These resources provide background information about each stage in our proposed hybrid methodology from varying disciplines. As we continue learning from each other and our broader research community, additional relevant resources will be added to this page. If you have any suggested resources to add, complete this form.
General Resources
- Adjerid, I. & Kelley, K. (2018). Big data in psychology: A framework for research advancement. American Psychologist.
- Dredze, M. (2012). How social media will change public health. IEEE Intelligent Systems, 27(4), 81-84.
- Dunning, T. (2012). Natural experiments in the social sciences: a design-based approach. Cambridge University Press.
- Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton.
- Hesse, B. W., Moser, R. P., & Roley, W. T. (2015). From big data to knowledge in the social sciences. The ANNALS of the American Academy of Political and Social Science, 659, 16-32.
- Injadat, M., Salo, F., & Nassif, A. B. (2016). Data mining techniques in social media. Neurocomputation 214, C, pp. 654-670.
- Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531-558.
- Kellstedt, P. M., & Whitten, G. D. (2018). The fundamentals of political science research. Cambridge University Press.
- Krippendorff, K. (2013). Content Analysis: An Introduction to Its Methodology, 3rd edition. Los Angeles: SAGE Publications.
- Kreuter, F. & Peng, R.D. (2014). Extracting information from Big Data: Issues of Measurement, Inference and Linkage. Privacy, Big Data, and the Public Good: Frameworks for Engagement. Lane, Stodden, Bender, Nissenbaum (eds.).
- Ledford, H. (2020). How Facebook, Twitter, and other data troves are revolutionizing social science. Nature, 582(7812), 328-330.
- Neuendorf, K. A. (2002). The Content Analysis Guidebook, Thousand Oaks, CA: SAGE Publications.
- Singh, L., Traugott, M., Bode, L., Budak, C., Davis-Kean, P. E., Guha, R., Ladd, J., Mneimneh, Z., Nguyen, Q., Pasek, J., Raghunathan, T., Ryan, R., Soroka, S., Wahedi, L. (2020). Data blending: Haven’t we been doing this for years? [White paper]. Georgetown Massive Data Institute Report.
- Singh, L., Polyzou, A., Wang, Y., Farr, J., & Gresenz, C. R. (2020). Social Media Data-Our Ethical Conundrum. IEEE Data Engineering, 23.
- Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L., & Merchant, R. M. (2017). Twitter as a tool for health research: a systematic review. American Journal of Public Health, 107(1), e1-e8.
- Velasco, E., Agheneza, T., Denecke, K., Kirchner, G., & Eckmanns, T. (2014). Social media and internet‐based data in global systems for public health surveillance: a systematic review. The Milbank Quarterly, 92(1), 7-33.
Methodology Components Resources
Study Design Resources
- Jensen, D. D., Fast, A. S., Taylor, B. J., & Maier, M. E. (2008). Automatic identification of quasi-experimental designs for discovering causal knowledge. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (pp. 372-380).
- Munger, K. (2019). The limited value of non-replicable field experiments in contexts with low temporal validity. Social Media+ Society, 5(3), 2056305119859294.
- Oktay, H., Taylor, B. J., & Jensen, D. D. (2010). Causal discovery in social media using quasi-experimental designs. In Proceedings of the Workshop on Social Media Analytics (pp. 1-9).
- Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.
Data Acquisition and Sampling Resources
- Berzofsky, M.E., McKay, T., Hsieh, Y., & Smith, A. (2018). Probability-based samples on Twitter: Methodology and application. Survey practice, 11, 4936.
- Conrad, F. G., Gagnon-Bartsch, J. A., Ferg, R. A., Schober, M. F., Pasek, J., & Hou, E. (2019). Social media as an alternative to surveys of opinions about the economy. Social Science Computer Review, 0894439319875692.
- de Pedraza, P., Visintin, S., Tijdens, K., & Kismihók, G. (2019). Survey vs scraped data: comparing time series properties of web and survey vacancy data. IZA Journal of Labor Economics, 8(1).
- Duncan, G. J., Magnuson, K. A., & Ludwig, J. (2004). The endogeneity problem in developmental studies. Research in human development, 1(1-2), 59-80.
- Hargittai, E. (2020). Potential biases in big data: Omitted voices on social media. Social Science Computer Review, 38(1), 10-24.
- Patone, M., & Zhang, L. C. (2019). On two existing approaches to statistical analysis of social media data. arXiv preprint arXiv:1905.00635.
- Piña-García, C. A., Gershenson, C., & Siqueiros-García, J. M. (2016). Towards a standard sampling methodology on online social networks: Collecting global trends on twitter. Applied network science, 1(1), 3.
Measurement and Feature Engineering Resources
- Angrist, J. D., & Pischke, J. S. (2014). Mastering’metrics: The path from cause to effect. Princeton University Press.
- Barberá, P. (2016). Less is more? How demographic sample weights can improve public opinion estimates based on Twitter data.
- Blei, D. M. & Lafferty, J.D. (2006). Dynamic topic models. In Proceedings of IEEE International Conference on Machine Learning (ICML)
- Canales, L., & Martínez-Barco, P. (2014). Emotion detection from text: A survey. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC) (pp. 37-43).
- Churchill, R., Singh, L., Kirov, C. 2018. A temporal topic model for noisy mediums. In Proceedings of Pacific Asian Conference on Knowledge Discovery and Data Mining (PAKDD).
- de Vreese, C. H. & Neijens, P. (2016). Measuring Media Exposure in a Changing Communications Environment. Communication Methods and Measures, 10(2-3): 69-80.
- Guess, A., Munger, K., Nagler, J., & Tucker, J. (2019). How accurate are survey responses on social media and politics?. Political Communication, 36(2), 241-258.
- Henderson, M., Jiang, K., Johnson, M., & Porter, L. (2019). Measuring Twitter use: Validating survey-based measures. Social Science Computer Review, 0894439319896244.
- Hsieh, Y. P. & Murphy, J. (2017). Total Twitter Error: Decomposing Public Opinion Measurement on Twitter from a Total Survey Error Perspective. Total Survey Error in Practice, New York: John Wiley & Sons. First Edition. 23-46.
- Liu, W., & Ruths, D. (2013). What’s in a name? Using first names as features for gender inference in Twitter. In 2013 AAAI Spring Symposium Series.
- Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110.
- Wang, X. & McCallum, A. (2006). Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD).
Model Construction Resources
- Al Zamal, F., Liu, W., & Ruths, D. (2012). Homophily and latent attribute inference: Inferring latent attributes of Twitter users from neighbors. ICWSM, 270(2012).
- Burton, J. W., Cruz, N., & Hahn, U. (2021). Reconsidering evidence of moral contagion in online social networks. Nature Human Behaviour, 1-7.
- Chen, X., Wang, Y., Agichtein. E., & Wang, F. (2015). A comparative study of demographic attribute inference in Twitter. In Ninth International AAAI Conference on Web and Social Media.
- Mohammady, E., & Culotta, A. (2014). Using county demographics to infer attributes of Twitter users. In Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media (pp. 7-16).
- Pennacchiotti, M. & Popescu, A. M. (2011). A machine learning approach to Twitter user classification. In Proceedings of AAAI Conference on Weblogs and Social Media.
Analysis and Visualization Resources
- Budak, C., Goesl, S., and Rao, J. M. (2016). Fair and Balances? Quantifying Media Bias through Crowdsourced Content Analysis. Public Opinion Quarterly, 80(S1), 250-271.
- Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. In Proceedings of the National Academy of Sciences (pp. 8788-8790).
- Schrek, T. and Keim, D. (2012). Visual Analysis of Social Media Data. IEEE, 46(5), 68-75.
Case Studies
- Antenucci, D., Cafarella, M., Levenstein, M., Ré, & Shapiro, C. (2014). Using social media to measure labor market flows. National Bureau of Economic Research.
- Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Analysis, 23(1), 76-91.
- de Choudhury, M., Counts, S., & Horvitz, E. (2013). Social Media as a Measurement Tool of Depression in Populations. In Proceedings of the 5th ACM International Conference on Web Science (Paris, France). WebSci.
- Earle, P., Bowden, D., & Guy, M. (2012). Twitter earthquake detection: earthquake monitoring in a social world. Annals of Geophysics, 54(6).
- Gallego, J., Martínez, J. D., Munger, K., & Vásquez-Cortés, M. (2019). Tweeting for peace: Experimental evidence from the 2016 Colombian Plebiscite. Electoral Studies, 62, 102072.
- Gholampour, V., & Van Wincoop, E. (2017). What can we learn from Euro-Dollar Tweets? (No. w23293). National Bureau of Economic Research.
- Glaeser, E. L., Kim, H., & Luca, M. (2017). Nowcasting the local economy: Using yelp data to measure economic activity (No. w24010). National Bureau of Economic Research.
- Glaeser, E. L., Kim, H., & Luca, M. (2018). Nowcasting gentrification: using yelp data to quantify neighborhood change. In AEA Papers and Proceedings (Vol. 108, pp. 77-82).
- Halberstam, Y., & Knight, B. (2016). Homophily, group size, and the diffusion of political information in social networks: Evidence from Twitter. Journal of Public Economics, 143, 73-88.
- Massey, P. M., Leader, A., Yom-Tov, E., Budenz, A., Fisher, K., & Klassen, A. C. (2016). Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter. Journal of Medical Internet Research, 18(12), e318.
- Menon, S., Berger-Wolf, T., Kiciman, E., Joppa, L., Stewart, C. V., Parham, J., J. Crall, J. Holmberg, & Van Oast, J. (2017). Animal Population Estimation Using Flickr Images. In Proceedings of International Workshop on the Social Web for Environmental and Ecological Monitoring.
- Ramakrishnan, N., Butler, P., Muthiah, S., Self, N., Khandpur, R., Saraf, P., Wang, W., Cadena, J., Kumar, A., Korkmaz, G., Kuhlman, C. J., Marathe, A., Zhao, L., Hua, T., Chen, F., Lu, C. T., Huang, B., Srinivasan, A., Trinh, K., . . . Mares, D. (2014). ‘Beating the news’ with EMBERS: forecasting civil unrest using open source indicators. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1799-1808).
- Singh, L., Wahedi, L., Wang, Y., Wei, Y., Kirov, C., Martin, S., Donato, K., Liu, Y., & Kawintiranon, K. (2019). Blending noisy social media signals with traditional movement variables to predict forced migration. In Proceedings of the ACM International Conference on Knowledge Discovery & Data Mining (pp. 1975-1983).
Social Media Population Studies
- Alhabash, S., & Ma, M. (2017). A Tale of Four Platforms: Motivations and Uses of Facebook, Twitter, Instagram, and Snapchat Among College Students? Social Media + Society.
- Blank, G. (2017). The digital divide among Twitter users and its implications for social research. Social Science Computer Review, 35(6), 679-697.
- Ernala, S. K., Burke, M., Leavitt, A., & Ellison, N. B. (2020). How well do people report time spent on Facebook? An evaluation of established survey questions with recommendations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp 1-14).
- Perrin, A., & Anderson, M. (2019). Share of US adults using social media, including Facebook, is mostly unchanged since 2018. Pew Research Center.
- Sloan, L. (2017). Who tweets in the United Kingdom? Profiling the Twitter population using the British social attitudes survey 2015. Social Media+ Society, 3(1), 2056305117698981.
- Wojcik, S. & Hughes, A. (2019). Sizing up Twitter users. Pew Research Center.