(SEM VI) THEORY EXAMINATION 2024-25 SOCIAL MEDIA ANALYTICS AND DATA ANALYSIS

Social Media Analytics and Data Analysis (BCAM061)

Section-Wise Important Questions & Study Notes

SECTION A

(Attempt all questions | 2 × 7 = 14 marks)
Write short, crisp answers (2–3 lines each). Focus on definitions + core idea.

(a) Social Media Analytics (SMA)

Social Media Analytics is the process of collecting, analyzing, and interpreting data from social media platforms to understand user behavior, trends, and opinions.
Primary objective: support better business and marketing decisions.

(b) Adjacency Matrix

An adjacency matrix is a square matrix used to represent a graph, where rows and columns denote nodes and entries show whether an edge exists between nodes.

(c) Equivalence in Network Structures

Equivalence means nodes having similar roles or positions in a network.
Example: Two users with similar follower and interaction patterns are structurally equivalent, even if they are not directly connected.

(d) Structural Holes

Structural holes are gaps between groups in a social network.
Individuals bridging these gaps gain information advantage and control, improving connectivity and influence.

(e) Influence Maximization

Influence maximization is the process of identifying key influential nodes that can spread information to the largest audience.
It is crucial for viral marketing and brand campaigns.

(f) Small vs Large Organizations in SMA

(g) Distance in a Graph

Distance is the shortest number of edges between two nodes.
It helps measure closeness, reachability, and efficiency of information flow.

 SECTION B

(Attempt any THREE | 7 × 3 = 21 marks)
Answers should be descriptive, use headings and examples.

(a) Role of SMA in Small and Large Organizations

SMA helps organizations track customer sentiment, brand perception, and campaign performance.
Small firms focus on customer engagement, while large firms use SMA for market intelligence, forecasting, and competitive analysis.

(b) Random Graphs and Network Evolution

Random graphs are networks where edges form probabilistically.
Key models:

Erdős–Rényi model

Preferential attachment model
Networks evolve as new nodes and links are added over time.

(c) Citation Networks

Citation networks link research papers through citations.
They help analyze research impact, influential authors, and knowledge flow.
Such networks evolve by cumulative growth and show power-law degree distribution.

(d) Google Analytics for Website Performance

Google Analytics tracks traffic, user behavior, conversions, and sources.
Limitations include privacy concerns and cookie dependency.
Google Website Optimizer improves performance through A/B testing.

(e) Collective Classification

Collective classification classifies nodes using their attributes and neighbors’ labels.
Unlike traditional methods, it uses network structure and is useful in social networks and fraud detection.

 SECTION C

(Attempt any ONE | 7 marks)

(a) Types of Social Networks

User-generated content networks (YouTube, Twitter)

Affiliation networks (LinkedIn groups)           Interaction networks (Facebook friends)
These networks support marketing, influencer analysis, and customer segmentation.

OR

(b) Web Search Engines & Digital Advertising      Search engines index and rank web content.
Digital advertising uses SEO, PPC, and social ads, which integrate with social media to improve visibility and conversions.