Reddit Political Affiliation

Political Classification

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Behavioral Classifier

Analogous to training a word2vec model with separate user and subreddit embeddings which learn parameters to maximize if the user participates in the subreddit. We extend this approach to use semi-supervised training in a multi-task setup: the traditional user2community model is retained and a separate linear layer is used to predict political affiliation from the user embedding if that user’s affiliation is known. This semi-supervised setup provides structure to the user embeddings, ideally infusing all users with information on their affiliation based on subreddit commenting behavior. Unlike the text-based classifier, the behavioral model captures user engagement in politically-affiliated communities, even if the user never explicitly declares their affiliation in comments. The primary difference between the behavioral and text classifiers is that the behavioral classifier captures whether a user associates with groups (subreddits) that are politically affiliated (e.g., gun-rights or pro-life for conservative users), whereas the text classifier captures whether a user says something that reveals their politics.

Code

• Dataset: src/data/word2vec contains the code to build the bipartite network between users and subreddits
• Training src/models/word2vec/train_model.py

Text Classifier

Some topics are politically oriented and can potentially reveal a user’s leaning, e.g., discussing interests in gun rights. To infer affiliation from such statements, we train a RoBERTa model over comments made from each user, excluding any statements they make that explicitly self-identify their affiliation. The model predicts each comment, and we aggregate the model outputs by taking the mean of predictions for selected comments associated with a user as the final label

Code src/models/textclassifier

Data data/all_interaction_comment_ids.tsv

Username Classifier

Usernames can reveal aspects of identity e.g., Hillary4Prez reveals a liberal leaning. To predict affiliation from names, we follow Wang and Jurgens (2018) and train a bidirectional character-based LSTM

Code src/models/usernameclassifier

Data Flairs used to build the dataset src/features/political_affiliations/political_labels.py

Interactions

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Here we examine the interactions between political users to probe the mechanisms behind toxicity of comments.

Code src/features/interactions/Interaction Regression.ipynb contains codes to do regression under R environment, src/features/interactions/offbert.py contains codes to train a bert to predict toxicity of comments.

Data data/all_interaction_comment_ids.tsv

Behavioral Analyses

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Participation in Reddit

Here, we test whether the different groups within an affiliation have separate bubbles themselves.

Code src/features/political_affiliations/ have a notebook Categorizing Political Users.ipynb which builds users' commenting frequencies across subreddits and applies PCA to identify latent variations in where users are active. The results are shown by t-sne projection which can be done by Rendering of t-sne Results.ipynb

Two-Faced Actors

We identified a small percent of political users who declare different political affiliations within a short period.

Code src/features/bad_actors contains codes to identify two-faced actors and two demo notebooks to do basic analysis and log-odds analysis.

Predicting Flips

Here, we test whether political affiliation changes can be predicted from prior behavior.

Code src/models/psm contains codes to predict affiliation changes using logistic regression.