Brand Analysis with NodeXL

In the digital age, where social networks form the backbone of communication and brand perception, understanding the complex interplay of interactions around a brand can be the key to unlocking its marketing potential. “Brand Analysis with NodeXL Pro” offers an insightful journey into the power of social network and content analysis through the lens of marketing, tailored specifically for marketers and social media managers seeking to refine their strategies and gain a competitive edge. 

By leveraging the sophisticated capabilities of NodeXL Pro, a leading software in network analysis, this webpage delves into a case study centered around the automobile brand Tesla on X platform. Through examining various NodeXL network data sets, we aim to showcase how in-depth analysis of social interactions and content dissemination can illuminate the strengths and opportunities within your brand’s digital footprint, enabling you to craft more effective, targeted marketing campaigns. Join us as we explore the intricacies of social network analysis and its profound impact on modern marketing practices.

Network science provides powerful methods for solving core problems in the social media marketing space. Visualizations and analysis of social media networks are useful methods for quickly identifying and evaluating influencers and contrasting the shape of the conversation around different topics and brands.

Until recently these methods have required advanced software development skills, but network analysis tools are evolving and now point-and-click solutions are emerging that can quickly identify sub-groups and market segments and describe the themes and high value resources in each. Key network concepts like “betweenness” and “centrality” can provide insights that go beyond the counts of things like likes, followers or replies.

A network perspective looks at social media as a “collection of connections” and reveals the emergent shape of the crowd. Research from Pew has shown that there are a small set of social media network structures that commonly appear in many forms of social media platforms that allow “reply”.

Viewing social media through a network lens transforms it into a vast web of interconnected relationships, spotlighting the intricate crowd dynamics at play. Pew Research highlights the existence of specific network structures across social media platforms, especially those facilitating user interactions like replies. Patterns such as divided, unified, fragmented, clustered, and hub-and-spoke configurations are prevalent, offering valuable insights for shaping social media strategies. By understanding the current conversational structure—and envisioning an ideal one—brands can develop new Key Performance Indicators (KPIs) to navigate the complex social media terrain.

Incorporating cluster analysis into this network perspective is crucial for delving deeper into the concept of homophily—the tendency of similar individuals to connect. This analysis unveils how communities or clusters form around shared interests or attributes, enabling brands to pinpoint where conversations coalesce around specific topics or sentiments.

For brands to leverage their social media presence effectively, it’s essential to:

  • Pinpoint key influencers and pivotal nodes within their social ecosystem.
  • Conduct thorough competitor and campaign intelligence to stay ahead.
  • Refine campaign strategies by cutting through the noise to identify and analyze the most engaging content or hashtags.
  • Enhance content strategy by harnessing insights on trending topics, ensuring the creation of resonant and relevant material for their audience.

By embracing a network perspective, enriched with cluster analysis, brands can navigate the complex social media landscape with greater precision, fostering more meaningful connections and engagements.

Depending on the search query design we will explore four distinct social media network maps that use different data perspectives to gain a variety of valuable insights:

  1. Ego Network: Created from tweets published by the brand account. Query: from:Tesla
  2. Brand Network: Based on all mentions of the brand in current tweets. Query: Tesla
  3. Community Network: Based on current tweets containing the brand account name. Query: @Tesla
  4. Discourse Network: Based on tweets around an important market segment. Query: (#ev OR (electronic vehicle)) (battery OR batteries) 

In the following, we will create NodeXL Pro INSIGHTS reports for each of these four network perspectives. So let`s get started!

How to analyze the data?

NodeXL Pro (the Network Overview Discovery and Exploration add-in for the familiar Microsoft Office Excel™ spreadsheet) from the Social Media Research Foundation can easily perform a full-scale social network and content analysis of social media data and visualize it in a network graph.

Such a network report is easily done with NodeXL Pro using the Task Automation feature. In addition we can export the reports to NodeXL Pro INSIGHTS which is based on a Microsoft Power BI report template that allows the creation of interactive network reports. A NodeXL Pro INSIGHTS report makes use of many different data visualizations – from simple tables to hashtag clouds, from image grids to scatter plots. Easily pivot around all social media dimensions: Tweets, users, groups, hashtags, media, sentiment, space and time.

How to get data?

There are several ways to import posts (tweets) from platform X (formerly known as Twitter). 

  1. Direct download of data sets into NodeXL via the into the integrated NodeXL Pro X (formerly Twitter) Search Network 3.0 data importer
  2. Easy file import of Excel files that contain X data provided by the commercial data providers BrandwatchMeltwater, Talkwalker or Tweet Binder

Step 1: Import a data recipe related to X from the official NodeXL Pro Options Files bundle available here.

Step 2: Open the NodeXL Pro X (formerly Twitter) Search Network 3.0 data importer, select NodeXL Pro > Data > Import > From NodeXL Pro X (formerly Twitter) Search Network 3.0 data importer

Step 3: Enter your search query, set the time frame and number of tweets and click OK. See image below.

Step 4: Wait for a short while. When Task automation is finished, you will receive a fully populated NodeXL Pro workbook and a network graph.

Step 5: Export your data to NodeXL Pro INSIGHTS.

Links to network reports

Here are the links to the NodeXL Pro network reports in NodeXL Graph Gallery and NodeXL Pro INSIGHTS that are presented below:

Report 1: Query: from:tesla (gallery / INSIGHTS)

Report 2: Query: @Tesla (gallery / INSIGHTS)

Report 3: Query: Tesla (gallery / INSIGHTS)

Report 4: Query: (#ev OR (electronic vehicle)) (battery OR batteries) (gallery / INSIGHTS)

Ego Network

About: Explore a network based on the material published by the official brand account by using the search operator “from:tesla” to reveal recent content and network strategies.

Network insights: Which are the most important user accounts mentioned in tweets, quotes and replies of the brand account? → Look at the data visual “Top Mentioned Users” and the tweet counts on all of the report pages of the INSIGHTS report below.

Content insights: Which are the most frequently used hashtags, URLs, words and word pairs? What is the timing pattern of the tweets? → Open the report page “Time” and select content on the visuals seen on the far right.

Community Network

About: Explore current tweets sent by and directed at the official X (formerly Twitter) brand account. The data may tell interesting stories around the brand, marketing campaigns, target groups or customer satisfaction.

Network insights: Which X (Twitter) users are influential when interacting with the brand account?

→ Look at the report page “Influencers” and user network metrics like Betweenness Centrality in the INSIGHTS report below.

Content insights: Which are the most frequently used hashtags, URLs, words and word pairs in the whole network and within the detected clusters?

 → The data visual “Groups” is available on all report pages. Select a group to slice and compare the data and explore the shared contents in the INSIGHTS report below.

Discourse Network

About: Investigate the wider conversations around specific topics, products, or campaigns linked to the brand. This analysis broadens the scope beyond direct interactions, capturing the overarching dialogue. Here we will look at discussions around electric vehicles and batteries with the search query: (#ev OR (electronic vehicle)) (battery OR batteries)

Network Insights: Uncover key influencers and the dynamics of their interactions within the discourse. Determine if discussions are centralized or spread across the community by

 → visually exploring the shape of the network graph and the shapes of the detected clusters

 → analyzing the clusters via the “Groups” data visual which is available on all report pages in the INSIGHTS report below

 → identifying influential users with the help of vertex network metrics such as Betweenness and Eigenvector Centrality

Content Insights: Identify dominant hashtags, URLs, words, and sentiments within the discourse. Analyze conversation peaks and engagement levels to understand what drives audience interest and reaction.

 → The report pages of the INSIGHTS report below contain the most frequently shared contents such as hashtags, URLs/domains, and words in tables usually on the right side of the report pages. In addition there are dedicated report pages around hashtags, words, sentiment, shared media, and shared URLs

Request your own free Sample Insights report


In this webinar, sociologist and founder of NodeXL, Dr. Marc Smith discusses how NodeXL simplifies social media data analysis by mapping relationships and identifying influential users through automated network metrics. 
Learn how NodeXL captures interactions such as replies and retweets, creating visual networks that highlight key individuals and discussion clusters. Register now and transform your approach to social network analysis. 👉


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