Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts

Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts Readme 20-September-2025 Contact: oseckin[at]iu.edu This repository contains the datasets required to reproduce the results presented in the paper "Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts." submissions.parquet: PII redacted Reddit submissions included in the study. comments.parquet: PII redacted Reddit submissions included in the study. t_manual_annotation.parquet: Manually annotated Reddit posts for toxicity. c_manual_annotation.parquet: Manually annotated Reddit posts for controversiality. Dataset Content for submissions.parquet Reddit Metadata created_utc: Unix timestamp indicating when the Reddit submission was created. id: Unique identifier for the Reddit submission. subreddit: The name of the subreddit where the submission was posted. score: Reddit's upvote/downvote score for the submission. train_valid_test: Dataset split indicator showing whether the submission is used for training, validation, or testing. Toxicity and Controversy Scores submission_openai: OpenAI's toxicity score for the submission text (0-1 scale). mean_openai: Mean toxicity score across all comments in the submission. toxic_ratio: Proportion of comments in the submission that are considered toxic (score ≥ 0.5). ta_score: Model-predicted toxicity score generated by the fine-tuned DistilBERT model. c_score: Model-predicted controversy score generated by the fine-tuned DistilBERT model. Topic Analysis bertopic: Topic assignment from BERTopic clustering analysis of submission texts. Linguistic Features is_question: Binary indicator (0/1) for whether the submission contains a question mark. question_ratio: Proportion of sentences in the submission that are questions. gratitude_ratio: Proportion of sentences in the submission that express gratitude. gratitude: Binary indicator (0/1) for whether the submission contains gratitude expressions. gratitude_count: Count of gratitude expressions found in the submission. pos_tag_counts: Dictionary containing counts of each part-of-speech tag in the submission. proper_noun_count: Count of proper nouns (NNP, NNPS) found in the submission. proper_noun_ratio: Proportion of words in the submission that are proper nouns. text_length: Total number of words in the submission. lexical_item_count: Count of unique lexical items (nouns, verbs, adjectives, adverbs) in the submission. mtld: Measure of Textual Lexical Diversity (MTLD) score indicating vocabulary richness. hedge: Binary indicator (0/1) for whether the submission contains hedging language. hedge_count: Count of hedging expressions found in the submission. hedge_ratio: Proportion of sentences in the submission that contain hedging language. polarity: Sentiment polarity score of the submission text (-1 to 1 scale). positive_polarity: Binary indicator (0/1) for whether the submission has positive sentiment. negative_polarity: Binary indicator (0/1) for whether the submission has negative sentiment.