Historical Content
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The "Historical Content" feature is designed to create an authentic simulation environment by pre-populating channels with realistic content that Players will see upon logging in. This feature allows Designers to specify dates and times for past articles and posts, adding depth and context to the scenario. Additionally, Designers can enhance engagement realism by simulating user interactions, such as comments, likes, and shares, using the 'Apply Influence' function.
Designers have the option to create past articles manually or by utilising an AI Assistant.
Use this feature to simulate historical engagement and interactions to set the stage in both social media and website channels, creating a lifelike scenario.
Ideal for scenarios where a credible, pre-populated media environment enhances training and simulation effectiveness, such as in social media management, public relations, or crisis response scenarios.
There are two methods for setting up historical content:
adding individually to each persona
performing a batch import through Excel import.
Content can be added:
Manually - click to add new content
with AI Assistant:
Use the AI Assistant to generate content by entering a topic. Ensure your input is detailed for optimal results.
The AI Assistant will create 3 articles based on the provided topic and will use the persona's faction "voice" to maintain consistency in the narrative.
CSV Upload: If you have prepared content, upload it using a .csv file to quickly populate the persona with historical content. Export first to get the correct format for the CSV file
Designers can set the date and time of historical content in three ways:
Publication (Real Time): The content is timestamped according to the current real-world date and time.
Specific Date and Time: Manually enter a specific date and time.
Relative Date and Time: Set content to appear a certain number of hours or weeks before the exercise upload date for dynamic scenarios.
When you click "apply influence" the past posts will be automatically given engagement within the boundaries set by the Influence values in "Other attributes". To get meaningful values of likes, shares and comments, you'll need to set up these values
Low means the lowest number of comments/likes/shares on any post and High is the highest number.
These values should be in keeping with the persona's influence. For example, taking three types of persona on Twitter:
Member of Public: A typical user with <1,000 followers, not widely known.
Opinion Shaper: A niche expert, journalist, or analyst with ~10k–100k followers.
Big Influencer: Celebrity or major creator with >500k followers, often verified.
Member of Public
1–10
0–3
0–2
Opinion Shaper
100–1,000
50–300
20–200
Big Influencer
5,000–100,000+
1,000–20,000+
500–10,000+
For websites, the CSV format has been created to be compatible with the MEL but the crucial columns are:
From - this is the persona name (it must match a persona)
Subject
Message
Timestamp
Questions, buttonText etc aren't used for historic content
For social media the columns are different and more appropriate to social media
Here are two example files:
