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3 - Social Media Research

from Part I - Quantitative Data Collection Sources

Published online by Cambridge University Press:  12 December 2024

By
Rosanna E. Guadagno  and
Alberto F. Olivieri
Edited by
John E. Edlund  and
Austin Lee Nichols
John E. Edlund
Affiliation:
Rochester Institute of Technology, New York
Austin Lee Nichols
Affiliation:
Central European University, Vienna
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Summary

The purpose of this chapter is to review the contemporary methods used to collect and examine data on social media and to explore the common pitfalls of internet research. The discussion focuses on the importance of internet research while also reviewing common practices of data retrieval (e.g., crowdsourcing and snowball sampling). We will also explain a commonly used tool to analyze data collected using social media. Specifically, one section is dedicated to the Linguistic Inquiry and Word Count software (LIWC); another section focuses on a brief overview of machine learning (ML) techniques and data visualization. At the end of the chapter, we will also examine some common ethical concerns, focusing mainly on anonymity and privacy, while also giving a general overview on the European General Data Protection Regulation (GDPR). Future directions for social media will then be addressed.

Type
Chapter
Information
The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
Volume 2: Performing Research
 , pp. 48 - 71
Publisher: Cambridge University Press
Print publication year: 2024

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References

Acker, A., & Kreisberg, A. (2020). Social media data archives in an API-driven world. Archival Science, 20, 105–123.CrossRefGoogle Scholar
Albertson, B., & Gadarian, S. (2014, July 1). Was the Facebook emotion experiment unethical? Washington Post. www.washingtonpost.com/news/monkey-cage/wp/2014/07/01/was-the-facebook-emotion-experiment-unethical/?utm_term=.15088275b53cGoogle Scholar
Anderson, C. A., Allen, J. J., Plante, C., Quigley-McBride, A., Lovett, A., & Rokkum, J. N. (2019). The MTurkification of social and personality psychology. Personality and Social Psychology Bulletin, 45(6), 842–850.CrossRefGoogle ScholarPubMed
Antoun, C., Zhang, C., Conrad, F. G., & Schober, M. F. (2016). Comparisons of online recruitment strategies for convenience samples: Craigslist, Google AdWords, Facebook, and Amazon Mechanical Turk. Field methods, 28(3), 231–246.CrossRefGoogle Scholar
Arif, A., Stewart, L. G., & Starbird, K. (2018). Acting the part: Examining information operations within #BlackLivesMatter discourse. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–27.CrossRefGoogle Scholar
Ashokkumar, A., & Pennebaker, J. W. (2021). Social media conversations reveal large psychological shifts caused by COVID-19’s onset across US cities. Science Advances, 7(39), eabg7843.CrossRefGoogle Scholar
Barrie, C., & Ho, J. C. (2022). Using the Twitter Academic API with R for Social Science Research. SAGE Publications. https://dx.doi.org/10.4135/9781529609233CrossRefGoogle Scholar
Bayer, J. B., Triệu, P., & Ellison, N. B. (2020). Social media elements, ecologies, and effects. Annual Review of Psychology, 71, 471–497.CrossRefGoogle ScholarPubMed
Bentley, F. R., Daskalova, N., & White, B. (2017, May). Comparing the reliability of Amazon Mechanical Turk and Survey Monkey to traditional market research surveys. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems (pp. 1092–1099). ACM.CrossRefGoogle Scholar
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political analysis, 20(3), 351–368.CrossRefGoogle Scholar
Boyd, R. L., Ashokkumar, A., Seraj, S., & Pennebaker, J. W. (2022). The Development and Psychometric Properties of LIWC-22. University of Texas at Austin.Google Scholar
Boyd, R. L., & Pennebaker, J. W. (2015). A way with words: Using language for psychological science in the modern era. In Dimofte, C. V., Haugtvedt, C. P., & Yalch, R. F. (eds.), Consumer Psychology in a Social Media World (pp. 222–236). Routledge.Google Scholar
Boyd, R. L., & Pennebaker, J. W. (2017). Language-based personality: A new approach to personality in a digital world. Current Opinion in Behavioral Sciences, 18, 63–68.CrossRefGoogle Scholar
Buchanan, E. A., & Hvizdak, E. E. (2009). Online survey tools: Ethical and methodological concerns of human research ethics committees. Journal of Empirical Research on Human Research Ethics, 4(2), 37–48.CrossRefGoogle ScholarPubMed
Buchanan, T., & Williams, J. E. (2010). Ethical issues in psychological research on the internet. In Gosling, S. D. & Johnson, J. A. (eds.), Advanced Methods for Conducting Online Behavioral Research (pp. 255–271). American Psychological Association.CrossRefGoogle Scholar
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.CrossRefGoogle Scholar
Burger, J. M. (2014). Personality. Cengage Learning.Google Scholar
Butler, L., Lamont, P., Wan, D. L. Y., Prike, T., Nasim, M., Walker, B., et al. (2022). The (mis) information game: A social media simulator. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02153-xCrossRefGoogle Scholar
boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.xCrossRefGoogle Scholar
Cadwalladr, C., Khalili, M., Phillips, C., Silver, M., Jenkins, A., Search, J., et al. (2021). Cambridge Analytica whistleblower: “We spent $1m harvesting millions of Facebook profiles” [video]. Guardian, March 17. www.theguardian.com/uk-news/video/2018/mar/17/cambridge-analytica-whistleblower-we-spent-1m-harvesting-millions-of-facebook-profiles-videoGoogle Scholar
Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29(6), 2156–2160.CrossRefGoogle Scholar
Chambers, C. (2014). Facebook fiasco: Was Cornell’s study of “emotional contagion” an ethics breach? Guardian, July 1. www.theguardian.com/science/head-quarters/2014/jul/01/facebook-cornell-study-emotional-contagion-ethics-breachGoogle Scholar
Chambers, M., Bliss, K., & Rambur, B. (2020). Recruiting research participants via traditional snowball vs Facebook advertisements and a website. Western Journal of Nursing Research, 42(10), 846–851.CrossRefGoogle ScholarPubMed
Chandler, J. (2023). Participant recruitment. In Nichols, A. & Edlund, J. E. (eds.), Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences (vol. 1, pp. 179–201). Cambridge University Press.CrossRefGoogle Scholar
Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12, 53–81.CrossRefGoogle ScholarPubMed
Clark, T., & Blackhart, G. (2023). Debriefing and post-experimental procedures. In Nichols, A. & Edlund, J. E. (eds.), Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences (vol. 1, pp. 244–265). Cambridge University Press.CrossRefGoogle Scholar
Clifford, S., Jewell, R. M., & Waggoner, P. D. (2015). Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, 2(4), 2053168015622072.CrossRefGoogle Scholar
Cohn, M. A., Mehl, M. R., & Pennebaker, J. W. (2004). Linguistic markers of psychological change surrounding September 11, 2001. Psychological Science, 15(10), 687–693.CrossRefGoogle ScholarPubMed
Dawson, P. (2014). Our anonymous online research participants are not always anonymous: Is this a problem? British Journal of Educational Technology, 45(3), 428–437.CrossRefGoogle Scholar
De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13, 1–10.Google Scholar
Dobber, T., Ó Fathaigh, R., & Zuiderveen Borgesius, F. J. (2019). The regulation of online political micro-targeting in Europe. Internet Policy Review, 8(4). https://policyreview.info/articles/analysis/regulation-online-political-micro-targeting-europeCrossRefGoogle Scholar
Edlund, J. E., Lange, K. M., Sevene, A. M., Umansky, J., Beck, C. D., & Bell, D. J. (2017). Participant crosstalk: Issues when using the Mechanical Turk. Tutorials in Quantitative Methods for Psychology, 13(3), 174–182.CrossRefGoogle Scholar
Foster, M. D. (2015). Tweeting about sexism: The well‐being benefits of a social media collective action. British Journal of Social Psychology, 54(4), 629–647.CrossRefGoogle ScholarPubMed
Gosling, S. D., & Mason, W. (2015). Internet research in psychology. Annual Review of Psychology, 66, 877–902.CrossRefGoogle ScholarPubMed
Graham-Harrison, E., & Cadwalladr, C. (2021). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. Guardian, September 29. www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-electionGoogle Scholar
Grimm, P. (2010). Social desirability bias. In Cooper, C. L. (ed.), Wiley International Encyclopedia of Marketing. Wiley.Google Scholar
Guadagno, R. E. (2019). Using the internet for research. In Edlund, J. E. & Nichols, A. L. (eds.), Advanced Research Methods for the Social and Behavioral Sciences (pp. 68–82). Cambridge University Press.Google Scholar
Guadagno, R. E. (in press). Psychological Processes in Social Media: Why We Click. Academic Press.Google Scholar
Guadagno, R. E., Loewald, T. A., Muscanell, N. L., Barth, J. M., Goodwin, M. K., & Yang, Y. (2013). Facebook history collector: A new method for directly collecting data from Facebook. International Journal of Interactive Communication Systems and Technologies (IJICST), 3(1), 57–67.CrossRefGoogle Scholar
Guadagno, R. E., Muscanell, N. L., & Pollio, D. E. (2013). The homeless use Facebook?! Similarities of social network use between college students and homeless young adults. Computers in Human Behavior, 29(1), 86–89. https://doi.org/10.1016/j.chb.2012.07.019CrossRefGoogle Scholar
Guadagno, R. E., Muscanell, N. L., Rice, L. M., & Roberts, N. (2013). Social influence online: The impact of social validation and likability on compliance. Psychology of Popular Media Culture, 2(1), 51–60.CrossRefGoogle Scholar
Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013). What makes a video go viral? An analysis of emotional contagion and internet memes. Computers in Human Behavior, 29(6), 2312–2319.CrossRefGoogle Scholar
Hellström, T., Dignum, V., & Bensch, S. (2020). Bias in machine learning: What is it good for? [preprint]. Arxiv. https://doi.org/10.48550/arXiv.2004.00686CrossRefGoogle Scholar
Hill, K. (2014). Facebook manipulated 689,003 users’ emotions for science. Forbes, June 28. www.forbes.com/sites/kashmirhill/2014/06/28/facebook-manipulated-689003-users-emotions-for-science/#593f5cbf197cGoogle Scholar
Ireland, M. E., & Henderson, M. D. (2014). Language style matching, engagement, and impasse in negotiations. Negotiation and Conflict Management Research, 7(1), 1–16.CrossRefGoogle Scholar
Jacobucci, R., & Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science, 15(3), 809–816. https://doi.org/10.1177/1745691620902467CrossRefGoogle ScholarPubMed
Jagayat, A. (2022, October 4). Mock social media website tool. OSF. https://osf.io/m2xd8Google Scholar
Jenders, M., Kasneci, G., & Naumann, F. (2013, May). Analyzing and predicting viral tweets. In Schwabe, Daniel (ed.), Proceedings of the 22nd International Conference on World Wide Web (pp. 657–664). ACM.CrossRefGoogle Scholar
Kendall, C., Kerr, L. R., Gondim, R. C., Werneck, G. L., Macena, R. H. M., Pontes, M. K., et al. (2008). An empirical comparison of respondent-driven sampling, time location sampling, and snowball sampling for behavioral surveillance in men who have sex with men, Fortaleza, Brazil. AIDS and Behavior, 12, 97–104.CrossRefGoogle ScholarPubMed
Kern, M. L., McCarthy, P. X., Chakrabarty, D., & Rizoiu, M. A. (2019). Social media-predicted personality traits and values can help match people to their ideal jobs. Proceedings of the National Academy of Sciences, 116(52), 26459–26464.CrossRefGoogle ScholarPubMed
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805.CrossRefGoogle ScholarPubMed
Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790.CrossRefGoogle ScholarPubMed
Ksiazek, T. B. (2015). Civil interactivity: How news organizations’ commenting policies explain civility and hostility in user comments. Journal of Broadcasting & Electronic Media, 59(4), 556–573.CrossRefGoogle Scholar
Lee, J., Gillath, O., Kimbrough, A. M., & Guadagno, R. E. (2017, January). Development and validation of helping in gaming scales. Poster presented at the Media Psychology Preconference, San Antonio, TX.Google Scholar
Lee, J., & Spratling, R. (2019). Recruiting mothers of children with developmental disabilities: Adaptations of the snowball sampling technique using social media. Journal of Pediatric Health Care, 33(1), 107–110.CrossRefGoogle ScholarPubMed
Markham, A., & Buchanan, E. (2012). Recommendations from the AoIR Ethics Working Committee (Version 2.0).Google Scholar
Merriam-Webster. (n.d.). Crowdsourcing. In Merriam-Webster.com [dictionary]. www.merriam-webster.com/dictionary/crowdsourcing (retrieved March 6, 2024).Google Scholar
Miller, J. D., Crowe, M., Weiss, B., Maples-Keller, J. L., & Lynam, D. R. (2017). Using online, crowdsourcing platforms for data collection in personality disorder research: The example of Amazon’s Mechanical Turk. Personality Disorders: Theory, Research, and Treatment, 8(1), 26–34.CrossRefGoogle ScholarPubMed
Murray, M. (2014). Users angered at Facebook emotion-manipulation study. Today, June 30. www.today.com/health/users-angered-facebook-emotion-manipulation-study-1D79863049Google Scholar
Muscanell, N. L., & Guadagno, R. E. (2012). Make new friends or keep the old: Gender and personality differences in social networking use. Computers in Human Behavior, 28(1), 107–112. https://doi.org/10.1016/j.chb.2011.08.016CrossRefGoogle Scholar
Nichols, A., & Edlund, J. E. (2020). Why don’t we care more about carelessness? Understanding the causes and consequences of careless participants. International Journal of Social Research Methodology, 23(6), 625–638. https://doi.org/10.1080/13645579.2020.1719618CrossRefGoogle Scholar
Oinas-Kukkonen, H., & Oinas-Kukkonen, H. (2013). Humanizing the Web: Change and Social Innovation. Palgrave Macmillan.CrossRefGoogle Scholar
Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184–188.CrossRefGoogle Scholar
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., et al. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934–952.CrossRefGoogle ScholarPubMed
Parker, C., Scott, S., & Geddes, A. (2019). Snowball Sampling. SAGE Publications. https://dx.doi.org/10.4135/9781526421036831710Google Scholar
Pennebaker, J. W., Chung, C. K., Ireland, M., Gonzales, A., & Booth, R. J. (2007). The development and psychometric properties of LIWC2007. LIWC. www.liwc.net/LIWC2007LanguageManual.pdfGoogle Scholar
Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. LIWC. www.researchgate.net/publication/246699633_Linguistic_inquiry_and_word_count_LIWCGoogle Scholar
Phing, A. N. M., & Yazdanifard, R. (2014). How does ALS ice bucket challenge achieve its viral outcome through marketing via social media? Global Journal of Management and Business Research, 14(E7), 57–64.Google Scholar
Roozenbeek, J., & van der Linden, S. (2019). Fake news game confers psychological resistance against online misinformation. Palgrave Communications, 5(12). https://doi.org/10.1057/s41599-019-0279-9CrossRefGoogle Scholar
Ross, J., Zaldivar, A., Irani, L., & Tomlinson, B. (2009). Who are the Turkers? Worker demographics in Amazon Mechanical Turk. In CHI ’10 Extended Abstracts on Human Factors in Computing Systems (pp. 2863–2872). ACM.Google Scholar
Rouse, S. V. (2015). A reliability analysis of Mechanical Turk data. Computers in Human Behavior, 43, 304–307.CrossRefGoogle Scholar
Prevency. (n.d.). The solution for a realistic social media simulation. https://socialmediasimulator.comGoogle Scholar
Solon, O. (2018). Facebook says Cambridge Analytica may have gained 37 m more users’ data. Guardian, April 4. www.theguardian.com/technology/2018/apr/04/facebook-cambridge-analytica-user-data-latest-more-than-thoughtGoogle Scholar
Stephens-Davidowitz, S. (2017). Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are. HarperLuxe.Google Scholar
Vasalou, A., Gill, A. J., Mazanderani, F., Papoutsi, C., & Joinson, A. (2011). Privacy dictionary: A new resource for the automated content analysis of privacy. Journal of the Association for Information Science and Technology, 62(11), 2095–2105.Google Scholar
Vaughn-Nichols, S. J. (2014). We’re all just lab rats in Facebook’s laboratory. ZDNet, June 30. www.zdnet.com/article/were-all-just-lab-rats-in-facebooks-laboratory/Google Scholar
Verma, I. M. (2014). Editorial expression of concern: Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(29), 10779–10779.Google ScholarPubMed
Wang, T., Brede, M., Ianni, A., & Mentzakis, E. (2017, February). Detecting and characterizing eating-disorder communities on social media. In de Rijke, M. (ed.), Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 91–100). ACM.CrossRefGoogle Scholar
Wright, S. A., & Goodman, J. K. (2019). Mechanical Turk in consumer research: Perceptions and usage in marketing academia. In Kardes, F. R., Herr, P. M., & Schwarz, N. (eds.), Handbook of Research Methods in Consumer Psychology (pp. 338–357). Routledge.Google Scholar
Zhou, H., & Fishbach, A. (2016). The pitfall of experimenting on the web: How unattended selective attrition leads to surprising (yet false) research conclusions. Journal of Personality and Social Psychology, 111(4), 493–504.CrossRefGoogle ScholarPubMed
Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361.CrossRefGoogle Scholar
Zimmer, M. (2018). Addressing conceptual gaps in big data research ethics: An application of contextual integrity. Social Media + Society, 4(2), 2056305118768300.CrossRefGoogle Scholar

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