README.md

metadata

language:
  - zh
  - en
pretty_name: TopicVid

TopicVid Dataset

This dataset provides structured metadata, content features, and a heterogeneous graph related to short-video topics and subtopics. It is designed for tasks such as topic analysis, audience interaction modeling, peak prediction, and research on graph neural networks or graph retrieval.

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Contents

• available_dataset_with_subtopic.json — Processed structured raw data of short video content and interaction statistics about topics.
• comment.npy — Comment features.
• content.npy — Content features.
• desc.npy — Description features.
• heterogeneous_graph.pkl — Heterogeneous graph file.
• title.npy — Title features.
• topic.npy — Topic embeddings.
• video.npy — Video features.

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Data Structure

1) available_dataset_with_subtopic.json

This file contains the raw data of short video content and interaction statistics.

Fields:

• url (string) — Direct link to the video on the platform.
• desc (string) — Description text of the video content.
• title (string) — Title of the video post.
• content (string) — Additional text content; may be empty.
• user_id (string) — Unique identifier of the publishing user.
• duration (integer) — Video duration in seconds.
• platform (string) — Source platform name (e.g., Douyin, Kuaishou).
• post_create_time (string) — Time of publication in "YYYY-MM-DD HH:MM:SS" format.
• topic (string) — Main topic associated with the video.
• subtopic (string) — Numbered subcategory under the main topic.
• time_frames (dict) — Interaction statistics recorded at different dates.
  • Key: Date in "YYYY-MM-DD" format
  • Value: Dictionary with fields:
    • fans_count — Number of followers
    • like_count — Number of likes
    • view_count — Number of views
    • share_count — Number of shares
    • collect_count — Number of collections
    • comment_count — Number of comments
• comments (dict) — Collection of user comments.
  • Key: Comment index (string)
  • Value: Dictionary with fields:
    • comment_user_id — Commenting user ID
    • comment_nickname — Commenting user's display name
    • comment_content — Comment text
    • comment_time — Time of comment
    • ip_address — IP location of the commenting user

2) *.npy

Numpy arrays containing preprocessed embeddings or feature vectors.

3) heterogeneous_graph.pkl

A serialized Python object containing:

• Node types and indices
• Edge types and lists
• Labels information is available at link