  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.
