ch6 (1)

Creating Value With Social Media Network Analytics

  • Chapter 6 by Dr. Gohar F. Khan

  • Copyright 2024 Gohar F. Khan

Learning Outcomes

  • Gain knowledge and skills in:

    • Network Analytics Theories, Concepts, and Tools: Understanding how these can be applied effectively.

    • Types of Networks and Terminologies: Familiarity with varied network structures.

    • Business Intelligence Applications: Recognizing how social media analytics can support business strategies.

    • Conventional Social Media Network Analysis: Techniques for extraction, construction, and analysis of networks.

    • Network Strategies: Developing methods for effective networking.

Introduction

  • Social media platforms create networks as users interact with each other and content

  • These networks arise from components such as friendships, follows, content interactions, reviews, and professional connections, revealing valuable business insights.

What is Social Media Network Analytics?

  • Definition: Involves constructing, analyzing, and comprehending social media networks.

  • Purpose: Provides organizations with tools to decode networks' anatomy—size, structure, and key influencers.

Core Objectives of Network Analysis

  • Overall Structure: Understanding the composition of networks, such as nodes and links.

  • Influential Nodes: Identify and rank node significance based on connectivity.

  • Pertinent Links: Assess relationships between nodes.

  • Cohesive Subgroups: Discover tight-knit communities within larger networks.

  • Multiplexity: Examine the interactions across different types of links.

Social Network Terminology

  • Networks: Groups of interconnected nodes.

  • Nodes: Represent entities such as individuals, organizations, or websites.

  • Links: Illustrate the relationships among nodes, such as friendships or trade connections.

Types of Networks

  • Social Networks:

    • Real World Example: Classmate network.

    • Online Example: Twitter following network.

  • Social Network Sites: Software designed for creating and maintaining social relations; examples include Facebook and LinkedIn.

  • Social Networking: The act of forming and maintaining social connections.

  • Social Network Analysis: The scientific study of social networks, stemming from multiple fields like sociology and graph theory.

Network Structures

  • Types of Structures:

    • Random Networks: Characterized by uniform node connectivity without dominant structures.

    • Scale-free Networks: Features power-law degree distributions where a few nodes have many connections (hubs).

    • Small World Networks: Consist of a few dominant nodes and many nodes with low connectivity, bridging gaps in networks.

Degree Distribution

  • Definition: Probability distribution indicating the degree of connectivity of nodes in a network.

  • Importance: Impacts information flow, node influence, and communication efficiency.

Random Networks

  • Characteristics: Homogeneous degree distribution, low clustering coefficient, and short path lengths.

  • Study Rationale: Benefits include benchmarking, theoretical insights, and understanding properties.

Scale-Free Networks

  • Definition: Few central nodes dominate while many nodes possess fewer connections.

  • Key Properties: Heterogeneous connectivity, robustness, and fragile structure under influence.

  • Real-world Examples: Banking networks, Facebook, citation networks.

Small World Networks

  • Characteristics: High clustering and short average path lengths; a few hubs dominate connectivity.

  • Theory: Inspired by the 6-degree separation hypothesis, which states individuals are connected by a few links, enhanced through social media.

Social Media Network Topologies

  • Different Forms of Network Structures: Emerge from user engagement on social media.

    • Types Include: Polarized Crowd, Tight Crowd, Brand Clusters, Broadcast Networks, Support Networks.

Common Social Media Network Types

  • Co-authororship, Co-commenter, Geo Co-existence, Hyperlink, Friendship, Professional, and Dating Networks among others.

Network Types Based on Direction, Weight, and Mode

  • Directed vs. Undirected: Placement of links determines if relationships have directionality.

  • Weighted vs. Unweighted: Weights on links indicate strength of connections.

  • One-Mode vs. Two-Mode Networks: Defined by the number of distinct node classes represented.

Node-Level Properties

  • Degree Centrality: Measures connectivity based on direct links to other nodes.

  • Betweenness Centrality: Identifies nodes that serve as vital bridges in the network.

  • Eigenvector Centrality: Evaluates a node's influence based on the importance of its connections.

Network-Level Properties

  • Clustering Coefficient: Indicates the tendency for nodes to form tightly connected groups.

  • Density: Reflects the proportion of actual connections to possible connections, indicating network cohesion.

  • Components: Subsets of nodes that are interconnected yet isolated from others.

  • Diameter: Measures the longest shortest path between nodes, suggesting network spread.

Social Media Network Strategies

  • Strategy 1: Focus on bridge building for enhanced connectivity.

  • Strategy 2: Utilizing the shortest path to gain prominence within networks.

  • Strategy 3: Crafting effective and engaging tweets.

Network Analytics Tools

  • NodeXL: Visualization and analysis tool for social networks.

  • UCINET: Robust application for network analysis with visualization capabilities.

  • Pajek: Software for analyzing large networks.

  • Netminer: Tool for social network analysis and visualization, particularly for education.

Review Questions

  • Examples include defining types of networks and differentiating between network properties.

  • Exercises: Focus on understanding network fundamentals and conducting network analysis.