Mining the Social Web

Spring 2015 – Mon 14:15-16:00 @ T5, Aalto CS

A large part of the Web, today, consists of online platforms that enable users to share information, access information shared by other users, and interact with each other. They include online social networks (e.g. Facebook, Google+), multimedia sharing websites (e.g. Youtube, Instagram, Flickr), and blogging platforms (e.g. WordPress, Tumblr, Twitter), to name a few examples. Such platforms are collectively referred to as ‘the Social Web’.

In this course, we discuss research work that aims to analyse data generated on Social Web platforms and provide insights that are relevant to a wide array of fields: from science (e.g., to social scientists), to civil engineering (e.g., to design better cities), or even commerce and politics (e.g., to market analysts and political campaign strategists who aim to understand their customer and electoral base, respectively).

Morevover, students who take this course for credit will be asked to propose, implement, and present in class a project to analyse social web data.

  • Credit Units:  5
  • Instructors: Aris Gionis and Michael MathioudakisEmail: first.last@aalto.fi
  • Office Hours:  By appointment or Mon 14:15-15:30 when no lecture.
  • Schedule
    • Jan 26th to Feb 9th: Lectures
    • Feb 23rd: Project proposals
    • Mar 30th: Progress report due
    • Mar 30th to Apr 6th: Intermediate presentations
    • May 4th to May 11th: Final presentations
    • May 15th: Final report due

Lecture Slides

  1. Introduction (Jan 25th)
  2. Structure & Dynamics of Social Networks (Feb 2nd)
  3. Politics, Financial Sentiment, Urban Computing (Feb 9th)

Presented Papers

  • Rattenbury, Tye, and Mor Naaman. “Methods for extracting place semantics from Flickr tags.” ACM Transactions on the Web (TWEB) 3.1 (2009): 1.
  • Leskovec, Jure, Kevin J. Lang, Anirban Dasgupta, and Michael W. Mahoney. “Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters.” Internet Mathematics 6, no. 1 (2009): 29-123.
  • Adamic, Lada A., and Natalie Glance. “The political blogosphere and the 2004 US election: divided they blog.” Proceedings of the 3rd international workshop on Link discovery. ACM, 2005.
  • Leskovec, Jure, Jon Kleinberg, and Christos Faloutsos. “Graph evolution: Densification and shrinking diameters.” ACM Transactions on Knowledge Discovery from Data (TKDD) 1, no. 1 (2007): 2.
  • Crandall, David, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, and Siddharth Suri. “Feedback effects between similarity and social influence in online communities.” In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 160-168. ACM, 2008.
  • Leskovec, Jure, Lars Backstrom, and Jon Kleinberg. “Meme-tracking and the dynamics of the news cycle.” Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009.
  • Bakshy, Eytan, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts. “Everyone’s an influencer: quantifying influence on twitter.” In Proceedings of the fourth ACM international conference on Web search and data mining, pp. 65-74. ACM, 2011.
  • Teng, Chun-Yuen, Liuling Gong, Avishay Livne Eecs, Celso Brunetti, and Lada Adamic. “Coevolution of network structure and content.” In Proceedings of the 4th Annual ACM Web Science Conference, pp. 288-297. ACM, 2012.
  • Conover, Michael, Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Filippo Menczer, and Alessandro Flammini. “Political polarization on twitter.” In ICWSM. 2011.
  • Cohen, Raviv, and Derek Ruths. “Classifying Political Orientation on Twitter: It’s Not Easy!.” In ICWSM. 2013.
  • Bollen, Johan, Huina Mao, and Xiaojun Zeng. “Twitter mood predicts the stock market.” Journal of Computational Science 2.1 (2011): 1-8.
  • Cranshaw, Justin, Raz Schwartz, Jason I. Hong, and Norman M. Sadeh. “The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City.” In ICWSM. 2012.

Links to Resources

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