Matin Yarmand
Ido Roll
ABSTRACT
As videos become increasingly ubiquitous, so is video-based commenting. To contextualize comments, people often reference specific audio/visual content within video. However, the literature falls short of explaining the types of video content people refer to, how they establish references and identify referents, how video characteristics (e.g., genre) impact referencing behaviors, and how references impact social engagement. We present a taxonomy for classifying video references by referent type and temporal specificity. Using our taxonomy, we analyzed 2.5K references with quotations and timestamps collected from public YouTube comments. We found: 1) people reference intervals of video more frequently than time-points, 2) visual entities are referenced more often than sounds, and 3) comments with quotes are more likely to receive replies but not more "likes". We discuss the need for in-situ dereferencing user interfaces, illustrate design concepts for typed referencing features, and provide a dataset for future studies.
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Our dataset consists of 2,994 potential references from 7 genres which were collected using YouTube Data API. Every entry consists of a number of comment and video attributes (such as video link, comment ID, and etc), as well as 3 others that were assigned during our coding process (Referent Type, Expression, and Temporal Specificity).
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Index Terms
- "Can you believe [1:21]?!": Content and Time-Based Reference Patterns in Video Comments
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