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README.md

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 pretty_name: THVL-Bench
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+---
+# Dataset Card for Dataset Name
+THVL-Bench is a multimodal benchmark for Temporal Harmful Video Localization (THVL), designed to evaluate state-of-the-art Multimodal Large Language Models (MLLMs) on their ability to precisely localize harmful content in long-form videos, with fine-grained category labels and modality contribution annotations. The dataset contains 450 real-world long videos with 1,099 manually annotated harmful segments across 11 harm categories.
+
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+
+## Dataset Details
+
+### Dataset Description
+
+THVL-Bench is the first benchmark dedicated to the Temporal Harmful Video Localization (THVL) task, which requires models to jointly perform harmful event detection, precise temporal boundary localization, fine-grained harm category classification, and contributing modality attribution for long untrimmed videos. Unlike conventional video-level harmful content classification datasets, THVL-Bench focuses on the realistic moderation scenario where harmful content is temporally sparse and embedded in extended contextual content.
+The dataset consists of 450 real-world long videos (all ≥3 minutes in duration) collected from YouTube and Bilibili, with 1,099 manually annotated harmful segments. Each segment is annotated with:
+1. Precise start/end timestamps
+2. One or more labels from 11 fine-grained harmful categories
+3. Contributing modality labels (visual, textual, auditory)
+4. Auxiliary annotation rationales
+
+- **Curated by:** [authors of the THVL-Bench paper]
+- **Funded by [optional]:** [THVL-Bench Authors]
+- **Shared by [optional]:** [THVL-Bench Authors]
+- **Language(s) (NLP):** [Engish]
+- **License:** [CC BY-NC 4.0]
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [THVL-Bench: A Multimodal Benchmark for Temporal Localization of Harmful Content in Long Videos]
+- **Demo [optional]:** [N/A]
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+[More Information Needed]
+
+## Dataset Structure
+The dataset is released as a single CSV file with 450 rows (one row per video) and 4 core columns, with segment-level annotations stored in structured list format:
+Column Name	Description
+videoID	Unique identifier for each video, corresponding to the original platform's video ID
+segment-level annotation	One-hot encoded labels for 11 harmful categories, with one list per annotated segment in the video
+segment-level timestamp	Start and end timestamps (in mm:ss format) for each annotated harmful segment, with one pair per segment
+contributing modalities	One-hot encoded labels for 3 contributing modalities (visual, textual, auditory), with one list per annotated segment
+Key Dataset Statistics
+Total videos: 450
+Total annotated harmful segments: 1,099
+Video duration range: 3–40 minutes
+Average harmful segment duration: 68.31 seconds
+Harmful categories: 11 fine-grained classes (Information Harm, Verbal Abuse, Hate, Bias, Addiction Harm, Sexual Harm, Physical Harm, Violence, Blood/Gore, Criminal Activity, Danger)
+Contributing modalities: 3 classes (Visual, Textual, Auditory)
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+[More Information Needed]
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+[More Information Needed]
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+[More Information Needed]
+
+### Annotations [optional]
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+Annotator Training: All annotators underwent a calibration phase using 20 pilot videos before formal annotation, with clear guidelines on temporal boundary marking, category labeling, and modality attribution
+Dual Annotation: Each video was independently annotated by two individuals, who identified harmful events, marked precise start/end timestamps, assigned category labels, identified contributing modalities, and wrote brief annotation rationales
+Disagreement Resolution: Disagreements regarding temporal boundaries, categories, or modalities were first addressed through discussion; unresolved cases were sent for a second round of review, with final labels determined by majority voting if consensus was not reached
+Quality Control: Inter-annotator agreement was measured on a randomly sampled subset, achieving a Cohen’s κ=0.74 for segment localization, with 92.3% agreement on category and modality labels
+
+
+#### Who are the annotators?
+
+THVL-Bench was annotated by four experts with extensive experience in multimodal video analysis and content understanding. All annotators were informed of the potential risks prior to their participation and had unrestricted access to institutional psychological counseling resources throughout the annotation process.
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+Limitations
+Scale Constraints: The current benchmark contains 450 videos with 1,099 annotated segments, which is relatively limited compared to large-scale general video datasets
+Category Distribution Imbalance: Some harmful categories (e.g., Hate, Addiction Harm, Physical Harm) have fewer annotated samples than more prevalent categories (e.g., Danger, Violence, Criminal Activity), which may affect model evaluation on underrepresented classes
+Limited Context Modeling: Annotations primarily focus on temporal grounding and modality attribution, while higher-level contextual factors such as intent, social context, and cultural nuance are not fully modeled
+Platform Bias: Videos are collected from YouTube and Bilibili, which may not fully represent harmful content scenarios on other video platforms or in other regions
+Risks
+Sensitive Content Exposure: The dataset describes and annotates harmful content including violence, hate speech, criminal activities, and other unsafe behaviors, which may be disturbing to some users
+Potential Misuse: The dataset could be misused to develop or optimize systems that generate or distribute harmful content
+Bias Amplification: Models trained on the dataset may amplify existing biases in the annotation data or source content if not properly validated
+### Recommendations
+Users should be made aware of the risks, biases and limitations of the dataset. Key recommendations include:
+Responsible Use: Use the dataset exclusively for research purposes related to video safety, harmful content understanding, and multimodal AI safety
+Bias Mitigation: When training models on the dataset, implement bias mitigation techniques to address category distribution imbalance and avoid amplifying harmful stereotypes
+Ethical Review: Conduct ethical review of any systems or models developed using the dataset before deployment in real-world scenarios
+Compliance: Comply with the terms of service of the original video platforms and the CC BY-NC 4.0 license of the dataset
+Mental Wellbeing: Take appropriate precautions when working with the dataset, as it describes sensitive and potentially disturbing harmful content
+
+Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Dataset Card Authors [optional]
+
+[More Information Needed]
+
+## Dataset Card Contact
+
+[More Information Needed]