Omkar2118/datasets huggingface dataset:
Content Behavior Corpus

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.

Introducing paper: Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
LAMBDA
Full name: Long-term Ad MemoraBility DAtaset

LAMDBA is a long term ad memorability dataset, featuring data from 1749 participants and 2205 ads across 276 brands.

Introducing paper: Long-Term Ad Memorability: Understanding & Generating Memorable Ads
UltraLAMBDA

UltraLAMBDAis a large-scale dataset of ads sourced from brand videos on platforms such as YouTube and Facebook Ads, as well as from CommonCrawl. The memorability scores for the ads are assigned by our model Henry.

Introducing paper: Long-Term Ad Memorability: Understanding & Generating Memorable Ads