JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives The objective of the course is to study the techniques to model, analyze, and

understand large-scale social media along with dynamic processes over social and information networks, and understand the link between qualitative and quantitative methods of social media mining.

Course Outcomes:

• 1. Understand the social media mining and its challenges
• 2. Analyze how to detect communities and influencers
• 3. Understand Community analysis
• 4. Measuring Assortative, Influence, Homophily, Recommendation in Social Media

UNIT - I

Introduction: What is Social Media Mining, New Challenges for social media Mining Graph Essentials: Graph Basics, Graph Representation, Types of Graphs, Connectivity in Graphs, Special Graphs, Graph Algorithms.

UNIT - II

Network Measures: Centrality, Transitivity and Reciprocity, Balance and Status, Similarity. Network Models: Properties of Real-World Networks, Random Graphs, Small-World Model, Preferential Attachment Model.

UNIT - III

Data Mining Essentials: Data, Data Preprocessing, Data Mining Algorithms, Supervised Learning, Unsupervised Learning Community Analysis: Community Detection, Community Evaluation, Community Evaluation

UNIT - IV

Information Diffusion in Social Media: Herd Behavior, Information Cascades, Diffusion of innovations, Epidemics Influence and Homophily: Measuring Assortativity, Influence, Homophily, Distinguishing Influence and Homophily

UNIT - V

Recommendation in Social Media: Challenges, Classical Recommendation Algorithms, Recommendation Using Social Context, Evaluating Recommendations Behavior Analytics: Individual Behavior, Collective Behavior.

TEXT BOOKS:

• 1. Social Media Mining (An Introduction), Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, Cambridge University Press, Draft Version: April 20, 2014

REFERENCES:

• 1. Mining the Social Web, 2nd Edition Data Mining Face book, Twitter, LinkedIn, Google+, GitHub, and More, Matthew A. Russell Publisher: O'Reilly Media.
• 2. Social Media Mining with R [Kindle Edition] Nathan Danneman Richard Heimann