Currently I scheduled a task to extract twitter data of a week (every sunday) to predict the stock market for the following days. The number of likes of a tweet is not static and changes over time. Tweets posted and extracted on weekends are less likely to reach their "true" number of likes and other characteristics.
Calculating an expected value for the number of likes based on early periods after a tweet got published seems unreasonable as this would heavily depend on the user and other factors I can't control.
What a ways to work with this kind of data so that a tweet that really got almost no likes on Monday (had 5 days time to get likes) and a tweet that is going to go wild do not look the same for my algorithm?