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:

Who actually influences discussions around a topic?
Which accounts amplify information?
Which communities are connected?
How does content spread?
Where are opportunities for partnerships and growth?

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:

X (Twitter) accounts
Facebook pages
Telegram channels
LinkedIn profiles
organisations
media outlets

Connections

Connections represent relationships between nodes.

Examples:

mentions
replies
reposts
shares
comments
hyperlinks
collaborations

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:

connectors
opinion leaders
network bridges
community influencers

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:

company X account
Facebook business page
Telegram channel
LinkedIn company profile

Questions:

Who regularly interacts with our content?
Which followers create additional reach?
Which topics generate discussions?
Are we connected to the right communities?

2. Analysing external channels

This approach studies public networks outside your organisation.

Examples:

competitors
industry leaders
journalists
investors
influencers
professional communities

Questions:

Who dominates conversations?
Which accounts influence a market?
Which communities are growing?
Where are partnership opportunities?

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:

mentions
replies
reposts
hashtags
conversation networks

Useful tools:

NodeXL
Gephi
X API-based tools
social listening platforms

X analysis can reveal:

influential accounts
discussion clusters
information pathways

Facebook

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

Possible sources:

public pages
public posts
public comments
business pages

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

Useful tools:

Meta Business Suite
social listening platforms
specialised research tools

Facebook is especially useful for understanding:

brand communities
customer discussions
public campaigns

Telegram has become increasingly important for professional and community communication.

Public Telegram channels can be analysed through:

subscriber growth
message activity
forwarding patterns
engagement levels

Useful tools:

Telegram API
Telethon
TGStat
Combot

Telegram analysis is particularly useful for:

fintech communities
technology groups
investment discussions
regional business networks

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:

impressions
engagement
profile visits
follower growth
mentions

Facebook Insights

Useful metrics:

reach
reactions
comments
shares
audience information

Telegram analytics

Useful metrics:

views
subscribers
forwards
engagement

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:

repost articles
comment on posts
mention the publication

These users may become:

contributors
partners
industry contacts

Hidden influencers

Some accounts have influence because they connect different groups.

For example:

A technology journalist may connect:

startups
investors
developers
business media

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:

dominant brands
influential journalists
investor networks
industry communities
emerging voices

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:

tweets
mentions
replies
user connections

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:

researchers
journalists
analysts
universities

Typical workflow:

Export network data.
Import into Gephi.
Select a visual layout.
Calculate network statistics.
Identify clusters.

The result may show:

separate communities
central accounts
information bridges

For example:

A business ecosystem may reveal clusters of:

investors
startups
regulators
media organisations

Step 8: Combining analysis with AI tools

Artificial intelligence is increasingly useful in social media research.

AI can help analyse:

large volumes of comments
sentiment patterns
recurring topics
emerging discussions

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:

private Facebook profiles cannot be analysed
private Telegram groups require permission
deleted posts may no longer exist
API restrictions limit data collection

Ethical analysis should focus on:

public information
legitimate research purposes
respecting platform policies

Practical example: analysing a business media channel

A business publication wants to improve its digital influence.

The process:

Month 1

Collect:

six months of X posts
mentions
reposts
replies

Month 2

Analyse:

strongest supporters
industry communities
competitor networks

Month 3

Create strategy:

engage important connectors
publish content for key communities
build relationships with influencers

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:

NodeXL — social network analysis platform
Gephi — open-source network visualisation software
X Analytics — platform analytics for X
Meta Business Suite — Facebook and Instagram analytics
Telegram API and analytics platforms — public Telegram research

Understanding influence, communities and information flows beyond followers and likes

What is social media network analysis?

Nodes

Connections

Why follower numbers are not enough

Two approaches to social media analysis

1. Analysing your own channels

2. Analysing external channels

Step 1: Define your research objective

Step 2: Choose the platform

X (Twitter)

Facebook

Telegram

Step 3: Collect your data

Native analytics

X Analytics

Facebook Insights

Telegram analytics

Step 4: Analyse your own network

Strong supporters

Hidden influencers

Content pathways

Step 5: Analyse competitors and external communities

Step 6: Using NodeXL for network analysis

1. Install NodeXL

2. Import data

3. Generate the network

4. Analyse network metrics

Degree centrality

Betweenness centrality

Eigenvector centrality

Step 7: Visualising networks with Gephi

Step 8: Combining analysis with AI tools

Step 9: Understand limitations

Practical example: analysing a business media channel

Month 1

Month 2

Month 3

The future of social media analysis

Further Reading & Official Resources

Related Posts

Mezzan Holding transforms its financial planning with successful Corporate Planning integration

Companies are overlooking the potential of the African cloud ecosystem, says DAI Magister

Five ways AI is creating everyday risks for African businesses