Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Seminar "Computation for Social Science"

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Wintersemester 2019 / 2020

Newest announcements:

  • The kick-off meeting slides can be found here: PDF
  • Registration is first come first serve, as soon as WS19 registrations open.
  • For those who register, the new syllabus and list of papers to choose from will be provided ASAP. Below is the old version.

Course Description

This seminar introduces a social science toolkit to students of Web Science. For an accurate understanding of social phenomena, research designs depend on methodical approaches to societal and political changes and problems. This attitude is the bread and butter of social science, but the waves of data in the digital age create urgent demand for computational processing of socially generated data. As result, interdisciplinary thinking that merges web and social science is a future skill sought by academia, industry, and society. In this seminar, we try this out ourselves.

Good term papers are encouraged to be published, or to be made into Master theses.

Materials

Not required but recommended:

  • Bit by Bit: Social Research in the Digital Age by Matthew J. Salganik, Chapters 1 and 2 
  • Analyzing Political Communication with Digital Trace Data by Andreas Jungherr, for an impression of how digital data changes social science.

Objectives

  1. designing research that is relevant for social science, society and politics
  2. building teams with social scientists that enmesh comparative skills
  3. utilizing web data with a realistic understanding of underlying social and political dynamics

Dates and rooms

TBA

Instructions for term paper and presentation

Pick one paper from a topic below. Write your own term paper based on this research and topic. You can either measure a concept that you have encountered in this research, or propose an incremental contribution that arises from what you have learned. Look at the intro slides for tips.

Papers and topics

The following literature comes from diverse social science families (because they are chosen by topic, not academic discipline). If you want to pick a different paper outside of the list, no problem. Just convince me via email whether they contain 1 and 2 in sufficient manner:

  1. A solid theoretical framework that avoids bias about societal dynamics
  2. A computational methodology

  • Core ideas
    • Michel et al 2011, Quantitative analysis of culture using millions of digitized books. Science, 331:6014
    • DiMaggio, Paul. "Adapting computational text analysis to social science (and vice versa)." Big Data & Society 2.2 (2015)
    • Andreas Jungherr, Harald Schoen, Oliver Posegga, and Pascal Jürgens. "Digital Trace Data in the Study of Public Opinion: An Indicator of Attention Toward Politics Rather Than Political Support". In: Social Science Computer Review 35.3 (2017), pp. 336-356. doi: 10.1177/0894439316631043
    • Panagiotis Takis Metaxas, Eni Mustafaraj, and Daniel Gayo-Avello. 2011. "How (not) to predict elections". In SocialCom 2011: The 3rd IEEE International Conference on Social Computing, ed. by Alessandro Vinciarelli, Maja Pantic, Elisa Bertino, and Justin Zhan, 165-171. Washington, DC: IEEE. doi:10.1109/PASSAT/SocialCom.2011.98
    • James Howison, Andrea Wiggins, and Kevin Crowston. 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.
    • Andreas Jungherr and Yannis Theocharis. 2017. "The Empiricist’s Challenge: Asking 8 Meaningful Questions in Political Science in the Age of Big Data". Journal of Information Technology & Politics 14 (1): 97-109. doi:10.1080/19331681.2017.1312187.
    • Andreas Jungherr and Pascal Jürgens. 2013. "Forecasting the pulse: How deviations from regular patterns in online data can identify offline phenomena". Internet Research 23 (5): 589-607. doi:10.1108/IntR-06-2012-0115.
    • Michael F. Schober, Josh Pasek, Lauren Guggenheim, Cliff Lampe, and Frederick G. Conrad. 2016. "Social Media Analyses for Social Measurement". Public Opinion Quarterly 80 (1): 180-211. doi:10.1093/poq/nfv048.
    • Fernando Diaz et al. "Online and social media data as a flawed continuous panel survey". In: PLoS ONE 11.1 (2016). e0145406. doi:10.1371/journal.pone.0145406.
    • Lilli Japec, Frauke Kreuter, Marcus Berg, Paul Biemer, Paul Decker, Cliff Lampe, Julia Lane, Cathy O’Neil, and Abe Usher. "Big Data in Survey Research: AAPOR Task Force Report". In: Public Opinion Quarterly 79.4 (2015), pp. 839-880. doi: 10.1093/poq/nfv039
    • David Lazer et al. "The Parable of Google Flu: Traps in Big Data Analysis". In: Science 343.6176 (2014), pp. 1203-1205. doi: 10.1126/science.1248506
    • Grimmer, Justin and Gary King. 2011. "General Purpose Computer-Assisted Clustering and Conceptualization" Proceedings of the National Academy of Sciences 108(7), 2643-2650
    • Lowe, W. and Benoit, K. (2013). Validating estimates of latent traits from textual data using human judgment as a benchmark. Political Analysis, 21(3):298313.
    • Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. (2011). Political polarization on twitter. Icwsm, 133, 89-96.
  • Misinformation, Polarization, Negativity
    • Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election.
      National Bureau of Economic Research.
    • Boyd, L. & Vraga, E (2015) In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media, Journal of Communication, 65 (4): 619-638.
    • Christopher Bail, Lisa Argyle, Taylor Brown, John Bumpus, Haohan Chen, M.B. Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. 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.
    • Auter, Z. J., & Fine, J. A. (2016). Negative campaigning in the social media age: Attack
      advertising on Facebook. Political Behavior, 38(4), 999-1020.
    • Jaidka, Kokil and Zhou, Alvin and Lelkes, Yphtach, Brevity is the soul of Twitter: The constraint affordance and political discussion (November 20, 2018).
    • 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.
    • Theocharis, Y., Lowe, W., Van Deth, J.W. and García-Albacete, G., 2015. Using Twitter to mobilize protest action: online mobilization patterns and action repertoires in the Occupy Wall Street, Indignados, and Aganaktismenoi movements. Information, Communication & Society, 18(2), pp.202-220.
  • Political applications of text data
    • Pablo Barberá. 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.
    • Monroe, Burt L., Michael P. Colaresi, and Kevin M. Quinn. "Fightin’words: Lexical feature selection and evaluation for identifying the content of political conflict." Political Analysis 16.4 (2008): 372-403.
    • Iyengar S. and S. J. Westwood (2015) Fear and Loathing across Party Lines: New Evidence on Group Polarization, American Journal of Political Science. Vol. 59, No. 3 (July 2015), pp. 690-707
    • King, G, J. Pan & M. Roberts, (May 2016) How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, Harvard University, http://gking.harvard.edu/files/gking/files/50c.pdf?m=1463587807
    • Marco Bastos and Dan Mercea. 2018. "Parametrizing Brexit: Mapping Twitter political space to parliamentary constituencies". Information, Communication & Society. doi:10.1080/1369118X.2018.1433224
    • Deen Freelon. "Analyzing online political discussion using three models of democratic
      communication". New Media & Society 12.7 (2010), pp. 1172-1190. doi: 10.1177/1461444809357927
    • Maurice Vergeer, Liesbeth Hermans, and Steven Sams. "Online social networks and micro-blogging in political campaigning: The exploration of a new campaign tool and a new campaign style". Party Politics 19.3 (2013), pp. 477-501.
    • Yu, Bei, Stefan Kaufmann, and Daniel Diermeier. 2008. "Classifying Party Affiliation
      from Political Speech". Journal of Information, Technology, and Politics. 5(1).
    • Hillard, Dustin, Stephen Purpura and John Wilkerson. 2007. "Computer Assisted Classification for Mixed Methods Social Science Research". Journal of Information, Technology, and Politics.
  • Understanding online attention
    • W. Russell Neuman, Lauren Guggenheim, S. Mo Jang, and Soo Young Bae. 2014. "The
      Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data". Journal of Communication 64 (2): 193-214. doi:10.1111/jcom.12088.
    • Sebastian Stier, Arnim Bleier, Haiko Lietz, and Markus Strohmaier. 2018b. "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
    • Andreas Jungherr, Harald Schoen, and Pascal Jürgens. 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.
    • Elizabeth Dubois and Devin Gaffney. 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
    • Todd Graham, Marcel Broersma, Karin Hazelhoff, and Guido van‘t Haar. 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
    • Daniel Kreiss. 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.
    • Yu-Ru Lin, Brian Keegan, Drew Margolin, and David Lazer. 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.
    • Sharad Goel et al. "The Structural Virality of Online Diffusion". In: Management Science 62.1 (2015), pp. 180-196. doi: 10.1287/mnsc.2015.2158
    • Andreas Jungherr. "The logic of political coverage on Twitter: Temporal dynamics and content". In: Journal of Communication 64.2 (2014), pp. 239-259. doi: 10.1111/jcom.12087
    • Jacob Eisenstein, Brendan O’Connor, Noah A. Smith, and Eric P. Xing. "Diffusion of lexical variation in online social media" (2014) PLOS-ONE
    • Marco T. Bastos, Dan Mercea, and Arthur Charpentier. "Tents, Tweets, and Events: The
      Interplay Between Ongoing Protests and Social Media". In: Journal of Communication 65.2 (2015), pp. 320-350. doi: 10.1111/jcom.12145.

Public datasets (optional)

  • Lars Kaczmirek, Philipp Mayr, Ravi Vatrapu, Arnim Bleier, Manuela Blumenberg, Tobias Gummer, Abid Hussain, Katharina Kinder-Kurlanda, Kaveh Manshaei, Mark Thamm, Katrin Weller, Alexander Wenz, and Christof Wolf. 2013. "Social Media Monitoring of the Campaigns for the 2013 German Bundestag Elections on Facebook and Twitter". arXiv, no. 1312.4476v2. https://arxiv.org/abs/1312.4476.
  • Sebastian Stier, Arnim Bleier, Haiko Lietz, and Markus Strohmaier. 2018b. "Election Campaigning on Social Media: Politicians, Audiences and the Mediation of Political Communication on Facebook and Twitter". Political Communication 35 (1): 50-74.

Lehrende

  • han@uni-koblenz.de
  • Wissenschaftlicher Mitarbeiter
  • B 006
  • +49 261 287-2864