Privacy & Security in Online Social Media (BSCS4003) in IITM BS: Syllabus

Privacy & Security in Online Social Media (BSCS4003) is a 4 credit level 4 elective in the IITM BS in Data Science. You study problems like spam, phishing, fraud, fake news and privacy leaks on social networks, and you learn to collect and analyse social media data to study them. The course page lists no prerequisite. Most weeks pair the topic with a data task or a research paper to read and report on.

The course at a glance

The instructor is Prof. Ponnurangam Kumaraguru ("PK") of IIIT Hyderabad. The course page lists the type as "Elective". The handbook table lists it as "L4_DEGREE", tagged "BD/BP", with 4 credits and no prerequisite. The two sources agree.

What you learn, week by week

The course page lists a topic for each of the 12 weeks, and a task for most of them.

• Weeks 1 to 3: the tools. The basics of social network analysis. Then collecting data from social media and analysing the text in it. The tasks ask you to collect data, analyse it and write a report.
• Weeks 4 and 5: cyber crime. How crime shows up on social media. One week you analyse data, the next you read a research paper and report on it.
• Weeks 6 and 7: fake news. The same pattern: a data analysis week, then a paper reading week.
• Weeks 8 and 9: privacy. Again, one week of data analysis and one week of reading a paper.
• Weeks 10 to 12: the bigger picture. Ethics and bias on social media, and computational social science, which uses social media data to study how people behave. These weeks are about reading papers and understanding ideas.

The course page also mentions an optional mini-project. In it, you design a project on one of the problems from the course, or on any social network problem you find.

How it is assessed

The course page lists weekly online assignments, 2 in-person invigilated quizzes and 1 in-person invigilated end term exam. The weekly tasks above (data reports and paper reports) are listed next to each week. The mini-project is marked optional.

Where it counts

It is a level 4 elective tagged BD/BP. The handbook does not say which stream a BD/BP course counts for at the BS degree level, so ask support before you use it for a stream requirement. It is not part of any minor.

The handbook table (updated 18 March 2026) marks it as offered in May 2026 only, and not in September 2026 or January 2027. If you want it, watch the course list each term.

Who finds it hard and how to prepare

The syllabus has little maths. The harder part is the steady weekly output: a data report or a paper report almost every week. If you have never read a research paper, it will be slow at first.

• Learn to read a paper quickly. Read the abstract, the figures and the conclusion first. Go into the method section only after you know what the paper found.
• Refresh your data skills. Getting data from the web, cleaning it and analysing it are covered in Tools in Data Science (BSSE2002). Revise that before week 2.
• Keep a report template. Use the same sections each week: question, data, method, findings, limits. It saves time.
• Respect privacy in your own work. When you collect social media data, follow the platform's rules and avoid sharing personal details in your reports. It is also what the course teaches.
• Start the mini-project early if you do it. It is optional, so decide by the middle of the term whether you have the time.

What to take before and after

Text analysis links well with Introduction to Natural Language Processing (BSDA5005). For presenting your findings clearly, look at Data Visualization Design (BSCS4001). The full list of options is in DS degree level electives.

Common questions

Is this a cyber security course?

Not in the usual sense of securing computers and networks. It looks at security and privacy problems on social networks, such as phishing, fraud nodes, identity theft and fake news, and studies them with data.

Is the mini-project compulsory?

No. The course page marks the mini-project as optional. If you take it up, you design a project around one problem from the course or one you find yourself.

Do I need to know machine learning for this course?

The course page lists no prerequisite. Social network analysis and text analysis are taught from the first weeks. The Python and data skills from your diploma are what you will use in the data tasks.

Can I take it in September 2026?

The March 2026 handbook table marks it as not offered in September 2026 or January 2027. Check the latest course list, since offerings can change.