VlogSense: Conversational Behavior and Social Attention in YouTube

We introduce the automatic analysis of conversational vlogs (VlogSense, for short) as a new research domain in social media. Conversational vlogs are inherently multimodal, depict natural behavior, and are suitable for large-scale analysis. Given their diversity in terms of content, VlogSense requires the integration of robust methods for multimodal analysis and for social media understanding. We present an original study on the automatic characterization of vloggers' audiovisual nonverbal behavior, grounded in work from social psychology and behavioral computing. Our study on 2,269 vlogs from YouTube shows that several nonverbal cues are significantly correlated with the social attention received by videos.

Vlogcast Yourself: Nonverbal Behavior and Attention in Social Media

Joan Isaac Biel Tres, Daniel Gatica-Perez

We introduce vlogs as a type of rich human interaction which is multimodal in nature and suitable for new large-scale behavioral data analysis. The automatic analysis of vlogs is useful not only to study social media, but also remote communication scenarios, and requires the integration of methods for multimodal processing and for social media understanding. Based on works from social psychology and computing, we rst propose robust audio, visual, and multimodal cues to measure the nonverbal behavior of vloggers in their videos. Then, we investigate the relation between behavior and the attention videos receive in YouTube. Our study shows signicant correlations between some nonverbal behavioral cues and the average number of views per video.

2010

The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

Joan Isaac Biel Tres, Daniel Gatica-Perez

Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.

Ieee-Inst Electrical Electronics Engineers Inc2013

You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in YouTube

Oya Aran, Joan Isaac Biel Tres, Daniel Gatica-Perez

An increasing interest in understanding human perception in social media has led to the study of the processes of personality self-presentation and impression formation based on user profiles and text blogs. However, despite the popularity of online video, we do not know of any attempt to study personality impressions that go beyond the use of text and still photos. In this paper, we analyze one facet of YouTube as a repository of brief behavioral slices in the form of personal conversational vlogs, which are a unique medium for selfpresentation and interpersonal perception. We investigate the use of nonverbal cues as descriptors of vloggers’ behavior and find significant associations between automatically extracted nonverbal cues for several personality judgments. As one notable result, audio and visual cues together can be used to predict 34% of the variance of the Extraversion trait of the Big Five model. In addition, we explore the associations between vloggers’ personality scores and the level of social attention that their videos received in YouTube. Our study is conducted on a dataset of 442 YouTube vlogs and 2,210 annotations collected using Amazon’s Mechanical Turk.

2011

The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

Joan Isaac Biel Tres, Daniel Gatica-Perez

Ieee-Inst Electrical Electronics Engineers Inc2013