Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Onderwijs Informatica en Informatiekunde

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Social computing

Course code:INFOMSOC
Credits:7.5 ECTS
Period:period 4 (week 17 through 26, i.e., 20-4-2020 through 26-6-2020; retake week 28)
Participants:up till now 49 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
innovatie          Albert Salah
Àlmila Akdag
Dong Nguyen
lecture          Albert Salah
Àlmila Akdag
Dong Nguyen
Contents:Introduction to Social Computing
Introduction to data ethics
Data sources in social computing
Social Media and Social Network Analysis
More on SNA
Natural language processing for social data analysis
Text mining of social media
Reality mining
Cultural Analytics
Social Simulations
Urban computing
AI for Social Good
Literature:Readings on Social Computing:
  • 1.Wang, F.-Y., K. M. Carley, D. Zeng, and W. Mao (2007). Social computing: From social informatics to social intelligence. IEEE Intelligent Systems 22(2), 79–83.
  • 2.Watts, D. J. (2007). A twenty-first century science. Nature, 445(7127), 489.
  • 3.Mann, A. (2016). Core concept: Computational social science. Proceedings of the National Academy of Sciences, 113(3), 468-470.
Reading on Ethics:
Salganik, Bit by Bit, Chapter 6 (Until "What to read next")

Optional reading
Kitchin R. 2016 The ethics of smart cities and urban science. Phil. Trans. R. Soc. A 374: 20160115.

Discussion Paper (1) on Ethics: (trial run, questions will be graded, but grades will not be recorded)
Kramer AD, Guillory JE, Hancock JT (2014) Experimental evidence of massive-scale emotional contagion through social networks. Proc Natl Acad Sci USA 111(24):8788–8790 (plus the editorial comment)

Readings on Data Sources:
Salganik, Bit by Bit, Chapter 2 - Observing Behavior (Until "What to read next"

Discussion Paper (2) on Data Sources:
Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., & Giannotti, F. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1), 1-15.

Readings on Social Media and Social Networks:
  • 1.Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business horizons, 54(3), 241-251.
  • 2.Hawe, P., Webster, C., & Shiell, A. (2004). A glossary of terms for navigating the field of social network analysis. Journal of Epidemiology & Community Health, 58(12), 971-975.
  • 3.Easley and Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Chapter 2, pp.23-44.

Bonus reading from our guest lecturer:
Gregory, K., Groth, P., Scharnhorst, A., & Wyatt, S. Lost or Found? Discovering Data Needed for Research: Supplementary Materials. Harvard Data Science Review, 2020.

Discussion Paper (3) on Social Media: Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M., Halliwell, M. R., ... & Griffith, S. A. (2015). Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters, 39(1), 1-22.

Readings on Social Networks: 1.Easley and Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Chapter 3 (Sections 1-5), pp.47-69
2.Easley and Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Chapter 4, pp.85-116.
3. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440.

Optional reading: 1.Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3-5), 75-174.
2.Easley and Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Chapter 20.
Discussion Paper (4) on Social Network Analysis (SNA): Ferrara, Emilio. "A large-scale community structure analysis in Facebook." EPJ Data Science 1.1 (2012): 9.

Reading on NLP:
Nguyen, Dong, Maria Liakata, Simon DeDeo, Jacob Eisenstein, David Mimno, Rebekah Tromble, and Jane Winters. "How we do things with words: Analyzing text as social and cultural data." arXiv preprint arXiv:1907.01468 (2019).
Discussion Paper (5) on Natural Language Processing (NLP):
Voigt, Rob, et al. "Language from police body camera footage shows racial disparities in officer respect." Proceedings of the National Academy of Sciences 114.25 (2017): 6521-6526.* * includes appendix

Readings on Social Media:
Ruths, Derek, and Jürgen Pfeffer. "Social media for large studies of behavior." Science 346.6213 (2014): 1063-1064.
Chew, Cynthia, and Gunther Eysenbach. "Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak." PloS one 5.11 (2010).

Discussion Paper (6) on Social Media Analysis:
Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J., & Gilbert, E. (2017). You can't stay here: The efficacy of Reddit's 2015 ban examined through hate speech. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), 1-22.

Readings on Reality Mining:
Eagle, N., & Pentland, A. S. (2006). Reality mining: sensing complex social systems. Personal and ubiquitous computing, 10(4), 255-268.
De Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the crowd: The privacy bounds of human mobility. Scientific reports, 3, 1376.

Discussion Paper (7) on Reality Mining:
Sapiezynski, P., Stopczynski, A., Gatej, R., & Lehmann, S. (2015). Tracking human mobility using wifi signals. PloS one, 10(7), e0130824.

Reading on Surveys:
Salganik, Bit by Bit, Chapter 3, Asking Questions.
Schneider, D., & Harknett, K. (2019). What’s to Like? Facebook as a Tool for Survey Data Collection. Sociological Methods & Research, 0049124119882477.

Optional reading:
Lohr, S. L., & Raghunathan, T. E. (2017). Combining survey data with other data sources. Statistical Science, 32(2), 293–312.
Further optional paper for explaining the Copenhagen data:
Sapiezynski, P., Stopczynski, A., Lassen, D. D., & Lehmann, S. (2019). Interaction data from the Copenhagen Networks Study. Scientific Data, 6(1), 1-10.

Discussion Paper (8) on Surveys:
Awad, Edmond, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan. "The moral machine experiment." Nature 563, no. 7729 (2018): 59-64.

Readings on Cultural Analytics:
Manovich, L. (2015). The science of culture? Social computing, digital humanities and cultural analytics.
Manovich, L. (2015). Trending: the promises and the challenges of big social data. 2012. URL: http://www. manovich. net/DOCS/Manovich_trending_ paper.
Hochman, Nadav, and Lev Manovich. "Zooming into an Instagram City: Reading the local through social media." First Monday 18.7 (2013).

Discussion Paper (9) on Cultural Analytics:
Hochman, N., & Schwartz, R. (2012). Visualizing instagram: Tracing cultural visual rhythms. AAAI Workshop - Technical Report, WS-12-03, 6–9.

Reading on Social Simulations:
Conte, Rosaria, and Mario Paolucci. "On agent-based modeling and computational social science." Frontiers in Psychology 5 (2014): 668.

Discussion Paper (10) on Social Simulations:
Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling users' activity on twitter networks: Validation of Dunbar's number. PloS one, 6(8).

Readings on Visualization:
Heer, J., & boyd, D. (2005). Vizster: Visualizing online social networks. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005. (pp. 32-39). IEEE.
Sah, P., Méndez, J. D., & Bansal, S. (2019). A multi-species repository of social networks. Scientific Data, 6(1), 44.
Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–1148.

Optional reading on Gapminder:
Tonnessen, What is visual-numeric literacy, and how does it work?, in Martin Engebretsen and Helen Kennedy (eds) Data Visualization in Society
Discussion Paper (11) on Visualization:
Venturini, T., Jacomy, M., & Jensen, P. (2019). What do we See when We Look at Networks. An introduction to visual network analysis and force-directed layouts. (April 26, 2019).

Readings on Urban Computing:
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 1-55.

Optional reading:
Santani, Darshan, Salvador Ruiz-Correa, and Daniel Gatica-Perez. "Looking at cities in Mexico with crowds." Proceedings of the 2015 Annual Symposium on Computing for Development. 2015.

Discussion Paper (12) on Urban computing:
Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., & Pentland, A. (2014, November). Once upon a crime: towards crime prediction from demographics and mobile data. In Proceedings of the 16th international conference on multimodal interaction (pp. 427-434).

Reading on AI for Social Good:
Verhulst et al. (2019), Leveraging private data for social good.

Optional readings:
Ledford, H. (2015). How to solve the world's biggest problems. Nature News, 525(7569), 308.
Elmer, G. (2003). A Diagram of Panoptic Surveillance. New Media & Society, 5(2), 231–247.

Discussion Paper (13) on AI for Social Good:
Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. "Predicting poverty and wealth from mobile phone metadata." Science 350.6264 (2015): 1073-1076.
Course form:Lectures
Paper presentations by student groups
Discussion questions prepared by students for each lecture on one reading assignment
Term project
Exam form:Take Home
Minimum effort to qualify for 2nd chance exam:4/10