Update README.md

README.md

@@ -23,3 +23,65 @@ The dataset is also complemented by an online analytics platform (https://hurric
 
 To our best knowledge, MASH is the first large-scale, multi-platform, multimodal, and multi-dimensionally annotated hurricane dataset. 
 We envision that MASH can contribute to the study of hurricanes' impact on society, such as disaster severity classification, event detections, public sentiment analysis, and bias identification.
+
+## 📘 Usage Notice
+
+This dataset includes four annotation files:
+
+	• reddit_anno_publish.csv
+	• tiktok_anno_publish.csv
+	• twitter_anno_publish.csv
+	• youtube_anno_publish.csv
+
+Each file contains post IDs and corresponding annotations on three dimensions:
+Humanitarian Classes, Bias Classes, and Information Integrity Classes.
+
+To protect user privacy, only post IDs are released. We recommend retrieving the full post content via the official APIs of each platform, in accordance with their respective terms of service.
+- [Reddit API](https://www.reddit.com/dev/api)  
+- [TikTok API](https://developers.tiktok.com/products/research-api)  
+- [X/Twitter API](https://developer.x.com/en/docs/x-api)  
+- [YouTube API](https://developers.google.com/youtube/v3)
+
+## Humanitarian Classes
+
+Each post is annotated with seven binary humanitarian classes. For each class, the label is either:
+
+	• True – the post contains this humanitarian information
+	• False – the post does not contain this information
+
+These seven humanitarian classes include:
+
+	• Casualty
+	• Evacuation
+	• Damage
+	• Advice
+	• Request
+	• Assistance
+	• Recovery
+
+Note: A single post may be labeled as True for multiple humanitarian categories.
+
+## Bias Classes
+
+Each post is annotated with five binary bias classes. For each class, the label is either:
+
+	• True – the post contains this bias information
+	• False – the post does not contain this information
+
+These five bias classes include:
+
+	• Linguistic Bias
+	• Political Bias
+	• Gender Bias
+	• Hate Speech
+	• Racial Bias
+
+Note: A single post may be labeled as True for multiple bias categories.
+
+## Information Integrity Classes
+
+Each post is also annotated with a single information integrity class, represented by an integer:
+
+	• -1 → False information (i.e., misinformation or disinformation)
+	• 0 → Unverifiable information (unclear or lacking sufficient evidence)
+	• 1 → True information (verifiable and accurate)