Step-by-step workflow guide for investigators
Posting Pattern Analysis
Video Overview
Step-by-Step Guide
Step 1: Read the Hourly and Day-of-Week Pattern
The hourly distribution is the most informative single view in Evidence, for a simple reason: human beings sleep and software does not. If you see engagement data or posts form the same account 24/7, you are likely dealing with a bot account.
Step 2: Map the Interaction Structure in Analytics
Because the collection holds the profile behind every comment and reaction, analytics can show who responds to the target. The question is who turns up on most of the posts.
Step 3: Identify Where the Engagement is Coming From
Some platforms rank content partly on interaction, which creates an incentive to manufacture it with the use of bots or by commenting under one's own posts.
An author commenting repeatedly on their own posts pushes that content into recommendation feeds. Once it surfaces there it reaches people who do not follow the account at all. The reach becomes real even though the engagement that triggered it was not — and that distinction is the finding worth writing down.
How to do it:
An author commenting repeatedly on their own posts pushes that content into recommendation feeds. Once it surfaces there it reaches people who do not follow the account at all. The reach becomes real even though the engagement that triggered it was not — and that distinction is the finding worth writing down.
How to do it:
- From the Data section, open the comments on the target's own posts and check who authored them.
- Compare comment volume against the substance of the post — content carrying no text at all but heavy engagement is the pattern to notice.
- Check whether the posts with the most self-authored comments are the ones that travelled furthest.
What's next?
Other guides you might find useful:
Give us your Feedback!
