initial draft

README.md

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+---
+task_categories:
+- text-classification
+tags:
+- reddit
+language: en
+---
+
+# Dataset Card for reddit_one_ups_2014
+
+## Dataset Description
+
+- **Homepage:** https://github.com/Georeactor/reddit-one-ups
+
+### Dataset Summary
+
+Reddit 'one-ups' or 'clapbacks' - replies which scored higher than the original comments. This task makes one-ups easier by focusing on a set of common, often meme-like replies (e.g. 'yes', 'nope', '(͡°͜ʖ͡°)').
+
+For commentary on predictions with a previous version of the dataset, see https://blog.goodaudience.com/can-deepclapback-learn-when-to-lol-e4a2092a8f2c
+
+For unique / non-meme seq2seq version of this dataset, see https://huggingface.co/datasets/georeactor/reddit_one_ups_seq2seq_2014
+
+Replies were selected from PushShift's archive of posts from 2014.
+
+### Supported Tasks
+
+Text classification task: finding the common reply (out of ~37) to match the parent comment text.
+
+Text prediction task: estimating the vote score, or parent:reply ratio, of a meme response, as a measure of relevancy/cleverness of reply.
+
+### Languages
+
+Primarily English - includes some emoticons such as ┬─┬ノ(ಠ_ಠノ)
+
+## Dataset Structure
+
+### Data Instances
+
+29,375 rows
+
+### Data Fields
+
+- id: the Reddit alphanumeric ID for the reply
+- body: the content of the original reply
+- score: the net vote score of the original reply
+- parent_id: the Reddit alphanumeric ID for the parent
+- author: the Reddit username of the reply
+- subreddit: the Reddit community where the discussion occurred
+- parent_score: the net vote score of the parent comment
+- cleantext: the simplified reply (one of 37 classes)
+- tstamp: the timestamp of the reply
+- parent_body: the content of the original parent
+
+## Dataset Creation
+
+### Source Data
+
+Reddit comments collected through PushShift.io archives for 2014.
+
+#### Initial Data Collection and Normalization
+
+- Removed deleted or empty comments.
+- Selected only replies which scored 1.5x higher than a parent comment, where both have a positive score.
+- Found the top/repeating phrases common to these one-ups/clapback comments.
+- Selected only replies which had one of these top/repeating phrases.
+- Made rows in PostgreSQL and output as CSV.
+
+## Considerations for Using the Data
+
+Comments and responses in the Reddit archives and output datasets all include NSFW and otherwise toxic language and links!
+
+- You can use the subreddit and score columns to filter content.
+- Imbalanced dataset: replies 'yes' and 'no' are more common than others.
+- Overlap of labels: replies such as 'yes', 'yep', and 'yup' serve similar purposes; in other cases 'no' vs. 'nope' may be interesting.
+- Timestamps: the given timestamp may help identify trends in meme replies
+- Usernames: a username was included to identify the 'username checks out' meme, but this was not common enough in 2014, and the included username is from the reply.
+
+Reddit comments are properties of Reddit and comment owners using their Terms of Service.