Social Computing CS60017

 Pre-requisites for the course

•  Data structures and algorithms

•  Probability and Statistics

•  Basics of Machine Learning

•  Basics of Natural Language Processing

•  Basics of Graph algorithms

•  Programming in Python (there will be programming-based assignments)

  Broad topics 

• Introduction to social computing

• Structural properties of large social networks

• Useful users/content on social media -- network centrality, topical experts, 

• Harmful users/content on social media -- hate speech, fake news, spammers, etc.

• Recommendation systems

•  Different types of social platforms -- E-commerce sites, Medical / health-related platforms

•  Bias and fairness in social computing systems

• Use of Large Language Models in Social Computing

  Plagiarism policy 

• Plagiarism in any form - copying from other students or from online resources - will be severely penalized. Every assignment should be done individually or by designated groups.

• While you can discuss the concepts and assignments with other students, you should NOT share your code/answers for any assignment with any other student, until the grading of the assignment is completed. It is your responsibility to ensure that your codes/answers are not available to others.

• We will use standard plagiarism detection software to check the similarity of submitted assignments. If we find submissions that are too similar (beyond what can be expected by chance, or due to discussion among students), all such submissions will be severely penalized. We will NOT attempt to differentiate between who gave the codes and who copied; all involved students will be penalized equally. The minimum penalty for plagiarism in an assignment is a zero on that assignment. There can be more severe penalties for repeat offences.