B.Tech Artificial Intelligence & Machine Learning · Sem 6 (Departmental Elective-III) · B.Tech Artificial Intelligence & Data Science · Sem 6 (Departmental Elective-III) · B.Tech CSE (Artificial Intelligence) · Sem 6 (Departmental Elective-III) · B.Tech CSE (AI & ML) · Sem 6 (Departmental Elective-III) · B.Tech CSE (Data Science) · Sem 6 (Departmental Elective-III)
Introduction to Social Media Introduction to Social Media, Social Media Landscape, Social Media Analytics & its Need. SMA in Small and Large Organisations; Application of SMA in Different Social Media Platforms. Types of Social Networks, friend, user-generated content, affiliation, etc., sociograms, sociometric studies Basics of Social Media and Business Models, Basics of Web Search Engines and Digital Advertising, Application of SMA in different areas.
Graphs and Matrices The adjacency matrix, paths, connectivity, Distance and Breadth First Search, Network Datasets: An Overview Nodes, ties, and influencers, Making connections, Link analysis. Random graphs and network evolution. Weighted Networks, Hypergraphs
Network Fundamentals Network Structures, equivalence, homophile, clustering, snowball sampling, contact tracing and random walks, Ego-ceneterd networks, dominance hierarchies, Third Party Records, affiliation Network Citation Networks, Peer to Peer Networks, Recommender Networks, Biological Networks
Social Network and Modelling Social Contexts: Affiliation and Identity, social capital, structural holes, structural balance, Predictive Modeling, Descriptive Modeling, community/ anomaly detection Facebook Analytics: Introduction, parameters, demographics, Analyzing page audience. Reach and Engagement analysis Google Analytics: Brief Implementation Technology, Limitations, Performance Concerns, Privacy Issues. Introduction and Working, Google Website Optimizer
Processing, Visualizing and Social Media Data Analytics Processing and Visualizing Data, Influence Maximization,Link Prediction, Collective Classification, Applications in Advertising and Game Analytics, collecting and visualizing social media data, visualization and exploration. Social Network and Web Data Analytics Methods, Clickstream analysis, A/B testing, online surveys, Web crawling and Indexing. Natural Language ProcessingTechniques for Micro-text Analysis, Trend, social influencers on judgements, opinion spread, judgement
After completion of the course student will be able to:
Note: The AKTU syllabus PDF of some branches prints this subject with small differences in the course outcomes (AI & Data Science). Please also check the official AKTU PDF of your branch, linked at the end of your semester page.
This subject is on page 33, 34 of the original AKTU PDF.
