Learning Outcomes
You will gain a deeper understanding of our clusters or network charts.
You will learn about the insights that can be obtained from clusters or network charts.
What is a Clusters or Network chart?
Clusters or Network charts in TRAC can transform any conversation or data point into a network of distinct segments, based on their unique characteristics and properties. This allows you to quickly and easily identify data points that are frequently associated or discussed together within a conversation. These data points can include groups of authors based on their bios, groups of keywords, entities, hashtags or topics, and groups of images based on their extracted concepts. By analysing the co-occurrence of these variables, these network charts can help you gain valuable insights to inform your creative and content planning. For instance, you can use Topic Clusters to plan your content around the specific topic areas identified, or tailor your content to reach specific groups of people based on their bios. If you want to learn more about the clustering algorithm we use to create the network charts, then check out the article linked below.
What Insights can you gain from a Clusters or Network chart?
Clusters or Network charts can be found within Audience Insights and Content Insights, under the following sections:
Content Insights > Images
and each cluster is telling a unique story about what people are discussing, the type of content and media being shared, and the groups of people involved in that conversation. Below is an overview of how Clusters work across different data types.
Audience Insights: Top Bio Keyword Clusters
You will find the Top Bio Clusters in the Audience Insights > Demographics section of your TRAC search. You can use this graph to identify the main groups of people involved in the conversation, based on how they describe themselves in their bios.
Content Insights: Hashtags, Keywords, Topics, Entity Clusters
When viewing Hashtags in a Clusters Network chart, you can quickly identify groups of hashtags that frequently appear or are discussed together within the same conversation. This allows you to understand how different hashtags relate to one another and which themes or narratives they collectively represent. Pulsar automatically groups related hashtags into distinct clusters and applies a clustering algorithm to determine the relevance and influence of each hashtag within its group. This helps surface the dominant themes present in your dataset. Each cluster is then summarised to provide a clear, at-a-glance explanation of what that cluster represents.
When viewing Keywords in a Clusters chart, you can easily identify groups of terms that frequently appear or are discussed together within the same conversation. This reveals how specific keywords relate to one another and highlights the concepts shaping your dataset. Pulsar automatically groups related keywords into distinct clusters and uses a clustering algorithm to determine the relevance and importance of each term within its group, helping you surface the dominant themes in the data. Each keyword cluster is then summarised to give you a clear, at-a-glance understanding of what that cluster represents.
When viewing Entities in a Clusters network chart, you can quickly identify groups of people or organisations that are frequently associated or discussed together within the same conversation. This helps you understand how a brand or client is connected to specific individuals, institutions, or competitors. Pulsar automatically groups related entities into distinct clusters and uses a clustering algorithm to determine the relevance and importance of each person or organisation within that group, allowing you to uncover the most influential players in the dataset. Each entity cluster is then summarised to provide a clear, at-a-glance explanation of what that cluster represents.
Lastly, when viewing Topics in a Clusters network chart, you can identify groups of topics that frequently appear or are discussed together within the same conversation. This helps you understand how different topics relate to one another and which themes are shaping the narrative. Pulsar automatically groups related topics into distinct clusters and applies a clustering algorithm to determine the relevance and importance of each topic within its group, revealing the dominant themes in your dataset. Each topic cluster is then summarised to provide a clear, at-a-glance explanation of what that cluster represents.
Content Insights: Image Captions
In the Content section, we surface the top image captions, segmented and clustered into a network. The chart displays related captions grouped together into distinct clusters. By using this chart, you can distinguish different images that are associated with your brand, campaign, or a specific subject. Our clustering algorithm analyses each image cluster and determines the relevance and importance of each image within it, helping you to uncover the dominant and most important visual themes present in a given dataset.
The Influencer Network
Another way TRAC clusters data is through the Influencers Network graph, which maps the people driving a conversation around a specific topic. This visualisation highlights the authors who are most influential, identified by the level of engagement they generate and the reactions they receive from other users.
The graph reveals how individuals interact, showing who influences whom, and whether the audiences responding to a particular author come from a single segment or span multiple clusters. You can also identify the most impactful voices in the conversation by examining how central they are within the network and the degree to which they connect with authors across different clusters.
To learn more about the Influencers Network, click the link below.
💡 Top Tip: With these insights, you can effectively shape your strategy and better target your audience by understanding the structure of conversation and the groups of people involved in the discussion.
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