DOI: 10.5281/zenodo.17167317
title: Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts

description: 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.