Week 6: How to Find A Research Question? & Data Lab

After getting the hang of collecting data on Twitter and preparing them for analysis, it is time for you to design your own research project. As always, the best research questions are not purely data driven or solely motivated by opportunities provided by access to specific data sets. Instead, make sure to anchor your research design within larger questions connected with political or social science.

One approach to finding a promising question might be: “What aspect of social or political life is closely connected with political Twitter activity and might, therefore, be illustrated by data collected on Twitter?”

Also, in checking up on the promise of your project you might ask yourself:

“Given the findings presented in my awesome study, what have we learned about the world that we didn’t know before?”

(Also known in some quarters as the Rasmus-question).

In the beginning, this might seem a little awkward or challenging but stick with it. If you will not ask these questions someone else will. This way, you’ll make sure you have a good answer once you are asked in front of a room full of people. Also, choosing projects based on answers to these questions will make for rewarding projects and ultimately better chances for publication.

If you get stuck in thinking about promising research questions have a look at Howard S. Becker’s classic Tricks of the Trade: How to think about your research while you’re doing it. This should get you unstuck pretty quickly.

Required Readings:

Before you settle on a question, make sure to read up on what has already been done with Twitter data. A helpful overview on Twitter-based research on electoral campaigns can be found in the required readings for this session:

• Jungherr, A. (2016). Twitter use in election campaigns: A systematic literature review. Journal of Information Technology & Politics, 13 (1), 72–91. doi:10.1080/19331681.2015.1132401.

Background Readings:

How to Find a Research Question?

• Becker, H. S. (1998). Tricks of the trade: How to think about your research while you’re doing it. Chicago, IL: The University of Chicago Press.

• Jungherr, A., & Theocharis, Y. (2017). The Empiricist’s Challenge: Asking Meaningful Questions in Political Science in the Age of Big Data. Journal of Information Technology & Politics, 14(2), 97-109. doi:10.1080/19331681.2017.1312187.

Conceptual Issues in Working with Digital Trace Data:

After getting a first overview on some of the work that has been done on and with Twitter in political contexts, it might pay off to read up on some of the conceptual issues in the use of digital trace data for social science research. Here is a short list of papers offering you a good window into current methodological debates.

• Bail, C. (2014). The cultural environment: Measuring culture with big data. Theory and Society, 43(3-4), 465–482. doi:10.1007/s11186-014-9216-5.

• Diaz, F., Gamon, M., Hofman, J. M., Kıcıman, E., & Rothschild, D. (2016). Online and social media data as an imperfect continuous panel survey. PLoS One, 11(1), e0145406. doi:10.1371/journal.pone.0145406.

• Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12), 767–797.

• Japec, L., Kreuter, F., Berg, M., Biemer, P., Decker, P., Lampe, C., Lane, J., O’Neill, C., & Usher, A. (2015). Big data in survey research: Aapor task force report. Public Opinion Quarterly, 79(4), 839–880. doi:10.1093/poq/nfv039.

• Jungherr, A., Schoen, H., & Jürgens, P. (2016). The mediation of politics through Twitter: An analysis of messages posted during the campaign for the German federal election 2013. Journal of Computer-Mediated Communication, 21 (1), 50–68. doi:10.1111/jcc4.12143.

• Jungherr, A., Schoen, H., Posegga, O., & Jürgens, P. (2017). Digital trace data in the study of public opinion: An indicator of attention toward politics rather than political support. Social Science Computer Review, 35 (3), 336–356. doi:10.1177/0894439316631043.

• Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of google flu: Traps in big data analysis. Science, 343 (6176), 1203–1205. doi:10.1126/science.1248506.

• Ribeiro, F. N., Araújo, M., Gonçalves, P., Gonçalves, M. A., & Benevenuto, F. (2016). Sentibench a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(23), 1–29. doi:10.1140/epjds/s13688-016-0085-1.

• Salganik, M. J. (2017). Bit by bit: Social research in the digital age. Princeton, NJ: Princeton University Press.

• Schober, M. F., Pasek, J., Guggenheim, L., Lampe, C., & Conrad, F. G. (2016). Social media analyses for social measurement. Public Opinion Quarterly, 80(1), 180–211. doi:10.1093/poq/nfv048.

Case Studies Illustrating Different Approaches to the Use of Twitter data:

Literature reviews and conceptual debates are all well and good but nothing stimulates your intuition as reading primary research directly. For this purpose, I provided you with a slightly longer list on innovative studies focusing on political uses of Twitter or using Twitter data in research. Of course, this can only be a small selection and is by no means exhaustive. Still, this should provide you with a running start.

• Alvarez, R. M. (Ed.). (2016). Computational social science: Discovery and prediction. New York, NY: Cambridge University Press. doi:10.1017/CBO9781316257340.

• Bail, C., Argyle, L., Brown, T., Bumpus, J., Chen, H., Hunzaker, M., . . . Volfovsky, A. (2018). Exposure to opposing views can increase political polarization: Evidence from a large-scale Field experiment on social media. SocArXiv. doi:10.17605/OSF.IO/4YGUX.

• Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Analysis, 23 (1), 76–91. doi:10.1093/pan/mpu011.

• Bastos, M. T., Mercea, D., & Charpentier, A. (2015). Tents, tweets, and events: The interplay between ongoing protests and social media. Journal of Communication, 65(2), 320–350. doi:10.1111/jcom.12145.

• Bastos, M., Piccardi, C., Levy, M., McRoberts, N., & Lubell, M. (2018). Coreperiphery or decentralized? topological shifts of specialized information on twitter. Social Networks, 52(1), 282–293. doi:10.1016/j.socnet.2017.09.006.

• Bastos, M. & Mercea, D. (2018). Parametrizing brexit: Mapping twitter political space to parliamentary constituencies. Information, Communication & Society. doi:10.1080/1369118X.2018.1433224.

• Conover, M. D., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., & Menczer, F. (2011). Political polarization on Twitter. In N. Nicolov, J. G. Shanahan, L. Adamic, R. Baeza-Yates, & S. Counts (Eds.), Icwsm 2011: Proceedings of the 5th international aaai conference on weblogs and social media (pp. 89–96). Menlo Park, CA: Association for the Advancement of Artificial Intelligence (AAAI).

• Dodds, P. S., Harris, K. D., Klouman, I. M., Bliss, C. A., & Danforth, C. M. (2011). Temporal patterns of happiness and information in a global-scale social network: Hedonometrics and Twitter. PLoS One, 6(12), 1–26. doi:10.1371/journal.pone.0026752.

• Dubois, E. & Gaffney, D. (2014). The multiple facets of influence: Identifying political influentials and opinion leaders on Twitter. American Behavioral Scientist, 58(10), 1260–1277. doi:10.1177/0002764214527088.

• Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2016). The structural virality of online diffusion. Management Science, 62 (1), 180–196. doi:10.1287/mnsc.2015.2158.

• Graham, T., Broersma, M., Hazelhoff, K., & van ’t Haar, G. (2013). Between broadcasting political messages and interacting with voters: The use of Twitter during the 2010 UK general election campaign. Information, Communication & Society, 16 (5), 692–716. doi:10.1080/1369118X.2013.785581.

• Jungherr, A. (2014). The logic of political coverage on Twitter: Temporal dynamics and content. Journal of Communication, 64 (2), 239–259. doi:10.1111/jcom.12087.

• Jungherr, A. (2015). Analyzing political communication with digital trace data: The role of twitter messages in social science research. Cham, CH: Springer.

• Jungherr, A. & Jürgens, P. (2014). Through a glass, darkly: Tactical support and symbolic association in Twitter messages commenting on Stuttgart 21. Social Science Computer Review, 32(1), 74–89. doi:10.1177/0894439313500022.

• Jürgens, P., Jungherr, A., & Schoen, H. (2011). Small worlds with a difference: New gatekeepers and the filtering of political information on Twitter. In D. D. Roure & S. Poole (Eds.), Websci 2011: Proceedings of the 3rd international web science conference (21). New York: ACM. doi:10.1145/2527031.2527034.

• Kreiss, D. (2016). Seizing the moment: The presidential campaigns’ use of Twitter during the 2012 electoral cycle. New Media & Society, 18(8), 1473–1490. doi:10.1177/1461444814562445.

• Lin, Y.-R., Keegan, B., Margolin, D., & Lazer, D. (2014). Rising tides or rising stars? Dynamics of shared attention on Twitter during media events. PLoS One, 9 (5), e94093. doi:10.1371/journal.pone.0094093.

• Metaxas, P. T., Mustafaraj, E., & Gayo-Avello, D. (2011). How (not) to predict elections. In A. Vinciarelli, M. Pantic, E. Bertino, & J. Zhan (Eds.), Socialcom 2011: The 3rd ieee international conference on social computing (pp. 165–171). Washington, DC: IEEE. doi:10.1109/PASSAT/SocialCom.2011.98.

• Munger, K. (2017). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 39(3), 629–649. doi:10.1007/s11109-016-9373-5.

• Neuman, W. R., Guggenheim, L., Jang, S. M., & Bae, S. Y. (2014). The dynamics of public attention: Agenda-setting theory meets big data. Journal of Communication, 64(2), 193–214. doi:10.1111/jcom.12088.

• Rogers, R. (2013a). Debanalizing Twitter: The transformation of an object of study. In H. Davis, H. Halpin, A. Pentland, M. Bernstein, & L. Adamic (Eds.), Websci 2013: Proceedings of the 5th annual acm web science conference (pp. 356–365). New York, NY: ACM. doi:10.1145/2464464.2464511.

• Shamma, D. A., Kennedy, L., & Churchill, E. F. (2011). Peaks and persistence: Modeling the shape of microblog conversations. In P. Hinds, J. C. Tang, J. Wang, J. Bardram, & N. Ducheneaut (Eds.), Cscw 2011: Proceedings of the acm 2011 conference on computer supported cooperative work (pp. 355–358). New York, NY: ACM. doi:10.1145/1958824.195887810.1145/1958824.1958878.

• Stier, S., Bleier, A., Lietz, H., & Strohmaier, M. (2018). Election campaigning on social media: Politicians, audiences and the mediation of political communication on facebook and twitter. Political Communication, 35(1), 50–74. doi:10.1080/10584609.2017.1334728.

• Theocharis, Y., Barberá, P., Fazekas, Z., Popa, S. A., & Parnet, O. (2016). A bad workman blames his tweets: The consequences of citizens’ uncivil twitter use when interacting with party candidates. Journal of Communication, 66(6), 1007–1031. doi:10.1111/jcom.12259.

• Theocharis, Y., Vitoratou, S., & Sajuria, J. (2017). Civil society in times of crisis: Understanding collective action dynamics in digitally-enabled volunteer networks. Journal of Computer-Mediated Communication, 22(5), 248–265. doi:10.1111/jcc4.12194.

• Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV and simultaneous comments on Twitter. Social Science Computer Review, 33(3), 259–276. doi:10.1177/0894439314537886.

Public Datasets:

In case you are interested in Twitter activities of German politicians there are potential some short-cuts available to you. The GESIS has published two dataset documenting all publicly available tweets published by candidates running in the 2013 and 2017 German Federal Election. The datasets also contains mentions by other Twitter users of these politicians and a set of tweets containing topically relevant hashtags.

• Kaczmirek, L., Mayr, P., Vatrapu, R., Bleier, A., Blumenberg, M., Gummer, T., Hussain, A., Kinder-Kurlanda, K., Manshaei, K., Thamm, M., Weller, K., Wenz, Al, & Wolf, C. (2013). Social media monitoring of the campaigns for the 2013 german bundestag elections on facebook and twitter. arXiv (1312.4476v2).

• Stier, S., Bleier, A., Bonart, M., Mörsheim, F., Bohlouli, M., Nizhegorodov, M., Posch, L., Maier, Jürgen, Rothmund, T., & Staab, S. (2018). Systematically monitoring social media: The case of the german federal election 2017. Köln, DE: GESIS Papers.

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Week 5—Week 8

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