Social Computing (CS60017) Autumn 2021

[IMPORTANT] Announcements

Please check the instructions for paper presentations very carefully.

The schedule for paper presentations is out. Please check your slot.

Check the course schedule page regularly for lecture schedule, test announcement and slides.

All course related information dissemination and assignment submission will happen via MS Teams and CSE Moodle. Please do join them (instructions below).

Every registered student should create an account on the Moodle system of CSE department. This system will be used for submission and grading of assignments. If you do not have an account already on the CSE department Moodle, create a new account for yourself following the given procedure. Login to the system, and follow the link "Autumn Semester (2021-22)". Choose the course "CS60017_2021-22 Social Computing". Join this course as "Student"; use Student Enrollment Key: CSTU60017

First class on Wednesday, August 11, at 11 am. Join the class Social Computing 2021 (CS60017) on MS Teams (IITKGP domain) using the Team Code eq3bb0z

The course requires a good knowledge of algorithms and data structures, graph algorithms, probability and statistics, Natural Language Processing and Machine Learning (see Prerequisites below). This is a research-oriented course that would require students to understand several CS research papers. Programming assignments need to be done using Python/C++. It is advisable to take this course only if you have the necessary background.

Instructor

Mainack Mondal
mainack @ cse.iitkgp.ac.in

TAs

Paramita Das
paramitad . iitkgp @ gmail.com

Jagdish Singh Samant
surajsamant1414 @ gmail . com

Rima Hazra
itsrima . iitkgp @ gmail . com

Ankan Mullick
ankanm @ kgpian . iitkgp . ac . in

Sayantan Adak
sayantanadak . skni @ gmail . com

Course Information

Credit (L-T-P)	3-0-0
Pre-requisites for the course	Data structures and algorithms Graph algorithms Probability and Statistics Basics of Machine Learning Basics of Natural Language Processing Programming in Python/C++ (there will be programming-based assignments) The course will involve understanding of several research papers, and students will have to present at least one research paper in online class. Here are some sample papers that you should be able to understand and present in the course: paper on community detection \| paper on sentiment analysis \| paper on hate speech detection
Lectures	Scheduled lecture timings are: Wednesday 11:00 am - 11:55 am Thursday 12:00 noon - 12:55 pm Friday 8:00 am - 8:55 am In this semester we will conduct the course online with a mix of live lectures, pre-recorded course videos and online doubt clearing sessions. Please keep an eye on the Schedule page for the latest updates. *Warning:* Recordings may not be available for guest lectures, or even for regular classes in case of technical snag, or if I forget to record some session. Availability of recordings is not a valid reason for not attending regular classes. In case you miss a class and its recording is not available for any reason, I would not be able to do anything about this.
Text and Reference Literature	Networks, Crowds and Markets - Easley and Kleinberg Social Network Data Analytics - Charu Aggarwal (ed.) - Springer, 2011 Mining of Massive Datasets - Jure Leskovec, Anand Rajaraman, Jeff Ullman Research papers to be pointed out in class
Topics	The broad topics to be covered in the course include: Social network analysis -- introduction, applications and challenges Structural properties of large networks (social networks, random networks, technological networks, etc.) Network centrality; identifying popular users/experts Community structure in social networks Social media text analysis -- introduction, applications and challenges Harmful users/content on social media -- hate speech, fake news, spammers in social networks, etc. Bias in social computing systems Advertising: the economic engine of social media Different types of social platforms -- Anonymous social networks, E-commerce sites Social media in times of COVID
Communication	We will update the course schedule regularly throughout the course. Live lectures / recordings Note that you NEED TO join the Microsoft teams classroom titled "Social Computing 2021 (CS60017)" for this course. We will also share all course related details of the lectures via Microsoft Teams. Drop the instructors an email ASAP if you cannot access the Microsoft teams classroom. Live lectures will be delivered via Zoom. We will use with the "live lectures" channel on Microsoft teams for live lecture related announcements (e.g., the zoom id/password). Please check that channel regularly. General discussion We'll use Microsoft Teams for general discussion and questions about course material. You should already have the account username and password to log into Microsoft teams. If you cannot access the Microsoft teams classroom titled "Social Computing 2021 (CS60017)" please let the instructors know as soon as possible. If you need to reach out to the instructors (e.g., pertaining to an illness or other events that might be impacting your performance in class), please send a private chat on Microsoft Teams visible only to the instructors. Please use the Microsoft teams chatroom (and channels) to discuss publicly with your peers in real-time. Please try to keep all course-related communication to Microsoft Teams rather than email.
Late policy	You need to strictly adhere to the deadlines for the submissions (e.g., reports, test scripts etc.) announced for this course in MS teams, or by design Moodle will not accept it. Of course, in exceptional circumstances related to personal emergencies, serious illness, wellness concerns, family emergencies, and similar, please make the course staff aware of your situation beforehand/as soon as possible and we will decide how to handle your case.

Course evaluation components [tentative]

Your course grade will be calculated as follows:

Online tests/quizzes	40%
Take-home programming assignments (3-4 in number)	40%
Presenting research paper(s):	20%

Honor code

You are permitted to talk to the course staff and to your fellow students about any of the problem sets. Any assistance, though, must be limited to discussion of the problem and sketching general approaches to a solution. Each student must write out his or her own solutions to the problem sets. Consulting another student's solution is prohibited, and submitted solutions may not be copied from any source. These and any other form of collaboration on assignments constitute cheating.

No collaboration is permitted on quizzes or assignments. All work submitted for the project must properly cite ideas and work that are not those of the students in the group. Simply stated, feel free to discuss problems with each other, but do not cheat. It is not worth it, and you will get caught. In that case, we will be forced to award you no marks for that assignment/quiz/project, take away 50% of your total final marks and you will risk deregistration.

Wellness

If a personal emergency comes up that might impact your work in the class, please let the instructors know via a private chat message (to all the course instructors) so that the course staff can make appropriate arrangements. We are going through unprecedented times and circumstances can sometimes be very overwhelming, and all of us benefit from support during times of struggle. You are not alone.