Implementation realized in the context of scholar project on network analysis. Database-specific implementation, meant for work on a public dataset published here by Kai Hirota, which contains pre-processed tweets collected between November 29th and December 25th, 2017 by Brett Turner (initial source here).
To run a basic results-production pipeline, simply download a copy of the
code and of the dataset, then edit the pipepline.py file to set up the
constants appropriately (lines 13 to 15) and run python3 pipepline.py.
This will build graphs set, output .gexf files of the final network states
for exploitation with Gephi, output visualizations of the network's evolution
(including a video file if the cv2 package is installed) and finally dump
to a .pickle file the graphs built and metrics computed to help characterize
them and their evolution.
The code is structured in classes that all aim at a specific part of the data processing, vizualisation and analysis pipepline:
-
Data processing classes:
MeTooDataExtractorextracts enriched data from the initial SQLite file to csv filesMeTooDataLoaderis a backend class for the former
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Network analysis classes:
MeTooGraphBuilderbuildsnetworkx.DiGraphinstances based on the extracted dataMeTooGraphDrawerdraws visual representations of the former graphsMeTooGraphAnalyzercomputes network-wide and user-wise statistics characterizing the former graphs
Copyright 2019 Paul Andrey
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.