TRAC: General Listening - Content Insights - Timeline

If you're looking to gain insights into how the conversation in your search has evolved over time, then TRAC's Timeline section is perfect, as we've done all the heavy lifting for you. With its collection of powerful analysis charts, you can visualise and gain deep insights into how the conversation has changed over a specific period of time.

The time-based charts in this view highlight a number of different insights into your coverage. Whether it's understanding the number of posts and engagements over time, or analysing changes in top data sources, sentiment, emotions or credibility over your chosen period of time, this section in TRAC is perfect to gauge how a topic has been performing over time, through various lens. 

To gain even more insights, you can analyse the data in each visual using additional metrics to understand changes in Volume, Visibility, Social Impressions, Media Reach, Media Impressions and AVE over time.

The Content Over Time chart is a great way to track changes in post and engagement activity over time. It helps you quickly identify spikes or dips within a given timeline and explore the underlying data driving those fluctuations. To make this even more insightful, we’ve overlaid a bubble chart on top of the timeline view. This combined visualisation allows you to see not only when activity changes occur, but also which data sources are contributing to them.

In the example below, it’s clear that the majority of posts and engagements, as well as the most significant spikes in volume, come from X data, represented by the black bubbles on the chart. If you’re familiar with TRAC’s colour-coding system, you’ll know that each data source is assigned its own colour, based on its brand identity guidelines, making it easy to distinguish between platforms at a glance.

The second chart in the Timeline section is the Top Data Sources Over Time chart. This visualisation helps you understand where your data is coming from, allowing you to quickly identify which channels are driving the conversation.

In the example below, it’s clear that the majority of conversations are taking place on X, with a noticeable spike in volume on October 14th. This suggests that something significant occurred around this date, leading to a surge in mentions, particularly from X, that’s worth investigating further. To explore this in more detail, simply click on the chart. You’ll be taken directly to the Results view, where you can dive deeper into the posts and discussions behind that spike for greater context.

Understanding when your audience is most engaged with your brand or a topic is essential for running effective social media campaigns. That’s where the Most Active Time and Day of the Week heat map comes in.

This chart helps you pinpoint the peak times when your audience is most vocal or engaged in conversation. By identifying these moments, you can strategically schedule posts or campaigns to go live when visibility and interaction are at their highest. For example, if your audience tends to be most active during lunch hours, evenings, or weekends, you can plan your content to publish during those windows, thus increasing the likelihood that it will be seen, shared, or engaged with.

The Sentiment Over Time chart provides a clear view of how Sentiment within a conversation evolves over time, breaking down posts by volume and sentiment category. In the area chart (as shown below), the width of each coloured area represents the proportion of posts that convey a specific sentiment; positive, neutral, or negative, while the total area reflects the overall number of posts analysed. By applying sentiment analysis to the timeline, you can easily track how sentiment shifts over time for any given topic. This helps you understand the sentiment trajectory of a conversation, revealing when discussions become more positive or negative, and allowing you to pinpoint the moments that drive those changes.

The "Emotions Over Time" chart breaks down your posts based on Emotion analysis. This visualises how the conversation has evolved in relation to five key emotions: anger, disgust, fear, joy and sadness over time.

In the area chart (as shown below), the width of each coloured area represents the proportion of posts that convey a specific emotion; anger, disgust, fear, joy and sadness, while the total area reflects the overall number of posts analysed. By applying emotion analysis to the timeline, you can easily track how emotions shifts over time for any given topic. This helps you understand the emotional trajectory of a conversation, revealing when discussions are leaning towards anger, frustration or joy, and allowing you to pinpoint the moments that drive those changes.

This visualisation allows you to track the prevalence of misinformation over time, based on the Credibility of the outlets or sites covering that topic. By examining the volume of posts coming from credible, non-credible, satirical, or user-generated sites over a specific time period, you can gain valuable insights into the scale of potentially misleading content. For example, if you notice a sudden increase in the number of non-credible media sources covering a particular topic, it may indicate that misinformation is spreading. By analysing the data behind this visual, you can gain a better understanding of where misinformation originates and take appropriate measures to combat it.