metadata

dataset_info:
  features:
    - name: video_id
      dtype: string
    - name: asr_raw
      list:
        - name: start
          dtype: float64
        - name: end
          dtype: float64
        - name: text
          dtype: string
        - name: words
          list:
            - name: confidence
              dtype: float64
            - name: start
              dtype: float64
            - name: end
              dtype: float64
            - name: text
              dtype: string
    - name: asr_grouped
      list:
        list: string
    - name: ocr
      list:
        list: string
    - name: blip2_annotations
      struct:
        - name: actions
          list: string
        - name: captions
          list: string
        - name: objects
          list: string
    - name: replay_graphs
      struct:
        - name: original_marker_duration
          dtype: float64
        - name: processed_marker_duration
          dtype: float64
        - name: multiplier
          dtype: float64
        - name: markers
          list:
            - name: start
              dtype: float64
            - name: end
              dtype: float64
            - name: replay_score
              dtype: float64
    - name: likes
      dtype: float64
    - name: views
      dtype: float64
    - name: metadata
      struct:
        - name: title
          dtype: string
        - name: description
          dtype: string
        - name: length
          dtype: float64
        - name: date
          dtype: string
    - name: channel_data
      struct:
        - name: channel_id
          dtype: string
        - name: company_name
          dtype: string
        - name: subscribers
          dtype: float64
  splits:
    - name: train
      num_bytes: 396758465
      num_examples: 22569
    - name: test
      num_bytes: 35343326
      num_examples: 2026
  download_size: 135245985
  dataset_size: 432101791
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: mit
pretty_name: Content Behavior Corpus
language:
  - en
tags:
  - youtube
  - content
  - behavior
  - likes
  - views
  - transcript
  - captions
  - OCR
  - replay

Dataset Card for Content Behavior Corpus

The Content Behavior Corpus (CBC) dataset, consisting of content and the corresponding receiver behavior.

Dataset Details

The progress of Large Language Models (LLMs) has largely been driven by the availability of large-scale unlabeled text data for unsupervised learning. This work focuses on modeling both content and the corresponding receiver behavior in the same space. Although existing datasets have trillions of content tokens (text, images, audio, and videos), they lack information on receiver effects. To address this, the paper utilizes YouTube, a large publicly available source of content-behavior data, which includes:

Communicator Data: Channel name, and number of subscribers.
Message: Youtube video ids, extracted speech, scene-wise captions, on screen text, video description, video length, upload date.
Receiver Effect: Video likes, views, and replay graphs.

This covers all five factors of communication, with the channel being fixed (YouTube) and receivers being average channel subscribers and viewers.

Website: https://behavior-in-the-wild.github.io/LCBM
Paper: https://arxiv.org/abs/2309.00359

Dataset Structure

Fields

video_id (string): Unique identifier for each video.
asr_raw (list of objects): Raw Automatic Speech Recognition (ASR) data.
- start (float64): Start time of the ASR segment.
- end (float64): End time of the ASR segment.
- text (string): Transcription of the ASR segment.
- words (list of objects): Word-level ASR details.
  - confidence (float64): Confidence score of the ASR word.
  - start (float64): Start time of the word.
  - end (float64): End time of the word.
  - text (string): Transcription of the word.
asr_grouped (list of lists of strings): ASR transcriptions grouped by replay segments.
ocr (list of lists of strings): Optical Character Recognition (OCR) data for each replay segment.
blip2_annotations (object): BLIP-2 annotations for the video's replay segments.
- actions (list of strings): List of actions identified in each replay segment.
- captions (list of strings): List of image captions generated for each replay segment.
- objects (list of strings): List of objects identified in each replay segment.
replay_graphs (object): Data related to video replay behavior.
- original_marker_duration (float64): Original duration for replay segments.
- multiplier (float64): Number of original replay segments combined to create processed replay segments.
- processed_marker_duration (float64): Processed duration for replay segments.
- markers (list of objects): Replay segments.
  - start (float64): Start time of the replay segment.
  - end (float64): End time of the replay segment.
  - replay_score (float64 Score [0, 1]): indicating replay behavior.
likes (float64): Number of likes the video received.
views (float64): Number of views the video received.
metadata (object): Metadata associated with the video.
- title (string): Title of the video.
- description (string): Description of the video.
- length (float64): Length of the video in seconds.
- date (string): Publication date of the video.
channel_data (object): Information about the YouTube channel.
- channel_id (string): Unique identifier for the channel.
- company_name (string): Name of the company or individual owning the channel.
- subscribers (float64): Number of subscribers to the channel.

Data Collection and Processing

videos: Videos were downloaded using pytube.
asr_raw: Extracted using openai/whisper-medium and the whisper-timestamped library.
asr_grouped: Extracted words from asr_raw are grouped by the replay segment that they fall into. A word may fall into multiple replay segments if its duration intersects with multiple replay segments.
ocr: OCR extracted using PaddleOCR.
blip2_annotations: Annotations extracted using blip2-flan-t5-xxl.
replay_graphs: Extracted by directly parsing a video page's HTML content. Original replay segments are combined to have a duration >= 1 second, giving processed replay segments. processed_duration = multiplier * original_duration.
likes: Extracted by directly parsing a video page's HTML content.
views: Extracted by directly parsing a video page's HTML content.
metadata: Extracted by directly parsing a video page's HTML content.
channel_data: Extracted by directly parsing a video page's HTML content.

Citation

BibTeX:

@inproceedings{
  khandelwal2024large,
  title={Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior},
  author={Ashmit Khandelwal and Aditya Agrawal and Aanisha Bhattacharyya and Yaman Kumar and Somesh Singh and Uttaran Bhattacharya and Ishita Dasgupta and Stefano Petrangeli and Rajiv Ratn Shah and Changyou Chen and Balaji Krishnamurthy},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://arxiv.org/abs/2309.00359}
}

APA:

Khandelwal, A., Agrawal, A., Bhattacharyya, A., Kumar, Y., Singh, S., Bhattacharya, U., Dasgupta, I., Petrangeli, S., Shah, R. R., Chen, C., & Krishnamurthy, B. (2024). Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior. The Twelfth International Conference on Learning Representations. https://arxiv.org/abs/2309.00359

Contact

Contact behavior-in-the-wild@googlegroups.com for questions and suggestions.