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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BackgroundSelf-citationUnderstanding Emoji Interpretation through User Personality and Message Context
Völkel, S. T., Buschek, D., Pranjic, J., & Hußmann, H. (2019) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3338286.3340114 · Cited by 51 · see citing works
“Ex-traverts were also found to gesture more often and more expan-sively [8, 42].”
BackgroundAutomated Screening of Job Candidate Based on Multimodal Video Processing
Gorbova, J., Lüsi, I., Litvin, A., & Anbarjafari, G. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.214 · Cited by 50 · see citing works
“The first impression database contains 10000 video-clips taken from more than 3000 different HD YouTube videos, where people are mostly sitting and speaking in English in front of the camera [8, 12].”
BackgroundOn the Effect of Observed Subject Biases in Apparent Personality Analysis From Audio-Visual Signals
Principi, R. D. P., Palmero, C., Jacques, J. C. S., & Escalera, S. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2956030 · Cited by 49 · see citing works
“Despite the great advances of previous work [18], [19], [20] to push the research on the topic, their studies are limited to analyses performed on relatively small datasets (i.”
BackgroundCorpus Development for Affective Video Indexing
Soleymani, M., Larson, M., Pun, T., & Hanjalić, A. (2014) · Archive ouverte UNIGE (University of Geneva) · Cited by 49 · see citing works
Social Profiling: A Review, Taxonomy, and Challenges
Bilal, M., Gani, A., Lali, M. I. U., Marjani, M., & Malik, N. (2019) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2018.0670 · Cited by 48 · see citing works
“The data collected from Facebook 80–84 and YouTube videos 85–87 have been used by researchers in making user personality predictions.”
Deep Personality Trait Recognition: A Survey
Zhao, X., Tang, Z., & Zhang, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.839619 · Cited by 47 · see citing works
Güçlütürk, Y., Güçlü, U., Gerven, M. A. J. V., Lier, R. J. V., Hua, G., & Jégou, H. (2016) · Radboud Repository (Radboud University) · Cited by 47 · see citing works
Training on the job: behavioral analysis of job interviews in hospitality
Muralidhar, S., Nguyen, L. S., Frauendorfer, D., Odobez, J., Mast, M. S., & Gática-Pérez, D. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2993191 · Cited by 44 · see citing works
“Overall Visual Motion We utilize a modified version of motion energy images, called Weighted Motion Energy Images (WMEI) [6] to capture the total amount of visual movement displayed by the applicant and interviewer during the job interview.”
BackgroundInfluentialPersonality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources
Flek, L., & Gurevych, I. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1208 · Cited by 42 · see citing works
“More recent work in this area focuses on the personality prediction in social networks (Kosinski et al., 2013; Kosinski et al., 2014) and multimodal personality prediction (Biel and Gatica-Perez, 2013; Aran and Gatica-Perez, 2013).”
BackgroundA Multivariate Regression Approach to Personality Impression Recognition of Vloggers
Farnadi, G., Sushmita, S., Sitaraman, G., Ton, N., Cock, M. D., & Davalos, S. V. (2014) · doi:10.1145/2659522.2659526 · Cited by 37 · see citing works
“In particular, we measure the performance of 5 multivariate regression techniques [18] on a YouTube personality dataset [4].”
BackgroundMethodResultInfluentialBeyan, C., Zunino, A., Shahid, M., & Murino, V. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2944614 · Cited by 37 · see citing works
Multimodal analysis of personality traits on videos of self-presentation and induced behavior
Giritlioğlu, D., Mandira, B., Yilmaz, S. F., Ertenli, C. U., Akgür, B. F., Kınıklıoğlu, M., Kurt, A. G., Mutlu, E., Gürel, Ş. C., & Dibeklioğlu, H. (2020) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-020-00347-7 · Cited by 40 · see citing works
Multimodal Personality Recognition in Collaborative Goal-Oriented Tasks
Batrinca, L., Mana, N., Lepri, B., Sebe, N., & Pianesi, F. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2522763 · Cited by 37 · see citing works
“As we have shown in Section II, several works have started to target the computational recognition of personality in different scenarios [18]–[21] and from different sources of data [31]–[35].”
BackgroundInfluentialAutomatic multimodal assessment of soft skills in social interactions: a review
Rasipuram, S., & Jayagopi, D. B. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08561-6 · Cited by 36 · see citing works
“The dataset used in [12] was collected in the wild.”
MethodMultimodal analysis of body communication cues in employment interviews
Nguyen, L. S., Marcos-Ramiro, A., Marrón-Romera, M., & Gática-Pérez, D. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2522860 · Cited by 36 · see citing works
“These include small group interactions [25], video blogs [7], or human-computer interaction [6].”
BackgroundMethodPersonality perception of robot avatar tele-operators
Bremner, P., Celiktutan, O., & Gunes, H. (2016) · 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2016.7451745 · Cited by 36 · see citing works
“To facilitate this we aim to apply automated personality classifiers to our data (such as [29], [37]), which can extract and identify useful cues automatically.”
MethodČereković, A., Aran, O., & Gática-Pérez, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11839-0_1 · Cited by 34 · see citing works
“The visual activity of the subject is extracted by using weighted motion energy images (wMEI), which is a binary image that describes the spatial motion distribution in the video sequence [3].”
BackgroundMethodInfluentialText-based automatic personality prediction: a bibliographic review
Feizi-Derakhshi, A.-R., Feizi‐Derakhshi, M., Ramezani, M., Nikzad-Khasmakhi, N., Asgari-Chenaghlu, M., Akan, T., Ranjbar-Khadivi, M., Zafarani-Moattar, E., & Jahanbakhsh-Nagadeh, Z. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00178-4 · Cited by 38 · see citing works
“As mentioned, YouTube dataset is a multimodal, video, speech, and transcript to text [82, 83].”
MethodAnalyzing Interpersonal Empathy via Collective Impressions
Kumano, S., Otsuka, K., Mikami, D., Matsuda, M., & Yamato, J. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2417561 · Cited by 32 · see citing works
“…contagion” between a pair of participants, and defines perceived empathy (perceived counter-empathy) as ”the impression of an observer who judges that a pair of participants engaged in a multi-party conversation is in an empathic (counter-empathic) state at that moment, where the pair are…”
BackgroundInferring mood in ubiquitous conversational video
Sánchez-Cortés, D., Biel, J.-I., Kumano, S., Yamato, J., Otsuka, K., & Gática-Pérez, D. (2013) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/2541831.2541864 · Cited by 31 · see citing works
“[6] J.-I. Biel and D. Gatica-Perez.”
MethodInfluentialSelf-citationAutomatic assessment of communication skill in interview-based interactions
Rasipuram, S., & Jayagopi, D. B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5654-9 · Cited by 28 · see citing works
“Non-verbal audio cues have shown to be significant for both the contexts of interface-based interviews where no human is involved [4] and for face-to-face when a conversational partner is involved [9].”
BackgroundMethodInfluentialTrustable Co-Label Learning From Multiple Noisy Annotators
Li, S., Liu, T., Tan, J., Zeng, D., & Ge, S. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3137752 · Cited by 28 · see citing works
INNOVATIVE PERSONALITY-BASED DIGITAL SERVICES
Buettner, R. (2016) · doi:10.13140/rg.2.1.4417.6889 · Cited by 27 · see citing works
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis
Jacques, J. C. S., Güçlütürk, Y., Pérez, M., Güçlü, U., Andújar, C., Baró, X., Escalante, H. J., Guyon, I., van Gerven, M. A. J., van Lier, R., et al. (2018) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · doi:10.48550/arxiv.1804.08046 · Cited by 26 · see citing works
“Results show that extraversion is the trait showing the largest activity cue utilization (reinforced in Table 2), which is related to evidences found in the literature that extraversion is typically easier to judge [54], [55].”
BackgroundMethodResultInfluentialShen, Z., Elibol, A., & Chong, N. Y. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103874 · Cited by 27 · see citing works
Mori, K., & Haruno, M. (2020) · Journal of Personality · doi:10.1111/jopy.12578 · Cited by 25 · see citing works
“Thus, this study extended previous research that reported personality predictions based on digital footprints including SNS (Biel & Gatica-Perez, 2013; de Montjoye et al., 2013; Guntuku et al., 2017; Kosinski et al., 2013; Quercia et al., 2011) in terms of both the range of personality traits and…”
BackgroundResultAlam, F., & Riccardi, G. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853738 · Cited by 25 · see citing works
“There have been significant studies on predicting personality traits from social media by analyzing text, audio and video: Twitter [14], Facebook [22,23], blog [24] and video-blog [19].”
BackgroundAutomated Prediction of Extraversion During Human–Humanoid Interaction
Anzalone, S. M., Varni, G., Ivaldi, S., & Chétouani, M. (2017) · International Journal of Social Robotics · doi:10.1007/s12369-017-0399-6 · Cited by 25 · see citing works
“In [11], the authors proposed the recognition of “personality states” from the analysis of nonverbal social signals in 442 Youtube video blogs.”
BackgroundLiu, Z., Rehman, A., Wu, M., Cao, W., & Hao, M. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3025108 · Cited by 25 · see citing works
“Contrastively, accuracy decreases when fewer features are used [18], [19], hence, the search for efficient tradeoffs.”
BackgroundA Hybrid Approach for Emotion Detection in Support of Affective Interaction
Gievska, S., Koroveshovski, K., & Chavdarova, T. (2014) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2014.130 · Cited by 25 · see citing works
“The set of audio-visual features provided with the dataset of 404 vlogs made available by the IDIAP Research Institute [35] was extended with: 1) coursegrain affective features, the six Ekman’s emotional categories detected by our hybrid method and represented by their 10 http://conceptnet5.media.mit.edu/ valences; an…”
MethodRomeo, M., García, D. H., Han, T., Cangelosi, A., & Jokinen, K. (2021) · Advanced Robotics · doi:10.1080/01691864.2021.1974941 · Cited by 24 · see citing works
“The VLOG dataset [17] consists of videos between 1 and 6min long from 469 different vloggers.”
BackgroundMethodРюмина, Е. В., Markitantov, M., Ryumin, D., & Karpov, A. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122441 · Cited by 23 · see citing works
Sánchez-Cortés, D., Kumano, S., Otsuka, K., & Gática-Pérez, D. (2015) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2641577 · Cited by 23 · see citing works
“The extraction of these nonverbal cues in social video is challenging due to the variety of content available, but has nevertheless been addressed to build computational models of vlogger personality [Biel and Gatica-Perez 2013].”
BackgroundLoud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
Santani, D., & Gática-Pérez, D. (2014) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.48550/arxiv.1403.1168 · Cited by 22 · see citing works
“ACM 978-1-4503-3459-4/15/10 ...$15.00. http://dx.doi.org/10.1145/2733373.2806277.”
InfluentialInvestigation of Small Group Social Interactions Using Deep Visual Activity-Based Nonverbal Features
Beyan, C., Shahid, M., & Murino, V. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240685 · Cited by 22 · see citing works
“[10, 11], by using broadcasted radio clips in [44], by investigating smartphone usage of people in [14] but this review considers small group meetings only.”
BackgroundAutomatic personality prediction from audiovisual data using random forest regression
Aydin, B., Kındıroğlu, A. A., Aran, O., & Akarun, L. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899604 · Cited by 22 · see citing works
“used audio features such as speaking activity and prosodic cues, and visual features like looking activity, pose, and overall visual activity [20], [21].”
Method“What’s So Awesome with YouTube”: Learning Music with Social Media Celebrities
Marone, V., & Rodriguez, R. C. (2019) · Online Journal of Communication and Media Technologies · doi:10.29333/ojcmt/5955 · Cited by 31 · see citing works
“…these outlets have also provided opportunities to grassroots authors to become emergent social media celebrities or “micro-celebrities” (Ashraf, 2009; Biel & Gatica-Perez, 2013; Marwick, 2015) who may become famous thanks to a single video that “goes viral” (Khan & Vong, 2014), i.e., that is…”
BackgroundAI-based personality prediction for human well-being from text data: a systematic review
Singh, S., & Singh, W. (2023) · Multimedia tools and applications · doi:10.1007/s11042-023-17282-w · Cited by 17 · see citing works
“The cross-lingual embeddings were obtained by training the CNN model on combined MyPersonality [71] and Vloggers [72] dataset with input as bilingual word embeddings to represent text.”
Multi-domain and multi-task prediction of extraversion and leadership from meeting videos
Kındıroğlu, A. A., Akarun, L., & Aran, O. (2017) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-017-0224-z · Cited by 19 · see citing works
“More details about the dataset and the annotations can be found in [8].”
BackgroundIntegrating Vision and Language for First-Impression Personality Analysis
Gorbova, J., Avots, E., Lüsi, I., Fishel, M., Escalera, S., & Anbarjafari, G. (2018) · IEEE Multimedia · doi:10.1109/mmul.2018.023121162 · Cited by 19 · see citing works
Hide and Seek: Choices of Virtual Backgrounds in Video Chats and Their Effects on Perception
Hwang, A. H., Wang, C. Y., Yang, Y.-Y., & Won, A. S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476044 · Cited by 18 · see citing works
Santani, D., & Gática-Pérez, D. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806277 · Cited by 18 · see citing works
“ACM 978-1-4503-3459-4/15/10 ...$15.00. http://dx.doi.org/10.1145/2733373.2806277.”
InfluentialStressful first impressions in job interviews
Finnerty, A. N., Muralidhar, S., Nguyen, L. S., Pianesi, F., & Gática-Pérez, D. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2993198 · Cited by 18 · see citing works
“This acts as an indicator of kinetic expressiveness [9].”
BackgroundA Face-to-Face Neural Conversation Model
Chu, H., Li, D., & Fidler, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00743 · Cited by 18 · see citing works
“We pre-train our micro-gesture module on the FirstImpression dataset [3], which contains close-up talking videos that allows high precision tracking of micro-gesture.”
MethodRadisavljević, D., Rzepka, R., & Araki, K. (2023) · Applied Sciences · doi:10.3390/app13074506 · Cited by 17 · see citing works
Nowson, S., & Gill, A. J. (2014) · doi:10.1145/2659522.2659530 · Cited by 17 · see citing works
“One of the most important individual di.erences is personality, arguably one of the central tenets of the notion of self.”
In Your Eyes: Modality Disentangling for Personality Analysis in Short Video
Sun, X., Liu, B., Ai, L., Liu, D., Meng, Q., & Cao, J. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3161708 · Cited by 17 · see citing works
“Biel and Gatica-Perez [30] analyzed the speech, rhythm, gaze, motion, and other features contained in the video and found that the prediction result of audio-visual combined information was significantly better than pictures or audio alone.”
BackgroundFirst Impressions - Predicting User Personality from Twitter Profile Images
Dhall, A., & Hoey, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46843-3_10 · Cited by 17 · see citing works
Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking
Sharma, R. A., Guha, T., & Sharma, G. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00058 · Cited by 17 · see citing works
“al interactions. Computational effort in this domain, though relatively recent, is as diverse as analyzing couples’ interaction [2], to personality analysis [3], to video blogging style analysis [4], [5]. Public speaking is an important aspect of human communication and interaction. A good speaker is articulate, has c…”
BackgroundSafari, F., & Chalechale, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10603-3 · Cited by 16 · see citing works
Stronger Together: Personality, Intelligence and the Assessment of Career Potential
Leutner, F., & Chamorro‐Premuzic, T. (2018) · Journal of Intelligence · doi:10.3390/jintelligence6040049 · Cited by 16 · see citing works
“Non-verbal behavioral cues work best for predicting Extraversion [101,102], whilst the remaining Big Five traits are better predicted by verbal content [103].”
BackgroundSmith, K. A., Dennis, M., Masthoff, J., & Tintarev, N. (2019) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09219-6 · Cited by 16 · see citing works
“Biel and Gatica-Perez [12] use features from video blogs such as speaking time, speaking speed, how much the person looks at the camera.”
BackgroundMining Crowdsourced First Impressions in Online Social Video
Biel, J.-I., & Gática-Pérez, D. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2346471 · Cited by 16 · see citing works
“We use a dataset consisting of 442 YouTube vlogs that was previously used in [8].”
InfluentialSelf-citationPredicting Learning and Engagement in Tutorial Dialogue: A Personality-Based Model
Vail, A., Grafsgaard, J. F., Wiggins, J. B., Lester, J. C., & Boyer, K. (2014) · International Conference on Multimodal Interaction · doi:10.1145/2663204.2663276 · Cited by 15 · see citing works
“In an expanded follow-up survey, the Extraversion trait was found to be most accurately predicted by facial indicators of emotion among all five traits [4].”
BackgroundThe Impact of Affective Verbal Content on Predicting Personality Impressions in YouTube Videos
Gievska, S., & Koroveshovski, K. (2014) · doi:10.1145/2659522.2659529 · Cited by 15 · see citing works
“…advances in intelligent technologies employed on the massive quantities of information generated through new means of communication raise high hopes for the potential of social computing for predictive personality modeling (Bachrach et al. 2013; Biel et al. 2013; Markovikj et al., 2013).”
BackgroundMethodInfluentialCrowdsourcing Thousands of Specialized Labels: A Bayesian Active Training Approach
Servajean, M., Joly, A., Shasha, D. E., Champ, J., & Pacitti, E. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2653763 · Cited by 15 · see citing works
“For instance, with Amazon Mechanical Turk, each image is presented to a set of users whose task is to provide relevant tags [3], [11].”
Multimodal Mixture Density Boosting Network for Personality Mining
Vo, N. N. Y., Liu, S., He, X.-Z. '., & Xu, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93034-3_51 · Cited by 15 · see citing works
On Text-based Personality Computing: Challenges and Future Directions
Fang, Q., Giachanou, A., Bagheri, A., Boeschoten, L., van Kesteren, E., Kamalabad, M. S., & Oberski, D. L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.691 · Cited by 14 · see citing works
Facing Employers and Customers
Muralidhar, S., Siegfried, R., Odobez, J., & Gática-Pérez, D. (2018) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3282894.3282925 · Cited by 14 · see citing works
Automatic Assessment of Human Personality Traits: A Step Towards Intelligent Human-Robot Interaction
Zafar, Z., Paplu, S. H., & Berns, K. (2018) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2018.8624975 · Cited by 14 · see citing works
“Interestingly, studies have shown that the outcomes of both measures are quite similar [13].”
BackgroundStappen, L., Baird, A., Lienhart, M., Bätz, A., & Schuller, B. W. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.773154 · Cited by 22 · see citing works
“, 2013; Tan and Zhang, 2019), and focusing on both long (Biel and Gatica-Perez, 2013) and short form video sharing (Cheng et al.”
Background
- 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 · 2012: 1
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Sociology and Political Science › 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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