The purpose of the project is to provide a quick overview about the activity and the engagement related to a twitter profile. The account analyzed is related to the italian politician Matteo Salvini because his profile is placed among the most active Twitter profiles in Italy.
Here's the pipeline
- Scraping: gather tweets with the library Twint. This library allows to collect almost the entire activity of a user without limitations
- Data Profiling: quick transformations (casting and field drop/rename) made with Pandas and PdPipe
- Data Visualization: made with Plotly and obtained the following plots:
- Tables exposing:
- All the collected tweets
- Top 5 retweeted posts
- Top 5 replied posts
- Top 5 liked posts
- Engagement overview over the time
- Number of tweets progression
- Number of likes progression
- Numbers of retweets progression
- Number of replies progression
- Waffle chart to show the overall activity on weekday and month
- Tables exposing:
- Data Consumption: static report made with Datapane
| Name | Link |
|---|---|
| Twint | https://github.com/twintproject/twint |
| pdPipe | https://pdpipe.github.io/pdpipe/ |
| pysqldf | https://pypi.org/project/pysqldf/ |
| Seaborn | https://seaborn.pydata.org/ |
| Ploltly | https://plotly.com/ |
| Datapane | https://datapane.com/ |
https://datapane.com/u/airaghidavide/reports/M7bQbw3/twitter-profile-report-matteo-salvini/
