Data Science MSc Programme is responsible for the course.

The course is available to MSc students outside the CS department.

No rigid prerequisites. Basic understanding of statistics and algorithms is desirable.
MSc students outside the CS department, with an interest in the field, are welcome to join the course.

The student explores the literature about how Web users consume news and engage in political activity on the Web.

The student develops good written scientific communication skills:

• uses exact and understandable language according to the scientific convention and solid argumentation without unnecessary reliance on the source materials;
• produces independently a well-formed and finished written report that concentrates on the essential with proper emphasis;
• can use versatile search strategies and databases when acquiring information;
• evaluates published information and its significance in the field critically;
• becomes familiar with ethical and professional conduct within the scientific community.

The student develops good oral scientific communication skills:

• uses exact and understandable language in oral communication;
• prepares an oral presentation that fits into the seminar;
• uses appropriate visual and other aids to support the oral presentation;
• answers questions with solid argumentation;
• emphasizes the essential and uses expressive examples;
• participates actively in the discussion and with expertise during others' presentations.

• Can be attended any time during the degree.
• The course is offered during the Autumn term of 2018/19.
• The course is not offered every year - a different seminar might be offered in its place.
• The course covers the entire Autumn term of 2018/19. As a conference-style seminar, students will conduct literature review during the first period and presentations during the second period.

Students will read, review, and present papers that study online activity related to news consumption and dissemination, as well as political activity.

The main activity for the students is to produce a literature review, in which they highlight the data analysis tasks discussed in the literature and the algorithmic techniques used during the analysis.

The students will:

• conduct literature review and produce an 18-20 pages report, using the departmental thesis template, individually or in small teams;
• conduct peer-review of other students' report;
• present papers in class.

The teacher will support students with introductory lectures, office hours, and feedback (in- or outside-class)

At the beginning of the course, the instructor provides a list of works from the literature, to serve as starting points for the students' literature review. The list is by no means exhaustive – and is not meant to be: as part of the course, the students will use online publication databases to search for publications.

Indicative list below:

• Bandari, Roja, Sitaram Asur, and Bernardo A. Huberman. "The pulse of news in social media: Forecasting popularity." ICWSM 12 (2012): 26-33.
• Lee, Chei Sian, and Long Ma. "News sharing in social media: The effect of gratifications and prior experience." Computers in human behavior 28, no. 2 (2012): 331-339.
• Kwak, Haewoon, Changhyun Lee, Hosung Park, and Sue Moon. "What is Twitter, a social network or a news media?." In Proceedings of the 19th international conference on World wide web, pp. 591-600. ACM, 2010.
• Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. "Exposure to ideologically diverse news and opinion on Facebook." Science 348, no. 6239 (2015): 1130-1132.
• Garimella, Kiran, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. "Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship." In Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 913-922. International World Wide Web Conferences Steering Committee, 2018.
• Conover, Michael D., Bruno Gonçalves, Jacob Ratkiewicz, Alessandro Flammini, and Filippo Menczer. "Predicting the political alignment of twitter users." In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, pp. 192-199. IEEE, 2011.
• Cohen, Raviv, and Derek Ruths. "Classifying political orientation on Twitter: It's not easy!." In ICWSM. 2013.
• Gayo Avello, Daniel, Panagiotis T. Metaxas, and Eni Mustafaraj. "Limits of electoral predictions using twitter." In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence, 2011.
• DiGrazia, Joseph, Karissa McKelvey, Johan Bollen, and Fabio Rojas. "More tweets, more votes: Social media as a quantitative indicator of political behavior." PloS one 8, no. 11 (2013): e79449.
• Guerra, Pedro Henrique Calais, Wagner Meira Jr, Claire Cardie, and Robert Kleinberg. "A Measure of Polarization on Social Media Networks Based on Community Boundaries." In ICWSM. 2013.
• Garimella, Kiran, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. "Quantifying controversy on social media." ACM Transactions on Social Computing 1, no. 1 (2018): 3.
• Barberá, Pablo. "How social media reduces mass political polarization. Evidence from Germany, Spain, and the US." Job Market Paper, New York University 46 (2014).
• Matti Nelimarkka, Salla-Maaria Laaksonen, and Bryan Semaan. 2018. Social Media Is Polarized, Social Media Is Polarized: Towards a New Design Agenda for Mitigating Polarization. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS '18). ACM, New York, NY, USA, 957-970.
• Garimella, Kiran, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. "Reducing controversy by connecting opposing views." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 81-90. ACM, 2017.
• Jang, Myungha, John Foley, Shiri Dori-Hacohen, and James Allan. "Probabilistic approaches to controversy detection." In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2069-2072. ACM, 2016.
• Vosoughi, Soroush, Deb Roy, and Sinan Aral. "The spread of true and false news online." Science 359, no. 6380 (2018): 1146-1151.

Pass/fail.

To pass the course, the students should attend all sessions (allowed exception of one session) and produce a substantial outcome for each of the aforementioned activities.

DATA16001: Network analysis.

Contact teaching - the active participation of students in the class will count towards the grade.

Students are required to attend all lectures and in-class presentations. Exception can be made for only one session, if it is justified.

The students will:

• conduct literature review and produce an 18-20 pages report, using the departmental thesis template, individually or in small teams;
• conduct peer-review of other students' report;
• present papers in class.