Improve dataset card: Add paper link, metadata, and detailed description

This PR significantly improves the SEPSIS dataset card by:
* Adding a direct link to the associated paper: https://huggingface.co/papers/2312.00292.
* Including essential metadata such as `task_categories` (text-classification), `language` (en), `size_categories` (100K<n<1M), and relevant `tags` (deception-detection, fake-news).
* Providing a comprehensive description of the dataset in the content section, detailing its purpose, curation, size, and the four distinct annotation layers (type of omission, colors of lies, intention, and topic of lies).

These updates will greatly enhance the dataset's discoverability and provide users with a clearer understanding of its content and utility.

README.md

@@ -1,3 +1,24 @@
----
-license: mit
----
+---
+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](https://huggingface.co/papers/2312.00292).
+
+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.