---
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  

    
---