license: mit
task_categories:
- text-classification
language:
- en
size_categories:
- 100K<n<1M
tags:
- deception-detection
- fake-news
This repository contains the SEPSIS dataset, introduced in the paper SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection.
The SEPSIS dataset is a novel, large-scale annotated dataset designed for deception detection, specifically focusing on "lies of omission" using Natural Language Processing (NLP) techniques. It comprises 876,784 samples, curated by amalgamating a popular large-scale fake news dataset and scraped news headlines from the Twitter handle of the Times of India.
Each sample in the dataset is labeled with four distinct layers of annotation, providing a comprehensive understanding of deceptive content:
- Type of omission: Categorizes the type of omission into speculation, bias, distortion, sounds factual, and opinion.
- Colors of lies: Identifies the moral or ethical implications of the lie (e.g., black, white).
- Intention of such lies: Indicates the underlying purpose of the lie (e.g., to influence).
- Topic of lies: Specifies the subject matter of the deceptive content (e.g., political, educational, religious).
This dataset aims to encourage further research in the field of deception detection and explores the intricate relationship between lies of omission and propaganda techniques.