If you're looking to gain insights into how the conversation in your search has evolved over time, then TRAC's Timeline subpage is the perfect tool 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 subpage'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, Media Reach, Impressions, Visibility and AVE over time.

The first chart you come across in the Timeline page is the "Content Over Time" graph. This graph refers to a method of analysing and interpreting conversations over a period of time. It involves examining posts and interactions in online spaces, and tracking changes in the quantity and quality of engagement 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.

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 Print, Broadcast, Online and 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 visual allows you to track the spread 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 labelled as credible, non-credible, satire, or user-generated content over a specific time period, you can gain valuable insights into the spread of misinformation.

The ability to monitor the spread of misinformation is crucial. With the rise of fake news and misinformation, it's essential to be able to distinguish between credible and non-credible sources. This graph provides a clear overview of the credibility labels assigned to posts over time, allowing you to identify any patterns or trends in the spread of misinformation.

For example, if you notice a sudden increase in the number of non-credible sources posting about 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.