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Home / Syllabus / B.E Artificial Intelligence and Machine Learning / Fourth Year AI ML / Semester 8 / Social Media Analytics

B.E Artificial Intelligence and Machine Learning Social Media Analytics Syllabus - Mumbai University

This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Social-Media-Analytics.pdf
Semester 8 · Fourth Year AI-ML

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Syllabus for Social Media Analytics

Semester 8 · Fourth Year AI-ML

Module 1: Social Media Analytics: An Overview

  • Core Characteristics of Social Media, Types of Social Media, Social media landscape, Need for Social Media Analytics (SMA), SMA in small & large organizations. Purpose of Social Media Analytics, Social Media vs. Traditional Business Analytics, Seven Layers of Social Media Analytics, Types of Social Media Analytics, Social Media Analytics Cycle, Challenges to Social Media Analytics, Social Media Analytics Tools

Module 2: Social Network Structure, Measures & Visualization

  • Basics of Social Network Structure - Nodes, Edges & Tie Describing the Networks Measures - Degree Distribution, Density, Connectivity, Centralization, Tie Strength & Trust Network Visualization - Graph Layout, Visualizing Network features, Scale Issues. Social Media Network Analytics - Common Network Terms, Common Social Media Network Types, Types of Networks, Common Network Terminologies, Network Analytics Tools.

Module 3: Social Media Text, Action & Hyperlink Analytics

  • Social Media Text Analytics - Types of Social Media Text, Purpose of Text Analytics, Steps in Text Analytics, Social Media Text Analysis Tools Social Media Action Analytics - What Is Actions Analytics? Common Social Media Actions, Actions Analytics Tools Social Media Hyperlink Analytics - Types of Hyperlinks, Types of Hyperlink Analytics, Hyperlink Analytics Tools

Module 4: Social Media Location & Search Engine Analytics

  • Location Analytics - Sources of Location Data, Categories of Location Analytics, Location Analytics and Privacy Concerns, Location Analytics Tools Search Engine Analytics - Types of Search Engines, Search Engine Analytics, Search Engine Analytics Tools

Module 5: Social Information Filtering

  • Social Information Filtering - Social Sharing and filtering , Automated Recommendation systems, Traditional Vs social Recommendation Systems Understanding Social Media and Business Alignment, Social Media KPI, Formulating a Social Media Strategy, Managing Social Media Risks

Module 6: Social Media Analytics Applications and Privacy

  • Social media in public sector - Analyzing public sector social media, analyzing individual users, case study. Business use of Social Media - Measuring success, Interaction and monitoring, case study. Privacy - Privacy policies, data ownership and maintaining privacy online.

Textbooks

  • 1 Seven Layers of Social Media Analytics_ Mining Business Insights from Social Media Text, Actions, Networks, Hyperlinks, Apps, Search Engine, and Location Data, Gohar F. Khan,(ISBN-10: 1507823207).
  • 2 Analyzing the Social Web 1st Edition by Jennifer Golbeck
  • 3 Mining the Social Web_ Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, Matthew A Russell, O‘Reilly
  • 4 Charu Aggarwal (ed.), Social Network Data Analytics, Springer, 2011
  • 1 Social Media Analytics [2015], Techniques and Insights for Extracting Business Value Out of Social Media, Matthew Ganis, AvinashKohirkar, IBM Press
  • 2 Social Media Analytics Strategy_ Using Data to Optimize Business Performance, Alex Gonçalves, APress Business Team
  • 3 Social Media Data Mining and Analytics, Szabo, G., G. Polatkan, O. Boykin & A. Chalkiopoulus (2019), Wiley, ISBN 978-1-118-82485-6
  • 1 https://cse.iitkgp.ac.in/~pawang/courses/SC16.html
  • 2 https://onlinecourses.nptel.ac.in/noc20_cs78/preview
  • 3 https://nptel.ac.in/courses/106106146
  • 4 https://7layersanalytics.com/

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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