Update README.md

README.md

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 pretty_name: Multiplatform Annotated Dataset for Societal Impact of Hurricane
-description: >
-  We present a Multiplatform Annotated Dataset for Societal Impact of Hurricane (MASH) that includes 98,662 relevant social media data posts from Reddit, X, TikTok, and YouTube. 
-  In addition, all relevant social media data posts are annotated on three dimensions: Humanitarian Classes, Bias Classes, and Information Integrity Classes. 
-  The dataset is also complemented by an online analytics platform that not only allows users to view hurricane-related posts and articles, but also explores high-frequency keywords, user sentiment, and the locations where posts were made.
-  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.
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+---
+# MASH: A Multiplatform Annotated Dataset for Societal Impact of Hurricane
+
+We present a Multiplatform Annotated Dataset for Societal Impact of Hurricane (MASH) that includes 98,662 relevant social media data posts from Reddit, X, TikTok, and YouTube. 
+In addition, all relevant social media data posts are annotated on three dimensions: Humanitarian Classes, Bias Classes, and Information Integrity Classes. 
+
+The dataset is also complemented by an online analytics platform (https://hurricane.web.illinois.edu/) that not only allows users to view hurricane-related posts and articles, but also explores high-frequency keywords, user sentiment, and the locations where posts were made.
+
+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.