MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane Article Swipe

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Anonymous · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5281/zenodo.17682231

We present a Multiplatform Annotated Dataset for Societal Impact of Hurricane (MASH) that includes 59,607 relevant social media data posts from Reddit, TikTok, and YouTube.    In addition, all relevant posts are annotated on three dimensions: Humanitarian Classes, Bias Classes, and Information Integrity Classes in a multi-modal approach that considers both textual and visual content (text, images, and videos), providing a rich labeled dataset for in-depth analysis.   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 response, disaster severity classification, public sentiment analysis, disaster policy making, and bias identification.   Usage Notice This dataset includes four annotation files:   • reddit_anno_publish.csv • tiktok_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)   - 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: The post reports people or animals who are killed, injured, or missing during the hurricane. • Evacuation: The post describes the evacuation, relocation, rescue, or displacement of individuals or animals due to the hurricane. • Damage: The post reports damage to infrastructure or public utilities caused by the hurricane. • Advice: The post provides advice, guidance, or suggestions related to hurricanes, including how to stay safe, protect property, or prepare for the disaster. • Request: Request for help, support, or resources due to the hurricane • Assistance: This includes both physical aid and emotional or psychological support provided by individuals, communities, or organizations. • Recovery: The post describes efforts or activities related to the recovery and rebuilding process after the hurricane.    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: The post contains biased, inappropriate, or offensive language, with a focus on word choice, tone, or expression. • Political Bias: The post expresses political ideology, showing favor or disapproval toward specific political actors, parties, or policies. • Gender Bias: The post contains biased, stereotypical, or discriminatory language or viewpoints related to gender.  • Hate Speech: The post contains language that expresses hatred, hostility, or dehumanization toward a specific group or individual, especially those belonging to minority or marginalized communities. • Racial Bias: The post contains biased, discriminatory, or stereotypical statements directed toward one or more racial or ethnic groups.    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)   Key Notes Versions 1 and 2 are no longer available. 

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other

Language

en

Landing Page

https://doi.org/10.5281/zenodo.17682231

OA Status

green

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https://openalex.org/W7106293739

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https://openalex.org/W7106293739

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DOI

https://doi.org/10.5281/zenodo.17682231

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Title

MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane

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other

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en

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Publication year

2025

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2025-11-22

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Anonymous

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https://doi.org/10.5281/zenodo.17682231

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Yes

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green

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https://doi.org/10.5281/zenodo.17682231

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Concepts

Annotation, Social media, Computer science, Humanitarian aid, World Wide Web, Social impact, Societal impact of nanotechnology, Data science, Information retrieval, Crowdsourcing, Sentiment analysis, User-generated content, Humanitarian crisis, Computer security, Impact assessment

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