If you're looking to gain insights into how the conversation in your search has evolved over time, then TRAC's Timeline page 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 your chosen period of time.

The Timeline page's time-based charts highlight a number of different insights into your coverage. Whether this is in the context of the volume or number of posts/engagements over time, or analysing changes in top data sources, sentiment, emotions or credibility over your chosen period of time.

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.

"Content Over Time" is an approach to analysing and interpreting conversations over a period. This method involves examining posts and interactions in online spaces, and tracking changes in engagement quantity and quality over time.

One useful feature of "Content Over Time" is the ability to visualise the data in a way that makes it easy to spot patterns and trends. For example, posts with high levels of engagement are colour-coded based on the channel where they were posted, such as X, Facebook, or Instagram, allowing researchers and marketers to quickly identify which platforms are generating the most content.

The second chart is "Top Data Sources Over Time". This visual is a representation of the changes in the data sources within the conversation. This chart displays where your conversation is coming from and includes different social platforms like X, Facebook, and Reddit.

Understanding when your audience is most engaged with your brand is crucial for creating effective social media campaigns. By knowing the peak activity times, you can schedule your posts to ensure maximum visibility and engagement.

For example, if your audience is most active during lunch breaks or in the evening, you can schedule your posts to go live at those times to increase the likelihood of them being seen and engaged with. Similarly, if your audience is most vocal during the weekends, you could plan your campaigns accordingly.

Third is "Sentiment Over Time". It is used to break down the volume of media coverage using sentiment analysis, and showing how sentiment volumes change over time. Sentiment analysis is the process of identifying and categorising emotions expressed in posts as positive, negative or neutral. The height of each area on the visual represents the proportion of posts that have been identified as conveying that particular sentiment score, with the total area representing the total number of posts analysed. By applying sentiment analysis to the posts, this visual displays the distribution of sentiment across your media coverage, providing insights into the overall sentiment of the conversation and how it has changed over time.

Fourth is "Emotions Over Time". It breaks down your posts based on Emotion analysis. This visualises how the conversation has evolved in relation to the five key emotions: anger, disgust, fear, joy and sadness over time. The height of each area on the visual represents the proportion of posts that have been identified as conveying that particular emotion, with the total area representing the total number of posts analysed. This allows you to easily see how the conversation's emotional tone changes over time.

This visualisation allows you to track the prevalence of misinformation over time, based on Credibility. Misinformation refers to false or misleading information that can spread quickly, and it can be difficult to differentiate between credible and non-credible sources. 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 provided by this visual, you can gain a better understanding of how misinformation spreads and take appropriate measures to combat it.