LCS2-IIITD/Hyphen

(NeurIPS 2022) Official Implementation of Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification

27
/ 100
Experimental

This project helps classify social media text, such as news posts, into categories like fake news, hate speech, sarcasm, or rumors. It takes a CSV file containing social media posts and their associated public comments, then outputs a classification for each post. This is ideal for social media analysts, content moderators, or researchers studying online discourse.

No commits in the last 6 months.

Use this if you need to automatically detect and classify types of problematic content like fake news, hate speech, sarcasm, or rumors on social media platforms by leveraging both the post content and public comments.

Not ideal if you only need basic text classification without considering the nuanced context of public discourse, or if your data is not from social media or lacks user comments.

social-media-analysis fake-news-detection hate-speech-detection online-content-moderation rumor-detection
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 4 / 25

How are scores calculated?

Stars

32

Forks

1

Language

Python

License

MIT

Last pushed

Jan 16, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/LCS2-IIITD/Hyphen"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.