Retrieved from https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html

Madden, M., & Rainie, L. (2015). Americans’ attitudes about privacy, security and surveillance (Report). Washington, DC: Pew Research Center. Retrieved from http://www.pewinternet.org/2015/05/20/americans-attitudes-about-privacy-security-and-surveillance/

Malik, M. M., & Pfeffer, J. (2016, March). Social media data and computational models of mobility: A review for demography. Paper presented at the ICWSM Workshop on Social Media and Demographic Research, Cologne, Germany. Retrieved from http://www.pfeffer.at/papers/2016_demography.pdf

Manovich, L. (2011). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the digital humanities (pp. 460–476). Minneapolis: University of Minnesota Press.

Marwick, A. E., & Boyd, D. (2010). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13, 114–133. https://doi.org/10.1177/1461444810365313 CrossRef

Massey, D. (2016, March). Measuring racial prejudice using Google trends. Paper presented at the annual meeting of the Population Association of America, Washington, DC.

Mateos, P., & Durand, J. (2014, May). Netnography and demography: Mining Internet discussion forums on migration and citizenship. Paper presented at the annual meeting of the Population Association of America, Boston, MA.

McCormick, T. H., Lee, H., Cesare, N., Shojaie, A., & Spiro, E. S. (2015). Using Twitter for demographic and social science research: Tools for data collection and processing. Sociological Methods & Research, 46, 390–421.CrossRef

Mendieta, J., Su, S., Vaca, C., Ochoa, D., & Vergara, C. (2016). Geo-localized social media data to improve characterization of international travelers. In Proceedings of the 2016 Third International Conference on eDemocracy & eGovernment (ICEDEG) (pp. 126–132). Piscataway, NJ: Institute of Electrical and Electronics Engineers.

Metzler, K., Kim, D. A., Allum, N., & Denman, A. (2016). Who is doing computational social science? Trends in big data research (White paper). London, UK: SAGE Publishing. https://doi.org/10.4135/wp160926

Mislove, A., Lehmann, S., & Ahn, Y. (2011). Understanding the demographics of Twitter users. In Proceedings of the Fifth International Conference on Weblogs and Social Media (pp. 554–557). Menlo Park, CA: AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2816/3234

Moreno, M. A., Christakis, D. A., Egan, K. G., Brockman, L. N., & Becker, T. (2012). Associations between displayed alcohol references on Facebook and problem drinking among college students. Archives of Pediatrics & Adolescent Medicine, 166, 157–163.CrossRef

National Research Council (NRC). (2014). Proposed revisions to the common rule for the protection of human subjects in the behavioral and social sciences (Report). Washington, DC: National Academies Press.

O’Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 122–129). Palo Alto, CA: AAAI Press. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1536/1842

Ojala, J, Zagheni E., Billari, F. C., & Weber, I. (2017). Fertility and its meaning: Evidence from search behavior. In Proceedings of the Eleventh International AAAI Conference on Web and Social Media (pp. 640–643). Palo Alto, CA: AAAI Press.

Palmer, J. R. B., Espenshade, T. J., Bartumeus, F., Chung, C. Y., Ozgencil, N. E., & Li, K. (2013). New approaches to human mobility: Using mobile phones for demographic research. Demography, 50, 1105–1128.CrossRef

Park, R. E., & Burgess, E. W. (1925). The city. Chicago, IL: University of Chicago Press.

Pettit, B. (2012). Invisible men: Mass incarceration and the myth of black progress. New York, NY: Russell Sage Foundation.

Pew Research Center. (2018). Internet/broadband fact sheet. Washington, DC: Pew Research Center. Retrieved from http://www.pewinternet.org/fact-sheet/internet-broadband/

Pötzschke, S., & Braun, M. (2016). Migrant sampling using Facebook advertisements: A case study of Polish migrants in four European countries. Social Science Computer Review, 35, 633–653.CrossRef

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and modeling population processes. Oxford, UK: Blackwell.

Reeder, H., McCormick, T. H., & Spiro, E. (2014). Online information behaviors during disaster events: Roles, routines, and reactions (Working Paper No. 144). Seattle, WA: Center for Statistics and the Social Sciences.

Reis, B. Y., & Brownstein, J. S. (2010). Measuring the impact of health policies using Internet search patterns: The case of abortion. BMC Public Health, 10(article 514). https://doi.org/10.1186/1471-2458-10-514

Rosello, J. L. D., & Filgueira, F. (2016, April). Big data in a small country: Integrating birth, maternal and child statistics in Uruguay. Paper presented at the annual meeting of the Population Association of America, Washington, DC.

Rosenfeld, M. J., & Thomas, R. J. (2012). Searching for a mate: The rise of the Internet as a social intermediary. American Sociological Review, 77, 523–547.CrossRef

Ruggles, S. (2014). Big microdata for population research. Demography, 51, 287–297.CrossRef

Ruppert, E., Law, J., & Savage, M. (2013). Reassembling social science methods: The challenge of digital devices. Theory, Culture and Society, 30(4), 22–46.CrossRef

Sagiroglu, S., & Sinanc, D. (2013). Big Data: A review. In 2013 International Conference on Collaboration Technologies and Systems (CTS) (pp. 42–47). Piscataway, NY: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CTS.2013.6567202

Ševčíková, H., Raftery, A. E., & Waddell, P. A. (2007). Assessing uncertainty in urban simulations using Bayesian melding. Transportation Research, Part B: Methodological, 41, 652–669.CrossRef

Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. Chicago, IL: University of Chicago Press.

Smith, A., & Anderson, M. (2018). Social media use in 2018. Washington, DC: Pew Research Center. Retrieved from http://assets.pewresearch.org/wp-content/uploads/sites/14/2018/03/01105133/PI_2018.03.01_Social-Media_FINAL.pdf

Snijders, C., Matzat, U., & Reips, U.-D. (2012). Big data: Big gaps of knowledge in the field of Internet science. International Journal of Internet Science, 7, 1–5. Retrieved from http://www.ijis.net/ijis7_1/ijis7_1_editorial_pre.html

Starbird, K., Maddock, J., Orand, M., Achterman, P., & Mason, R. M. (2014). Rumors, false flags, and digital vigilantes: Misinformation on Twitter after the 2013 Boston Marathon bombing. In M. Kindling & E. Greinfeneder (Eds.), iConference 2014 proceedings (pp. 654–662). Urbana-Champaign, IL: iSchools.

Starbird, K., Spiro, E., Edwards, I., Zhou, K., Maddock, J., & Narasimhan, S. (2016). Could this be true? I think so! Expressed uncertainty in online rumoring. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 360–371). New York, NY: ACM.

State, B., Rodriguez, M., Helbing, D., & Zagheni, E. (2014). Migration of professionals to the U.S.: Evidence from LinkedIn data. In L. M. Aiello & D. McFarland (Eds.), 6th International Conference on Social Informatics, SocInfo 2014 (pp. 531–543). Cham, Switzerland: Springer.

Stevenson, A. J. (2014). Finding the Twitter users who stood with Wendy. Contraception, 90, 502–507.CrossRef

Sutton, J., Spiro, E. S., Johnson, B., Fitzhugh, S., Gibson, B., & Butts, C. T. (2014). Warning tweets: Serial transmission of messages during the warning phase of a disaster event. Information, Communication & Society, 17, 765–787.CrossRef

Tamgno, J. K., Faye, R. M., & Lishou, C. (2013). Verbal autopsies, mobile data collection for monitoring and warning causes of deaths. In 14th International Conference on Advanced Communication Technology, Technical Proceedings, 2013 (pp. 495–501). Piscataway, NJ: Institute of Electrical and Electronics Engineers. Retrieved from https://ieeexplore.ieee.org/document/6488236/

Taylor, L., Floridi, L., & van der Sloot, L. (Eds.). (2017). Group privacy: New challenges of data technologies. Cham, Switzerland: Springer.

Tomlinson, M., Solomon, W., Singh, Y., Doherty, T., Chopra, M., Ijumba, P., . . . Jackson, D. (2009). The use of mobile phones as a data collection tool: A report from a household survey in South Africa. BMC Medical Informatics and Decision Making, 9(article 51). https://doi.org/10.1186/1472-6947-9-51

Tourangeau, R., & Yan, T. (2007). Sensitive questions in surveys. Psychological Bulletin, 133, 859–883.CrossRef

Tourassi, G., Yoon, H. J., & Xu, S. (2016). A novel web informatics approach for automated surveillance of cancer mortality trends. Journal of Biomedical Informatics, 61, 110–118.CrossRef

Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (pp. 505–514). Palo Alto, CA: AAAI Press.

Vitak, J. (2015). I like it….Whatever that means: The evolving relationship between disclosure, audience, and privacy in networked spaces [SlideShare presentation]. Retrieved from https://www.slideshare.net/jvitak/i-like-itwhatever-that-means-the-evolving-relationship-between-disclosure-audience-and-privacy-in-networked-spaces

Wang, W., Rothschild, D., Goel, S., & Gelman, A. (2015). Forecasting elections with non-representative polls. International Journal of Forecasting, 31, 980–991.CrossRef

Willekens, F., Massey, D., Raymer, J., & Beauchemin, C. (2016). International migration under the microscope. Science, 352, 897–899.CrossRef

Williams, N. E., Thomas, T. A., Dunbar, M., Eagle, N., & Dobra, A. (2015). Measures of human mobility using mobile phone records enhanced with GIS data. PLoS One, 10(7), 1–16. https://doi.org/10.1371/journal.pone.0133630 CrossRef

Zagheni, E., Garimella, V. R. K., Ingmar, W., & State, B. (2014). Inferring international and internal migration patterns from Twitter data. In Proceedings of the 23rd International Conference on World Wide Web (pp. 439–444). New York, NY: ACM Press. https://doi.org/10.1145/2567948.2576930

Zagheni, E., & Weber, I. (2012). You are where you e-mail: using e-mail data to estimate international migration rates. In Proceedings of the 4th Annual ACM Web Science Conference (pp. 348–351). New York, NY: ACM. https://doi.org/10.1145/2380718.2380764

Zagheni, E., Weber, I., & Gummadi, K. (2017). Leveraging Facebook’s advertising platform to monitor stocks of migrants. Population and Development Review, 43, 721–734.CrossRef

Zeng, L., Starbird, K., & Spiro, E. S. (2016). Rumors at the speed of light? Modeling the rate of rumor transmission during crisis. In 49th Hawaii International Conference on System Sciences (HICSS), 1969–1978. Piscataway, NJ: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/HICSS.2016.248

Zimmer, M. (2010). But the data is already public: On the ethics of research in Facebook. Ethics and Information Technology, 12, 313–325.CrossRef

Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 1–6. https://doi.org/10.1177/2053951714559253 CrossRef

Titel: Promises and Pitfalls of Using Digital Traces for Demographic Research
verfasst von: Nina Cesare
Hedwig Lee
Tyler McCormick
Emma Spiro
Emilio Zagheni
Publikationsdatum: 01.10.2018
Verlag: Springer US
Erschienen in: Demography / Ausgabe 5/2018
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-018-0715-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 5/2018

Can Public Housing Decrease Segregation? Lessons and Challenges From Non-European Immigration in France

Gender Bias in Parental Attitude: An Experimental Approach

Prenatal Exposure to an Acute Stressor and Children’s Cognitive Outcomes

Individual Uncertainty About Longevity

Financially Overextended: College Attendance as a Contributor to Foreclosures During the Great Recession

Living on the Edge: Neighborhood Boundaries and the Spatial Dynamics of Violent Crime