The DOI matches its Crossref record; 55 of the 60 references it cites were found in a registry or an index.
The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs
Open accessAbstract
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.
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“The main difference with respect to “standard” SSP approaches is the adoption of social network inspired features (e.g., number of times a video has been watched, on-line ratings, etc.) typically available in social media (Biel and Gatica-Perez, 2012; Salvagnini et al., 2012).”
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BackgroundComputational personality recognition in social media
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Background
Metadata
- Authors
- Biel, J.-I., & Gatica-Perez, D.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2013-01
- Volume
- 15
- Issue
- 1
- Pages
- 41-55
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2028422222
- Cited by
- 209
- References
- 60
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 7 · 2023: 9 · 2022: 16 · 2021: 20 · 2020: 16 · 2019: 20 · 2018: 18 · 2017: 24 · 2016: 23 · 2015: 13 · 2014: 20 · 2013: 6
- FWCI
- 17.14
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- personality impressions, vlogs, YouTube, crowdsourcing, nonverbal cues, audiovisual analysis, social attention, personality prediction, conversational video, vloggers, social media perception
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