Social Computing (CS60017)

Autumn semester 2018


  • Final evaluation of Term projects: A final report should be submitted by email to TA Surjya Ghosh by December 02, 2018. All requirements of the mentors should also be completed by this date. December 02, 2018 is a HARD deadline.
  • 04.10.2018: Extra class on Monday, October 8, 5.30pm - 7.30 pm -- guest lecture by Dr. Palash Dey, Department of CSE, IIT Kharagpur, on Computational Social Choice.
  • 26.09.2018: Guest Lectures by Dr. Malay Bhattacharyya, ISI Kolkata, in the scheduled classes on October 03 and October 04. Topics: (1) Crowdsourcing - Introduction, Crowd Computing, Creative Applications, (2) Crowdsourcing - Collective Intelligence, Judgment Analysis, Citizen Science.
  • 29.08.2018: Mid-term evaluation of term projects will be on October 01 (5.30 pm - 7.30 pm) and October 02 (11:00 am -- 1:00 pm). Each group must present their work for 20 minutes, including Q&A. Venue for evaluation: CSE 119. Schedule for presenting: pdf.
  • 13.08.2018: Term projects have been allocated: see allocation. All students should immediately contact the project mentor and start working on the projects.
  • 07.08.2018: Term projects declared (via mail to the mailing group below). Every student should bid for the projects by Friday, August 10.
  • All registered students should join the mailing group (you can use the link!forum/social-computing-2018)


Saptarshi Ghosh

Office: CSE 207

Email: saptarshi [at]

Course Timings

Wednesday 11:00 - 11:55

Thursday 12:00 - 12:55

Friday 08:00 - 08:55

Class venue: CSE 119

Teaching Assistants

  1. Surjya Ghosh (surjya.ghosh [at] gmail [dot] com)
  2. Abhisek Dash (assignmentad [at] gmail [dot] com)

Course evaluation

Mid-semester exam: 20%

End-semester exam: 40%

Term-project: 30%

Attendance and participation in shared tasks: 10%


The major components of the course include
  1. Social network analysis (network properties, network centrality, community structure)
  2. Social media text analysis
  3. Crowdsourcing
  4. Basics of Computational Social Choice
  5. Bias and fairness in social computing algorithms
  6. Various types of social media

Text and Reference Literature

  1. Networks, Crowds and Markets - Easley and Kleinberg
  2. Social Network Data Analytics - Charu Aggarwal (ed.) - Springer, 2011
  3. Mining of Massive Datasets - Jure Leskovec, Anand Rajaraman, Jeff Ullman
  4. Research papers to be pointed out in class
Topic Slides Notes (Relevant papers discussed in class, additional references, etc.)
Structural properties of large networks Slides 1. The Structure and Function of Complex Networks - Newman
2. Assortative Mixing in Networks - Newman
3. Measurement and Analysis of Online Social Networks - Mislove et al.
4. The Anatomy of the Facebook social graph - Ugander et al.
5. What is Twitter, a Social Network or a News Media? - Kwak et al.
Network centrality Slides 1. Authoritative Sources in a Hyperlinked Environment - Kleinberg
2. The PageRank Citation Ranking: Bringing Order to the Web - Page et al.
3. Topic-sensitive PageRank - Haveliwala
4. Combating Web Spam with TrustRank - Gyongyi et al.
5. Measuring User Influence in Twitter: The Million Follower Fallacy - Cha et al.
6. Understanding and combating link farming in the twitter social network - Ghosh et al.
7. Cognos: Crowdsourcing Search for Topic Experts in Microblogs - Ghosh et al.
Subgraphs and Community Structure Slides 1. Community structure in social and biological networks - Newman, Girvan, PNAS 2002
2. Empirical Comparison of Algorithms for Network Community Detection - Leskovec, WWW 2010
3. Fast algorithm for detecting community structure in networks - Newman, PRE 2004
4. Community detection in graphs - Fortunato, Physics Reports, 2010
5. Uncovering the overlapping community structure of complex networks in nature and society - Palla, Nature 2005
6. Link communities reveal multiscale complexity in networks - Ahn, Nature 2010
7. Deep Twitter Diving: Exploring Topical Groups in Microblogs at Scale - Bhattacharya, CSCW 2014
(Guest lectures by Malay Bhattacharyya)
Class 1
Class 2
Class 1: Crowdsourcing - Introduction, Crowd Computing, Creative Applications
Class 2: Collective Intelligence, Judgment Analysis, Citizen Science.
Relevant papers listed at the end of the slides.
Basics of Computational Social Choice
(Guest lecture by Palash Dey)
No slides Microeconomic Theory, Mas-Colell et al., Chapter 21 (Social Choice Theory)
pdf of book
Bias in social systems No slides 1. Bias on the Web, Communications of the ACM 2018
2. Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media, CSCW 2017
3. Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations, ICWSM 2017
Use of social computing on Ecommerce platforms Slides Relevant papers listed at the end of the slides.