Social Computing - CS60017

Fall Semester - 2016-17

Instructors

Pawan Goyal and Animesh Mukherjee

Course Timings

Lectures

Wednesday - 12:00 - 12:55 (CSE-107)

Thursday - 11:00 -11:55 (CSE-107)

Friday - 9:00 - 9:55 (CSE-107)

Teaching Assistants

Abhijnan Chakraborty, Sandipan Sikdar

Announcements

Final project presentations are scheduled on November 17th, 2016. Schedule has been sent by email.

Guest Lectures by Dr. Indrajit Bhattachrya from IBM Research, India are scheuled on September 23rd, from 9-11 AM and 6-7 PM in CSE-107.

Classes start from July 20th (Wednesday) in CSE - 107.

Lecture Slides

July 20, 2016 Introduction
July 21-22, 2016 Social Networks: Basic Structure and Measures
July 27, 2016 Reading: Crawling Social Networks as Facebook
July 28, 2016 Reading: Basic of Social Media Analytics
July 29, 2016 NLP for Social Media: Language Identification
August 3-4, 2016 NLP for Social Media: Language Identification, Text Normalization
August 5, 2016 NLP for Social Media:POS tagging, Sentiment analysis
August 17, 2016 Entity Linking
August, 2016 Entity Resolution, LSH, Reservoir Sampling
August, 2016 Graph Sampling, Population Sampling, LSH revised
September 7-9, 2016 Topic Models
October 5-6, 2016 Random Walks on Graphs
October 7, 2016 Supervised Random Walks
October 19-21, 2016 Recommendation Systems
October 26-28, 2016 Diffusion, Language Dynamics
November 2-4, 2016 Link Prediction, Voter Model
November 10, 2016 Hashtags on Twitter: Information Diffusion
November 11, 2016 Link Farming on Twitter

Class Test

Class Test, 2016 Problems

Last Year Exam Questions

Mid-Term, 2014 Problems
Mid-Term, 2015 Problems

Course Contents

The 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. Social Networks - Basic Structure and Measures
    3. Basics of Text Processing over Social Data
    4. Entity linking and entity resolution for Social data
  2. Studying Characteristics of OSNs
    1. Information Diffusion
    2. Experimental studies over OSNs,
    3. Sampling
  3. Fundamentals of Social Data Analytics
    1. Topic Models
    2. Random Walks
    3. Heterogeneous Information Networks
  4. Applied Social Data Analytics
    1. Recommendation Systems
    2. Community identification and link prediction
  5. Other Advanced Topics
    1. Online experiments for Computational Social Science
    2. Big Data Sampling

Text and Reference Literature

  1. Matthew A. Russell. Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More, 2nd Edition, O'Reilly Media, 2013.
  2. Jennifer Golbeck, Analyzing the social web, Morgan Kaufmann, 2013.
  3. Charu Aggarwal (ed.), Social Network Data Analytics, Springer, 2011.