3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos

TL;DR

3MASSIV is a comprehensive multilingual, multimodal dataset of 50,000 short social media videos from Moj, annotated for concepts, affect, media types, and audio language, enabling advanced semantic and cross-lingual analysis.

Contribution

This paper introduces 3MASSIV, a novel large-scale dataset capturing diverse social media short videos with rich annotations for multimodal and multilingual research.

Findings

Dataset covers 11 languages and various video trends.

Strong baselines demonstrate potential for semantic understanding.

Highlights the dynamic and temporal nature of social media videos.

Abstract

We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for multimodal and multilingual semantic understanding on these unique videos by annotating them for concepts, affective states, media types, and audio language. We present a thorough analysis of 3MASSIV and highlight the variety and unique aspects of our dataset compared to other contemporary popular datasets with strong baselines. We also show how the social media content in 3MASSIV is dynamic and temporal in nature, which can be used for semantic understanding tasks and cross-lingual analysis.