Classifying Tweets from Russian Troll Accounts

My project involves building a machine learning model to detect tweets from Russian troll twitter accounts.

If you want to explore some of my tweet-level predictions, check out this tool. You can also use this web app to make predictions on new tweets.

Research Process

1. Collect data

For this project, I construct a dataset of about 340,000 labeled tweets, denoting whether they were sent from a Russian troll account or not. The data on Russian troll tweets was compiled by Linvill and Warren of Clemson University, and open-sourced through FiveThirtyEight. In order to construct a control group, I employed the twint package to collect a sample of tweets from verified users, tweeting within the same time period as the Russian troll accounts. This collection can be replicated using this code:

$ bash collect_verified_tweets.sh

2. Process text data and feature engineering

I perform extensive preprocessing of the raw text from tweets (including tokenization, stop word removal, and lemmatization), as well as feature engineering. You can find these functions in src:

Data processing: src/d01_data/data_processing.py
Text processing: src/d02_features/text_preprocessing.py

3. Build models and tune hyperparameters

I test out several different classifiers, and settle on random forest as the best estimator. The model evaluation metric I'm trying to optimize for is recall (which will minimize false negatives). Here's a summary comparing the performance of various baseline models I tested:

You can take a look at my entire workflow in a single notebook, which lives here.

4. Results

After hyperparameter tuning through grid search, my random forest classifier achieves a recall score of 93.3%.

You can explore tweet predictions using this tool.

You can try making predictions on new tweets using this web app. (The source code for this web app is available here.)

A slide deck summarizing my project can be found here.