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Social Media Network Analysis: How Businesses Can Analyse Their Own Channels and External Communities

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Understanding influence, communities and information flows beyond followers and likes

Social media analytics traditionally focus on visible numbers: followers, likes, impressions and engagement rates. These metrics remain important, but they do not always explain how influence is created.

A company may have thousands of followers but limited impact, while a smaller account connected to the right communities can generate significant visibility.

This is where social media network analysis becomes valuable.

Network analysis examines relationships between accounts, communities and conversations to understand how information moves through digital ecosystems.

For businesses, media organisations, researchers and entrepreneurs, it provides answers to important questions:

Instead of measuring only audience size, network analysis examines the structure behind online conversations.

What is social media network analysis?

Social media network analysis (SNA) is a method used to study relationships between users, organisations and digital communities.

Every social network can be viewed as a system of connected elements.

Nodes

Nodes represent participants in a network.

Examples:

Connections

Connections represent relationships between nodes.

Examples:

By analysing these connections, researchers can identify influential accounts, communities and communication patterns.

Why follower numbers are not enough

A common mistake in social media analysis is assuming that the largest audience creates the greatest influence.

However, influence often depends on position within the network.

For example:

An industry expert with 8,000 followers may regularly communicate with investors, journalists and executives.

Another account with 100,000 followers may have limited interaction with relevant communities.

Network analysis helps identify accounts that connect different groups and accelerate information flow.

These users are often called:

Two approaches to social media analysis

Businesses usually need two types of analysis.

1. Analysing your own channels

This approach focuses on understanding your existing audience and relationships.

Examples:

Questions:

2. Analysing external channels

This approach studies public networks outside your organisation.

Examples:

Questions:

Step 1: Define your research objective

Before collecting data, define what you want to discover.

A weak research question:

“Analyse our social media.”

A stronger approach:

“Which accounts amplify our business content?”

“Who influences conversations about African fintech?”

“How does information spread among investors and entrepreneurs?”

The objective determines what data should be collected.

Step 2: Choose the platform

Different platforms provide different opportunities.

X (Twitter)

X remains one of the most suitable platforms for network analysis because many interactions are public.

Possible analysis:

Useful tools:

X analysis can reveal:

Facebook

Facebook analysis requires more attention because much of the platform is private.

Possible sources:

Private profiles and private groups cannot normally be analysed without permission.

Useful tools:

Facebook is especially useful for understanding:

Telegram

Telegram has become increasingly important for professional and community communication.

Public Telegram channels can be analysed through:

Useful tools:

Telegram analysis is particularly useful for:

Step 3: Collect your data

The first source should usually be the platform itself.

Native analytics

Most platforms provide basic information.

Examples:

X Analytics

Useful metrics:

Facebook Insights

Useful metrics:

Telegram analytics

Useful metrics:

These tools explain content performance.

Network analysis explains relationships.

Step 4: Analyse your own network

Imagine a business publication analysing its X account.

The objective:

Understand who helps distribute its content.

The analysis may identify:

Strong supporters

Accounts that frequently:

These users may become:

Hidden influencers

Some accounts have influence because they connect different groups.

For example:

A technology journalist may connect:

This position can be more valuable than a large but isolated audience.

Content pathways

Network analysis can show how information moves.

Example:

Company publishes research →

Industry expert shares →

Professional community discusses →

Media accounts notice →

New audiences discover the company.

Understanding this path helps organisations create better distribution strategies.

Step 5: Analyse competitors and external communities

External analysis helps businesses understand the wider ecosystem.

Example:

A company operating in African fintech analyses conversations around competitors.

The research may reveal:

This creates a map of the market.

Step 6: Using NodeXL for network analysis

NodeXL is one of the most accessible tools for beginners.

It works with Microsoft Excel and allows users to create social network maps.

Basic workflow:

1. Install NodeXL

Install the NodeXL extension.

2. Import data

Depending on the platform, collect:

3. Generate the network

NodeXL creates a visual representation of relationships.

4. Analyse network metrics

Important measurements include:

Degree centrality

Shows how many connections an account has.

A high degree score means an account interacts with many others.

Betweenness centrality

Shows which accounts connect different communities.

These users often act as bridges.

Example:

A journalist connecting business leaders and technology communities.

Eigenvector centrality

Measures influence based on connections with other influential accounts.

An account connected to important people may have greater importance than its follower count suggests.

Step 7: Visualising networks with Gephi

Gephi is an open-source platform for network visualisation.

It is commonly used by:

Typical workflow:

  1. Export network data.
  2. Import into Gephi.
  3. Select a visual layout.
  4. Calculate network statistics.
  5. Identify clusters.

The result may show:

For example:

A business ecosystem may reveal clusters of:

Step 8: Combining analysis with AI tools

Artificial intelligence is increasingly useful in social media research.

AI can help analyse:

However, AI does not replace network analysis.

AI explains:

“What are people saying?”

Network analysis explains:

“Who is connected to whom, and how does information spread?”

Together they provide deeper insight.

Step 9: Understand limitations

Social media research has important limitations.

Not all information is publicly available.

Examples:

Ethical analysis should focus on:

Practical example: analysing a business media channel

A business publication wants to improve its digital influence.

The process:

Month 1

Collect:

Month 2

Analyse:

Month 3

Create strategy:

The result is not just more followers.

The goal is a stronger position inside the relevant network.

The future of social media analysis

Digital influence is becoming less dependent on simple audience size.

Search engines, AI systems and online communities increasingly rely on signals of expertise, relevance and relationships.

For businesses, understanding networks will become as important as tracking traffic and conversions.

The key question is changing.

From:

“How many followers do we have?”

To:

“How does our network create influence?”

Social media network analysis provides a practical way to answer that question.

Tools mentioned in this guide:

Further Reading & Official Resources

Readers who want to explore social media analytics and network analysis in more depth can use the following official resources:

Author note:
This article is intended as a practical introduction. Advanced research projects may require specialised datasets, APIs, statistical methods and ethical review procedures.

Inspired by research and educational resources from the Social Media Research Foundation and NodeXL community.

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