The Good, the Bad, and the Angry: Analyzing Crowdsourced Impressions of Vloggers

We address the study of interpersonal perception in social conversational video based on multifaceted impressions collected from short video-watching. First, we crowdsourced the annotation of personality, attractiveness, and mood impressions for a dataset of YouTube vloggers, generating a corpora that has potential to develop automatic techniques for vlogger characterization. Then, we provide an analysis of the crowdsourced annotations focusing on the level of agreement among annotators, as well as the interplay between different impressions. Overall, this work provides interesting new insights on vlogger impressions and the use of crowdsourcing to collect behavioral annotations from multimodal data.


Presented at:
Proceedings of AAAI International Conference on Weblogs and Social Media
Year:
2012
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 Record created 2013-12-19, last modified 2018-09-13

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