Social Computing - CS60017

Fall Semester - 2014-15


Pawan Goyal and Animesh Mukherjee

Course Timings


Monday - 11:30 -12:25 (CSE-302)

Teusday - 9:30 - 11:25 (CSE-302)

Reserved Slot: Thursday - 7:30 -8:25 (CSE-302)

Teaching Assistants

Tanmoy Chakraborty, Suman Kalyan Maity


The next series of lectures on Computational Advertisement will be held on November 14th (5:30 - 7:30 PM) and November 15th (9:30 - 11:30 AM) at CET.

Next Class of Social Computing will be held on October 7th, 9:30 AM.

Dr. Sayan Pathak from Microsoft Ad-Center will be giving lectures on "Computational Advertisement" on October 10th (5:30 - 7:30 PM) and October 11th (9:30 - 11:30 AM) at CET

Mid-Semester Exam on September 18th, 2:00 - 4:00 PM.

Mid-sem evaluation of the term project will be held on Sep 24th, Wednesday, during 15:00 - 18:00.

No Class on August 18th due to Institute Foundation Day.

No Class on July 29th due to Institute Holiday. Class on July 31st, Thursday, 7:30 - 8:25 AM.

Class on July 24th, Thursday, 7:30 - 8:25 AM.

Classes from July 21st.

Lecture Slides

July 21st, 2014 Introduction
July 22nd, 2014 Collecting Social Network Data
July 24-28th, 2014 Hashtags on Twitter
July 31st, 2014 Link Farming
August 4-12, 2014 Opinion Dynamics
August 19, 2014 Social Network: Basic Structure and Measures
September 2-4, 2014 Random Walks on Graphs
September 8, 2014 Supervised Random Walks
September 9, 2014 Reservoir Sampling, Entity Resolution
October 7, 2014 Locality Sensitive Hashing
October 10-11, 2014 Computational Advertisement - Part I
October 13-14, 2014 Topic Models
October 20, 2014 Relational Topic Models
October 21, 2014 Recommender Systems
October 27 - November 03, 2014 Link Prediction
November 14-15, 2014 Computational Advertisement - Part II
November 17, 2014 Recommender Systems - Part II, Reciprocity Prediction

Course Contents

The four major components of the course include
  1. Online Social Networks (OSNs)
    1. Introduction - Types of social networks (e.g., Twitter, Facebook), Measurement and Collection of Social Network Data
    2. Techniques to study different aspects of OSNs -- Follower-followee dynamics, link farming, spam detection, hashtag popularity and prediction, linguistic styles of tweets
    3. Human Centered Computing - Classes of human-centered computation, Methods of human-centered computation, Incentives for participation, computer supported co-opeartive work, computer supported collaborative learning
    4. Crowdsourcing as a Model for Problem Solving, ESP Game
  2. Models of Opinion Formation
    1. Opinion Dynamics - Continuous and Discrete Models
    2. Cultural, Language Dynamics - Axelrod Model and its variant, The Naming game, The Category Game
    3. Crowd Behavior- Flocking, Pedestrian behavior, Applause Dynamics and Mexican Wave
    4. Formation of Hierarchies - The Bonabeau Model, The advancement-decline Model
    5. Social spreading Phenomena- rumor spreading, gossip spreading
  3. Fundamentals of Social Data Analytics
    1. Introduction - Working with Social Media Data
    2. Topic Models
    3. Modeling social interactions on the Web
    4. Random Walks
    5. Variants of random walk
  4. Applied Social Data Analytics
    1. Application of Topic models
    2. Opinions and Sentiments - Mining, Analysis and Summarization
    3. Recommendation Systems
    4. Language dynamics and influence in online communities
    5. Community identification, link prediction and topical search in social networks
    6. Psychometric analysis

Text and Reference Literature

  1. Cioffi-Revilla, Claudio. Introduction to Computational Social Science, Springer, 2014.
  2. Matthew A. Russell. Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More, 2nd Edition, O'Reilly Media, 2013.
  3. Robert Hanneman and Mark Riddle. Introduction to social network methods. Online Text Book, 2005.
  4. Jennifer Golbeck, Analyzing the social web, Morgan Kaufmann, 2013.
  5. Claudio Castellano, Santo Fortunato, and Vittorio Loreto, Statistical physics of social dynamics, Rev. Mod. Phys. 81, 591, 11 May 2009.
  6. S. Fortunato and C. Castellano, Word of mouth and universal voting behaviour in proportional elections, Phys. Rev. Lett. 99, (2007).
  7. Douglas D. Heckathorn, The Dynamics and Dilemmas of Collective Action, American Sociological Review (1996).
  8. Michael W. Macy and Robert Willer, From factors to actors: Computational Sociology and Agent-Based Modeling, Annual Review of Sociology Vol. 28: 143-166 (2002).