| --- |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - en |
| tags: |
| - social_media |
| - politics |
| pretty_name: Classifying Social Media Comments |
| size_categories: |
| - 10K<n<100K |
| --- |
| --- |
|
|
| # **Description** |
|
|
| This dataset was created in an attempt to understand the nature of social media commentary beyond the usual |
| 'positive', 'negative', 'neutral' labels. Below is a description of the sources, labels and methods used to |
| create the dataset |
|
|
| ## **Sources** |
|
|
| The social media comments available in this data have been pulled from the following sources |
|
|
| - You Tube |
| - Hacker News |
| - MetaFilter |
| - Reddit |
| - BlueSky |
|
|
| ## **Labels** |
|
|
| **Argumentative** |
| - Makes specific claims, predictions, or assertions supported by reasoning |
| - Uses evidence, anecdotes, or scenarios to build a case |
| - The key distinction from Opinion: there's an attempt to *persuade* or *explain why*, not just state a position |
|
|
| **Informational** |
| - Shares facts, data, links, or context relevant to the discussion |
| - Low emotional affect — the comment is trying to *inform*, not convince or react |
| - Includes answering another commenter's question with factual content |
| - The key distinction from Argumentative: presenting information without advocating for a position |
|
|
| **Opinion** |
| - States a value judgment, stance, or take without substantial reasoning |
| - "This is good/bad/wrong/overrated" — the comment *asserts* but doesn't *argue* |
| - The key distinction from Argumentative: no real attempt to persuade or support the claim |
| - The key distinction from Expressive: the comment is making a point, not just reacting |
|
|
| **Expressive** |
| - Emotional reactions, sarcasm, jokes, venting, exclamations |
| - The comment is primarily *expressing feeling* rather than making a point |
| - Includes performative agreement/disagreement ("THIS," "lol exactly," "what a joke") |
| - The key distinction from Opinion: no identifiable stance being taken, just affect |
|
|
| **Neutral** |
| - Clarifying or rhetorical questions, meta-commentary, off-topic remarks |
| - Comments that don't clearly fit the other four categories |
| - Includes simple factual questions directed at other commenters |
|
|
| ## **Methods** |
|
|
| **Collection** |
|
|
| The social media comments were pulled from posts in the above sources that fit the following criteria |
|
|
| - Search query was 'politics' or 'US Politics' |
| - Data range varied from 2024 to mid-Feb of 2026 depending on the nature of the site. For instance Reddit is |
| heavily trafficked and the daily rate limit was hit for posts pulled in just the first two weeks of Feb, while |
| Metafilter posts were pulled from as far back as 2024 |
| - Posts with less than 10 comments were ignored, and no more than 300 comments were pulled from any one post |
|
|
| **Labeling** |
|
|
| A sample of 100 comments were independently labeled by 2 of our group, then compared and revised. The rest were |
| sent via the Batch API to 3 language models: Gemini Flash 3, Chat GPT 5.1 and Calude Haiku 4.5. Included in the |
| prompt were 10 examples of correctly labeled samples and 10 examples of samples that had been incorrectly labeled |
| with the correct label provided. The comments that had an agreement of 2 or more models were kept with the |
| reamining comments set aside for evaluation |
|
|
| **Processing** |
|
|
| - Approximately 2-3k duplicate comments were removed |
| - NaN's were removed |
| - Emojis were converted into text using the `emoji` package |
| - Text was converted to lower case |
| - Remaining HTML artifacts were removed |
| - URL links were replaced with a '[URL]' tag |
| - Some comments contained escaped characters, these were converted back e.g. (&/quot; -> ") |
|
|
| ## **Dataset_info:** |
| |
| **Features:** |
| |
| - text -> string |
| - label |
| |
| **Splits:** |
| |
| - name: train |
| - num_bytes: 10.19 Mb |
| - num_examples: 49,268 |
| - name: test |
| - num_bytes: 2.19 Mb |
| - num_examples: 10,558 |
| - name: valid |
| - num_bytes: 2.19 Mb |
| - num_examples: 10,557 |
| - download_size: 9.16 Mb |
| - dataset_size: 14.57 Mb |
| |
| **Configs:** |
| |
| config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| - split: valid |
| path: data/valid-* |
| |
| **label2id:** |
| |
| Neutral: 0 |
| Opinion: 1 |
| Argumentative: 2 |
| Expressive: 3 |
| Informational: 4 |
| |
| **id2label:** |
| |
| 0: Neutral |
| 1: Opinion |
| 2: Argumentative |
| 3: Expressive |
| 4: Informational |
| |
| |
| --- |
