This project is based on Facebook/Meta available personal data for Messenger conversations. Here I collected, clean-up and plotted some statistics for a group chat with me and some friends. He had more than 400,000 messages over more than 5 years.

This simple analysis focus on the time statistics for each participant: how much message each participant sends for a given period of the day, which days of the week they are more active and how that changed with years.

I also considered the 'react' dynamics (emojis that one can react to messagens among ourselves): who reacted more, who gets more reacts and how the participants reacted among themselves.

Python

os, pandas, numpy, matplotlib, json and datetime

Facebook/Meta conversation as JSON files downloaded from here

Can be found in the folder results in this reposity. Plots generated using the notebook also in this repository.

1. Fix the emoji encoding to plot the results using matplotlib

1. We are more active after 10am with message peaking around 8-9pm

2. Our messages are consistent for all days of week with more chat during 2020 (pandemic)

3. The overall reaction probability is around 4-12%

4. The most used emoji for react, by far, is 😆