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

Seminar "Computation for Social Science"

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Winter Term 2020 / 2021

Newest Announcements

  • Instructions for how to apply will be sent out.

Dates

  • November 2, 2020: Intro meeting (2pm)
  • November 30, 2020: Mini-conference of your slide presentations (10am)
  • December 7, 2020: deadline for draft paper
  • December 14, 2020: deadline for paper

Course Aims

Complicated phenomena require computational processing of socially generated data. We explore social science questions that are relevant to Data Science.

Introductory reading, 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

  • identifying the state of research in the intersection of social science and WDS
  • understanding your potential contributions in understanding social and political dynamics

Course structure

Pick one paper from the list below and introduce it at the mini-conference. Write your own term paper that describes this paper and the state of the art (cite papers that relate to it) and how it could be improved even more. Submit the term paper draft, get my comments and submit the final term paper.

Grading categories

  • Cite at least 10 social science-related papers. The quality of your term paper will be largely decided by how well you have understood them.
  • Demonstrate that you understood what the paper did and decscribe whether that is still the state of the art, based on your cited papers.
  • Feasibility of your proposed improvement (not too simple, not too ambitious)
  • Structure (academic format and order of sections)
  • English syntax, grammar, and style

Papers (you may suggest others)

  • 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.

Lecturers

  • han@uni-koblenz.de
  • Scientific Employee
  • B 006
  • +49 261 287-2864