Introduction to Social Media Analytics

Birla Institute Of Technology And Science–Pilani (BITS–Pilani) via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

1 day 11 hours 38 minutes
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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This comprehensive course explores the intersection of social media platforms and network science, providing students with essential skills for analysing digital social interactions. Beginning with graph theory fundamentals, students learn to model social media data as networks and apply mathematical frameworks to extract meaningful insights. The curriculum progresses through advanced network analysis, centrality measures, and community detection algorithms. Students master key concepts, including degree centrality, betweenness analysis, PageRank algorithms, and information diffusion models. Practical applications focus on influencer identification, recommendation systems, viral marketing strategies, and community leader detection. Advanced modules cover machine learning techniques for social media, including language analysis, fake news detection, and behavioural prediction. Students explore ethical considerations in social media research, privacy preservation, and responsible AI applications. The course emphasises hands-on implementation using NetworkX, real-world case studies, and industry-relevant projects. By completion, students will be equipped to analyse social media networks professionally, develop recommendation algorithms, design viral marketing campaigns, and conduct ethical social media research. This course is ideal for data scientists, marketing professionals, researchers, and anyone seeking to understand the mathematical foundations of social media analytics.

Syllabus

Course Introduction

In this module, the learners will be introduced to the course and its syllabus, setting the foundation for their learning journey. The course's introductory video will provide them with insights into the valuable skills and knowledge they can expect to gain throughout the duration of this course. Additionally, the syllabus reading will comprehensively outline essential course components, including course values, assessment criteria, grading system, schedule, details of live sessions, and a recommended reading list that will enhance the learner’s understanding of the course concepts. Moreover, this module offers the learners the opportunity to connect with fellow learners as they participate in a discussion prompt designed to facilitate introductions and exchanges within the course community.

Introduction to Social Media and Graph Fundamentals

This foundational module introduces students to the intersection of social media platforms and network science. You will explore how social media ecosystems function as complex networks and master fundamental graph theory concepts essential for social media analytics. Key concepts include social media platform typologies, graph structures (nodes, edges, directed/undirected networks), representation methods (adjacency matrices, lists), and ethical data collection practices. Through hands-on demonstrations with NetworkX, you will build practical skills in modelling social media interactions as graphs. This module establishes the theoretical and practical foundation necessary for advanced network analysis in subsequent modules.

Network Analysis and Graph Properties

This module explores advanced graph types, including bipartite, weighted, temporal, and scale-free networks common in social media platforms. Students implement fundamental graph algorithms like DFS, BFS, and Dijkstra's algorithm for network exploration and shortest path analysis. The module covers network connectivity, components, and global properties such as density and efficiency. Students learn to analyse network structures and understand algorithmic complexity considerations for large-scale social media networks. Practical demonstrations guide students through implementing graph algorithms and analysing real social media network properties using computational tools.

Network Measures and Centrality Analysis

This module focuses on measuring node importance and identifying influential users in social networks. Students master fundamental centrality measures including degree, betweenness, closeness, and PageRank algorithms to analyse user roles and network positions. The module covers local node properties, structural patterns like transitivity and homophily, and link prediction techniques. Students learn to profile users based on multiple network measures and understand social network formation principles. Hands-on demonstrations teach students to compute centrality measures and build comprehensive user analysis systems for social media applications.

Community Detection and Analysis

This module examines methods for identifying and analysing groups within social networks. Students explore community detection approaches, including modularity-based methods, the Louvain algorithm, and spectral clustering techniques. The module covers overlapping communities, dynamic community evolution, and quality evaluation metrics. Students learn to compare different detection algorithms and understand their strengths and limitations. Applications in targeted marketing, content recommendation, and information flow analysis are emphasised. Practical demonstrations guide students through the implementation of community detection algorithms and the analysis of community structure in real social media networks.

Information Diffusion and Behaviour Analytics

This module studies how information and behaviours spread through social media networks. Students explore diffusion models, including independent cascade and linear threshold mechanisms, along with influence maximisation techniques. The module covers collective behaviours such as herd mentality, echo chambers, and social contagion phenomena. Students learn to detect information cascades, distinguish influence from homophily, and predict viral content. Applications in crisis detection, marketing campaigns, and behaviour prediction are emphasised. Comprehensive demonstrations teach students to simulate diffusion models and analyse real-world information spread patterns.

Recommender Systems

This module describes the design of recommender systems in the modelling of social media. The application domains for the recommendation models and systems are summarised. The Internet-scale algorithms using rule-based and parameter-based techniques are given. Further optimisation based on recent advancements in deep learning is also discussed. The various data analytics tasks in the recommendation problems are given based on the previously studied data mining models, such as clustering, frequent pattern mining, and association rule mining.

Big Data Analytics

This module provides the characterisation of big data generated on social media platforms. It provides an introduction to the adaptations of the data analytics tasks for processing big data. Complex graph analysis is explained in terms of dynamic networks formed on social media datasets. The corresponding mathematical properties to be satisfied by the complex datasets, such as non-stationarity and causality, are then incorporated into the data analytics algorithms. The resultant downstream applications are discussed with reference to recent developments in Agentic AI. Emerging technologies based on AI robustness and fairness are also introduced with reference to misinformation, disinformation, and the weaponisation of social media in multi-stage cyber attack campaigns.

Intelligent Systems

This module introduces robust and privacy-aware algorithm design for social media systems operating under adversarial conditions. It covers polarization mitigation through network interventions and adversarial perturbations, misinformation detection, encryption and anonymization techniques, reinforcement and bandit learning for adaptive recommendation, and hybrid deep learning models. The module also integrates MLOps practices for deploying, monitoring, and maintaining responsible ML-driven platforms.

Language Analysis

This module examines advanced content-based and personalised recommendation frameworks in social media ecosystems. It introduces language modelling, topic modelling, and novelty-detection filters for analysing multilingual and multimedia content. Knowledge representation through semantic web architectures, ontology engineering, and knowledge graphs is integrated with web data mining techniques. The module further explores transformer architectures, foundation models, multimodal learning systems, and adversarial deep learning in black-box environments. Privacy-preserving analytics and adversarial attacks on data privacy models are discussed within the broader context of responsible and scalable social media intelligence systems.

Social Media Detection Methods

This module examines algorithmic approaches to detecting and countering malicious content and adversarial behaviour in digital ecosystems. It covers fake news characterization, misinformation propagation patterns, feature engineering, and graph-based detection models. Image forensics and deepfake generation and detection are analysed using adversarial learning, geometric features, and decision boundary sensitivity analysis. The module further explores cyberbullying detection, phishing and URL analysis, information warfare strategies, and adversarial deep learning in attack–defense scenarios. Applications include LLMs, foundation models, wargaming simulations, multi-agent systems, and game-theoretic frameworks for AI-driven cybersecurity and strategic decision-making environments.