The Spread of Misinformation on Reddit

Observing how different forms of misinformation and conspiracies are spread through social media.

Overview

With the amount of actively spread misinformation circulating popular social media platforms, our goal is to explore if various forms of misinformation follow varying patterns of diffusion. While misinformation exists on many platforms, the scope of our project will be limited to two misinformation types -- myth and political misinformation, and focused on Reddit. Our data will be obtained from Reddit archive pushshift.io.

Contents

• src contains the source code of our project, including algorithms for data extraction, analysis, and modelling.
• notebooks contain some examples of the models this code will generate, detailing our findings under the circumstances in which we conducted our testing.
• config contains easily changable parameters to test the data under various circumstances or change directories as needed.
• run.py will build and run different the different parts of the source code, as needed by the user.
• references cite the sources we used to construct this project.
• requirements.txt lists the Python package dependencies of which the code relies on.

How to Run

• Install the dependencies by running pip install -r requirements.txt from the root directory of the project.
• Alternatively, you may reference our Docker image to recreate our environment, located here.
• Due to the open source nature of the PushShift archive, there is no need for any API use or developer account.

Building the project stages using run.py

• To download the data, run python run.py data
  • This downloads reddit comments from specified subreddits between a certain time period. The subreddits and time period are specified in config/data_params.json
• To create visualizations of EDA charts, run python run.py eda
  • This creates bar charts representing the dataset we have collected. It also shows visualizes statitics of one-time posters and average number of posts in each category and subreddit.
• To get user polarities, run python run.py user_polarity
  • This generates a metric for all users collected in the data, getting filepaths from config/user_polarity_params.json
• To generate common user matrices, run python run.py matrices
  • This creates two matrices demonstrating, for every possible pair of subreddits, the number and average user polarity of the users in that subset. Filepaths are specified in config/matrix_params.json.
  • NOTE: user_polarities should be run at least once before running matrices.
• To create visualizations of user polarities and matrices, run python run.py visualize
  • This creates bar charts representing the general user polarity spread, as well as charts showing how users of different types cross into other subreddits. These bar charts will be replaced by heatmaps in a future update for easier visualization. Filepaths are specified in config/visualize_params.json.
  • NOTE: user_polarities and matrices should be run at least once before running visualize.
• To run the full pipeline, run python run.py all
  • This will run all the targets. These targets include: data, eda, user_polarity, matrices, and visualize
• To run the full pipeline on test data, run python run.py test
  • This will run all the targets on test data. These targets include: data, eda, user_polarity, matrices, and visualize