Stuttgart’s Black Thursday on Twitter: Mapping Political Protests with Social Media Data



Event detection based on textual data is an approach often used in the social sciences. The method has been used predominantly in the fields of international politics (Schrodt 2010) and public opinion research (Landmann and Zuell 2008). Event detection presupposes that major events leave traces in textual documents. By automatically identifying events in publicly available documents, researchers can establish timelines of events relevant to their research. For example, in international politics, researchers work on how to reliably identify political actors, time and topics from official documents, hoping to establish comprehensive and detailed maps of international treaties and conflicts. Based on these maps, they aim to develop models of the dynamics of conflict (Brandt et al. 2011). In public opinion research, one goal is to automatically deduce major events from newspaper coverage. This might be a first step in calculating the impact of these events on changes in public opinion (Landmann and Zuell 2008).


Social Media Event Detection Twitter User Social Media Data Twitter Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Allan, J. (ed.) (2002) Topic Detection and Tracking: Event-based Information Organization (Boston: Kluwer Academic Publishers).Google Scholar
  2. Asur, S. and Huberman, B.A. (2010) Predicting the Future with Social Media, available at: Scholar
  3. Becker, H., Naaman, M. and Gravano, L. (2011) ‘Beyond Trending Topics: Real-World Event Identification on Twitter’, in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (Menlo Park, CA: The AAAI Press), pp. 438–441.Google Scholar
  4. Bieber, C. (2010) Politik Digital: Online zum Wähler (Salzhemmendorf: Blumenkamp Verlag).Google Scholar
  5. Bilger, C. and Raidt, E. (2011) ‘Schwarzer Donnerstag: Ein lauter Tag hallt nach, Stuttgarter Zeitung’, available at: inhalt.schwarzer-donnerstag-ein-lauter-tag-hallt-nach.94770415-a4dd-4957-8422-a689eaa2909e.html.Google Scholar
  6. Bird, S., Loper, E. and Klein, E. (2009) Natural Language Processing with Python(Sebastopol, CA: O’Reilly Media).Google Scholar
  7. Brandt, P.T., Freeman, J.R. and Schrodt, P.A. (2011) ‘Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict’, Conflict Management and Peace Science, 28, 41–64.CrossRefGoogle Scholar
  8. Bruns, A. and Burgess, J. (2011) ‘#Ausvotes: How Twitter Covered the 2010 Australian Federal Election’, Communication, Politics & Culture, 44 (2), 37–56.Google Scholar
  9. Bruns, A. and Liang, Y.E. (2012) ‘Tools and Methods for Capturing Twitter Data During Natural Disasters’, First Monday 17 (4), available at: Scholar
  10. Bunse. V. (2010) ‘#S21: Man prügelt seine Bürger nicht …Kaffee bei mir?’, available at:
  11. Busemann, K. and Gscheidle, C. (2012) ‘Web 2.0: Habitualisierung der Social Communitys’, Media Perspektiven, 7–8, 380–390.Google Scholar
  12. Cha, M., Haddadi, H., Benevenuto, F. and Gummadi, K.P. (2010) ‘Measuring User Influence in Twitter: The Million Follower Fallacy’, in Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (Menlo Park, CA: The AAAI Press), pp. 10–17.Google Scholar
  13. Chakrabarti, D. and Punera, K. (2011) ‘Event Summarization Using Tweets’, in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media(Menlo Park, CA: The AAAI Press), pp. 66–73.Google Scholar
  14. Chew C. and Eysenbach G. (2010) ‘Pandemics in the Age of Twitter: Content Analysis of Tweets During the 2009 H1N1 Outbreak’, PLoS ONE 5/11, e14118.CrossRefGoogle Scholar
  15. Crawford, K. (2009) ‘Following You: Disciplines of Listening in Social Media’, Continuum: Journal of Media & Cultural Studies, 23 (4), 525–535.CrossRefGoogle Scholar
  16. David, G. (2010) ‘Camera Phone Images, Videos and Live Streaming: A Contemporary Visual Trend’, Visual Studies, 25 (1), 89–98.CrossRefGoogle Scholar
  17. Gabriel, O.W., Schoen, H. and Faden-Kuhne, K. (2013) Die Volksabstimmung über ‘Stuttgart 21’ (Leverkusen: Opladen Budrich).Google Scholar
  18. Gayo-Avello, D., Metaxas, P.T. and Mustafaraj, E. (2011) ‘Limits of Electoral Predictions Using Twitter’, in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (Menlo Park, CA: The AAAI Press), pp. 490–493.Google Scholar
  19. Golder, S.A. and Macy, M.W. (2011) ‘Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures’, Science, 333 (6051), 1878–1881.CrossRefGoogle Scholar
  20. González-Bailón, S., Borge-Holthoefer, J., Rivero, A. and Moreno, Y. (2011) ‘The Dynamics of Protest Recruitment Through an Online Network’, Scientific Reports 1, Article number 197.Google Scholar
  21. González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J. and Moreno, Y. (2012) ‘Assessing the Bias in Communication Networks Sampled from Twitter’, available at: Scholar
  22. Hoffmann, T. (2010) ‘Netzgemeinde Mobilisiert für Gauck’,, available at: Scholar
  23. Jackson, N. and Lilleker, D. (2011) ‘Microblogging, Constituency Service and Impression Management: UK MPs and the use of Twitter’, The Journal of Legislative Studies, 17 (1), 86–105.CrossRefGoogle Scholar
  24. Jakat, L. (2010) ‘ “Astroturfing” — Geheimkampf um Botschaften im Netz’,, available at: Scholar
  25. Jungherr, A. (2009) The DigiActive guide to Twitter for Activism, available at: Scholar
  26. Jungherr, A. (2012) ‘The German Federal Election of 2009: The Challenge of Participatory Cultures in Political Campaigns’, Transformative Works and Fan Activism, 10, available at: Scholar
  27. Jungherr, A. and Jürgens, P. (2013) ‘Forecasting the Pulse: How Deviations from Regular Patterns in Online Data Can Identify Offline Phenomena’, Internet Research 23(5), 589–607.CrossRefGoogle Scholar
  28. Jungherr, A., Jürgens, P. and Schoen, H. (2012) ‘Why the Pirate Party Won the German Election of 2009 or the Trouble with Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. “Predicting Elections with Twitter: What 140 Characters Reveal About Political Sentiment”’, Social Science Computer Review 30 (2), 229–234.CrossRefGoogle Scholar
  29. Jürgens, P. (2010) Stell’ Dir vor du twitterst und keiner hört zu. Themen und Öffentlichkeit auf Twitter, Unpublished Master’s Thesis at the Institut für Publizistik, Universität Mainz, Germany.Google Scholar
  30. Jürgens, P. and Jungherr, A. (2011) ‘Wahlkampf vom Sofa aus: Twitter im Bundestagswahlkampf 2009’, in E.J. Schweitzer and S. Albrecht (eds.), Das Internet im Wahlkampf: Analysen zur Bundestagswahl2009 (Wiesbaden: VS Verlag für Sozialwissenschaften), pp. 201–225.CrossRefGoogle Scholar
  31. Jürgens, P. and Jungherr, A. (2014) ‘The Use of Twitter During the 2009 German National Election’, German Politics. In Publication.Google Scholar
  32. Jürgens, P., Jungherr, A. and Schoen, H. (2011) ‘Small Worlds with a Difference: New Gatekeepers and the Filtering of Political Information on Twitter’, in Proceedings of the ACM WebSci’11, New York, NY, ACM, available at: Scholar
  33. Kleinberg, J. (2003) ‘Bursty and Hierachical Structure in Streams’, Data Mining and Knowledge Discovery, 7 (4), 373–397.CrossRefGoogle Scholar
  34. Kuhn, J. (2010) ‘Live aus der Baumkrone’,, available at: Scholar
  35. Landmann, J. and Zuell, C. (2008) ‘Identifying Events Using Computer-Assisted Text Analysis’, Social Science Computer Review, 26, 483–497.CrossRefGoogle Scholar
  36. Lang, K. and Lang, G.E. (1953) ‘The Unique Perspective of Television and Its Effect: A Pilot Study’, American Sociological Review, 18 (1), 3–12.CrossRefGoogle Scholar
  37. Mader, F. (2010) ‘Schwabenstreich im Netz’,, available at: Scholar
  38. Maireder A. and Schwarzenegger, C. (2011) ‘A Movement of Connected Individuals: Social Media in the Austrian Student Protests 2009’, Information, Communication & Society 15 (2), 171–195.CrossRefGoogle Scholar
  39. Marwick, A.E. and Boyd, D. (2011) ‘I Tweet Honestly, I Tweet Passionately: Twitter Users, Context Collapse, and the Imagined Audience’, New Media & Society, 13 (1), 114–133.CrossRefGoogle Scholar
  40. Nikolov, S. (2012) Trend or No Trend: A Novel Nonparametric Method for Classifying Time Series, Unpublished Master’s Thesis, Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
  41. Petrovic, S., Osborne, M. and Lavrenko, V. (2010) ‘Streaming First Story Detection with Application to Twitter’, in NAACL ‘10: Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (Stroudsburg, PA: ACL), pp. 181–189.Google Scholar
  42. Pfeiffer, T. (2010a) ‘Stuttgart21 im Spiegel von Twitter’, web evangelisten, available at: Scholar
  43. Pfeiffer, T. (2010b) ‘Stuttgart 21: Auf Facebook redet man nicht mit “den Anderen” ‘, web evangelisten, available at: stuttgart21-auf-facebook/.Google Scholar
  44. Pickard, V.W. (2006) ‘United yet Autonomous: Indymedia and the Struggle to Sustain a Radical Democratic Network’, 28 (3), 315–336.Google Scholar
  45. Reißmann, O. (2010) ‘Riesenwut auf #S21-Polizeieinsatz’, Spiegel Online, available at:,1518,720701,00.html.Google Scholar
  46. Sakaki, T., Okazaki, M. and Matsuo, Y. (2010) ‘Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors’, in Proceedings of the 19th International World Wide Web Conference, WWW ‘10 (New York, NY: ACM), pp. 851–860.CrossRefGoogle Scholar
  47. Schimmelpfennig, M. (2010) ‘Auf Twitter mehr Bewegen: Ideen, den Widerstand zu stärken’, Copywriting, available at: Scholar
  48. Schrodt, P.A. (2010) ‘Automated Production of High-Volume, Near-Real-Time Political Event Data’, paper presented at the Annual Meeting of the American Political Science Association, Washington, 2–5 September 2010.Google Scholar
  49. Segerberg, A. and Bennett, L. (2011) ’social Media and the Organization of Collective Action: Using Twitter to Explore the Ecologies of Two Climate Change Protests’, The Communication Review, 14 (3), 197–215.CrossRefGoogle Scholar
  50. Shamma, D.A., Kennedy, L. and Churchill, E. F. (2009) ‘Tweet the Debates: Understanding Community Annotation of Uncollected Sources’, in Proceedings of the First SIGMM Workshop on Social Media, WSM ‘09 (New York, NY: ACM), pp. 3–10.Google Scholar
  51. Shamma, D.A., Kennedy, L. and Churchill, E.F. (2011) ‘Peaks and Persistence: Modeling the Shape of Microblog Conversations’, in Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work (New York, NY: ACM), pp. 355–358.CrossRefGoogle Scholar
  52. Smith, A. (2011) Twitter and Social Networking in the 2010 Midterm Elections, Pew Internet & American Life Project, available at: ~/media//Files/Reports/2011/PIP-Social-Media-and-2010-Election.pdf.Google Scholar
  53. Smith, A. and Brenner, J. (2012) Twitter Use 2012, Pew Internet & American Life Project, available at: aspx.Google Scholar
  54. Stegers, F. (2010) ‘The Revolution Will Be Televised Streamed via Mobile’,, available at: Scholar
  55. (2010) ‘Die ersten Bäume sind gefallen’, available at:
  56. Ternieden, H. (2010) ‘Machtdemonstration gegen Mappus’, Spiegel Online, available at:,1518,720840,00.html.Google Scholar
  57. Vergeer, M., Hermans, L. and Sams, S. (2011) ‘Is the Voter only a Tweet Away? Micro-Blogging During the 2009 European Parliament Election Campaign in the Netherlands’, First Monday, 16 (8), available at: Scholar
  58. Verma, S., Vieweg, S., Corvey, W.J., Palen, L, Martin, J.H., Palmer, M., Schram, A. and Anderson, K.M. (2011) ‘Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets During Mass Emergency’, in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (Menlo Park, CA: The AAAI Press), pp. 386–392.Google Scholar
  59. Weng, J. and Lee, B. (2011) ‘Event Detection in Twitter’, in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (Menlo Park, CA: The AAAI Press), pp. 401–408.Google Scholar
  60. White, J.S., Matthews, J.M. and Stacy, J.L. (2012) ‘Coalmine: An Experience in Building a System for Social Media Analytics’, Proc. SPIE 8408, Cyber Sensing 2012, 84080A.Google Scholar
  61. Wienand, L. (2010) ‘Mobiles Kamera-Einsatzkommando: Die “Volksreporter” von Stuttgart 21’, Rhein-Zeitung, available at: nachrichten/computerundmedia_artikel,-Mobiles-Kamera-Einsatzkommando-Die-Volksreporter-von-S21-_arid,151852.html.Google Scholar

Copyright information

© Andreas Jungherr and Pascal Jürgens 2014

Authors and Affiliations

There are no affiliations available

Personalised recommendations