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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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Zhang, H., Li, S., Zeng, D., Yan, C. C., & Ge, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29613 · Cited by 13
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Multiparty Visual Co-Occurrences for Estimating Personality Traits in Group Meetings
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On leveraging crowdsourced data for automatic perceived stress detection
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“The WCPR14 organizers provided the participants with a training (348 texts) and test set (56 texts) of transcribed video blogs [1].”
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Siddique, F. B., & Fung, P. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1379 · Cited by 10
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MethodCabrera-Quirós, L., Gedik, E., & Hung, H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2930605 · Cited by 9
Visual Nonverbal Behavior Analysis: The Path Forward
Akhtar, Z., & Falk, T. H. (2017) · IEEE Multimedia · doi:10.1109/mmul.2017.265091158 · Cited by 9
PCENet: Psychological Clues Exploration Network for Multimodal Personality Assessment
Zhu, Y., Wei, Y., Li, M., Zhang, T., Wei, S., & Wu, B. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615005 · Cited by 8
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BackgroundPsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait
Cosma, A., & Rǎdoi, E. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00454 · Cited by 8
“In this area, numerous datasets are proposed, from multimodal (MHHRI [12], ChaLearn [21], SEMAINE [49]) to audiovisual (ELEA [58], Youtube Vlog [5–7]).”
BackgroundMaya Codical Glyph Segmentation: A Crowdsourcing Approach
Can, G., Odobez, J., & Gática-Pérez, D. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2755985 · Cited by 8
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BackgroundToward Multimodal Modeling of Emotional Expressiveness
Lin, V., Girard, J. M., Sayette, M. A., & Morency, L. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418887 · Cited by 8
“vidual is above or below the median on a trait (although a more rigorous approach is to predict their dimensional scores [12, 65]). Previous models used acoustic [49, 54], linguistic [61], and visual [5] data, often in multimodal combinations. Traditional machine learning algorithms have been most common, especially s…”
BackgroundRetracted: A Survey on: Personality Prediction from Multimedia through Machine Learning
Singh, B. K., Katiyar, M., Gupta, S., & Ganpatrao, N. G. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418384 · Cited by 8
“The comment of the corpus that we utilized in this examination is based on the spectator report[26].”
MethodRetractedMeta-analysis of digital-based learning to improve learning outcomes
Ismiyati, I., Pramusinto, H., & Sholikah, M. (2022) · Psychology Evaluation and Technology in Educational Research · doi:10.33292/petier.v4i2.114 · Cited by 8
Personal-ITY: A Novel YouTube-based Corpus for Personality Prediction in Italian
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BackgroundPersonality Perception of Robot Avatar Teleoperators in Solo and Dyadic Tasks
Bremner, P., Çeliktutan, O., & Güneş, H. (2017) · Frontiers in Robotics and AI · doi:10.3389/frobt.2017.00016 · Cited by 8
“…al. Personality Perception of Robot Avatar Teleoperators Frontiers in Robotics and AI | www.frontiersin.org May 2017 | Volume 4 | Article 16 practicality as it enables collecting responses from a large group of people within a short period of time (Biel and Gatica-Perez, 2013; Salam et al., 2016).”
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Vijay, R. S., Shubham, K., Renier, L. A., Kleinlogel, E. P., Mast, M. S., & Jayagopi, D. B. (2021) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3461615.3485397 · Cited by 7
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BackgroundLingual markers for automating personality profiling: background and road ahead
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BackgroundThin slicing to predict viewer impressions of TED Talks
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BackgroundEditorial: IEEE Transactions on Affective Computing—Challenges and Chances
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Twenty Years of Personality Computing: Threats, Challenges and Future Directions
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Голос и психологические свойства человека: обзор современных исследований
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“We observe that the Extraversion and Agreeableness traits are the most predictable; this is consistent with other works using regression [4, 5, 9] and classification [1].”
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Emotional and Social Signals: A Neglected Frontier in Multimedia Computing?
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Blogging, craft culture, and women empowerment
Manosevitch, I., & Tzuk, Y. (2017) · Cogent Social Sciences · doi:10.1080/23311886.2017.1408753 · Cited by 6
“The increased use of vlogs (Biel & Gatica-Perez, 2013) raises another important direction for further inquiry, looking into differences in the nature and usage of blogs compared with vlogs for women empowerment.”
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“The original dataset, provided by Idiap research institute, first used by Biel and Perez in [29] is based on Vlogs”
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Thomas, C., Jayagopi, D. B., & Gática-Pérez, D. (2019) · ACM International Conference on Interactive Experiences for TV and Online Video · doi:10.1145/3317697.3323357 · Cited by 6
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Das, K. G., & Das, D. (2017) · International conference natural language processing · Cited by 6
Masumura, R., Orihashi, S., Ihori, M., Tanaka, T., Makishima, N., Suzuki, S., Mizuno, S., & Hojo, N. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i2.32136 · Cited by 7
Automatic Personality Prediction: A Systematic Mapping Study
Chraibi, K., Chaker, I., & Zahi, A. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308479 · Cited by 5
“The most used public corpus are MyPersonality [20], The Speaker Personality Corpus (SPC) [21], PAN CLEF 2015 Corpus [22], Essays [23], The YouTube Personality Dataset [24], Youtube "First Impressions Challenge" Dataset [25] and others as illustrated in Fig.”
MethodHow may I help you? behavior and impressions in hospitality service encounters
Muralidhar, S., Mast, M. S., & Gática-Pérez, D. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136771 · Cited by 5
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Incorporating Emotion and Personality-Based Analysis in User-Centered ModellingPreprint
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“To elicit diverse and realistic social inferences, we computationally sampled naturalistic videos from 10,000 social media clips 52 to derive a subset of 444 videos that feature target individuals with diverse demographical backgrounds, nonverbal information, and narrative topics.”
InfluentialLi, J., He, Y., Xu, J., Luo, T., Hu, Z., Hong, R., & Wang, M. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3762017 · Cited by 9
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An Open Dataset for Impression Recognition from Multimodal Bodily Responses
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“These nonverbal cues include prosodic cues (pitch, energy, voice loudness modulation, spectral entropy), speaking activity features (speaking time, speaking turns, pauses, short utterances), visual motion (WMEI [2]) and head nods.”
BackgroundVideo Annotation by Cascading Microtasks
de Amorim, M. N., Segundo, R. M. C., Santos, C. A. S., & Tavares, O. D. L. (2017) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/3126858.3126897 · Cited by 4
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BackgroundCONTEMPORARY TRENDS OF VLOGGING IN PAKISTAN: A CONTENT ANALYSIS OF POPULAR VLOGS
Ilyas, K. (2021) · ILMA Journal of Social Sciences & Economics · doi:10.46745/ilma.ijsse.2021.01.01.05 · Cited by 4
Assessing Personality Traits and Interview Performance from Asynchronous Video Interviews
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A personality-labeled semantic dataset from facial expressions, gaze, and head movement cues
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Digitale Video- & Spiel-Assessments: Psychometrie und Maschinelles Lernen
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Genuine Personality Recognition from Highly Constrained Face Images
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MPRNet: A Temporal-Aware Cross-Modal Encoding Framework for Personality Recognition
Li, J., Zhang, H., Chen, J., Gong, J., Wang, Y., Wang, S., & Zhao, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3601134 · Cited by 2
“It plays a critical role in understanding emotional behavior, interaction dynamics, and individual differences in affective computing systems [2], [3].”
Investigating the Structure of Emotion: Tools, Pitfalls and Recommendations
Lin, C., Han, Y., Keleş, Ü., Xu, Y., Lu, J., Zheng, R., & Adolphs, R. (2025) · Affective Science · doi:10.1007/s42761-025-00337-6 · Cited by 3
“While most databases only include stimuli generated in laboratory conditions (e.g., posed photos of participants), a growing number include stimuli that are more naturalistic (Bainbridge et al., 2013; Biel & Gatica-Perez, 2013; Liu et al., 2015).”
Automatic Personality Perception
Camastra, F., & Vinciarelli, A. (2015) · Advanced information and knowledge processing · doi:10.1007/978-1-4471-6735-8_16 · Cited by 2
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“(2012)[5] believed that Brand image refers to the all-related imagination of the brand in consumers’ minds when the brand name is mentioned.”
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Muralidhar, S., Mast, M. S., & Gática-Pérez, D. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264939 · Cited by 3
“This feature is computed by a modified version of motion energy images, called Weighted Motion Energy Images (WMEI) [9]. a Head Nods & Channeling: Head nods were extracted using a 3D face centered method [18].”
MethodPrediction of instantaneous likeability of advertisements using deep learning
Saha, D., Rahman, S. M. M., Islam, M. T., Ahmad, M. O., & Swamy, M. N. S. (2021) · Cognitive Computation and Systems · doi:10.1049/ccs2.12022 · Cited by 2
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BackgroundUnderstanding and Engaging YouTube Communities
Savage, K. (2015) · DigitalGeorgetown (Georgetown University Library) · Cited by 2
Crowdsourcing: jóvenes prosumidores en los medios de comunicación de Colombia.
Monsalve, W. D. G. (2023) · Razón y Palabra · doi:10.26807/rp.v26i115.1930 · Cited by 1
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Cabrera-Quirós, L. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2971359 · Cited by 1
“Parallel efforts in other scenarios, such as meetings [16, 10] or vlogs [3], have fused information coming from other modalities (eg. audio or wearable sensors) with video, improving considerably the performance of unimodal approaches (referring to a single sensor type) in classification/regression…”
BackgroundCan a machine pass a situational judgment test measuring personality perception?
Chen, L., Zaromb, F. M., Yang, Z., Leong, C. W., & Martín‐Raugh, M. P. (2017) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2017.8272615 · Cited by 1
“[4] selected 281 video blog videos for rating personality traits.”
BackgroundUses and Gratifications of Watching YouTube Beauty Vlogs
Lantigua, K. R. (2019) · Cited by 1
Bhin, H., Lim, Y., & Choi, J. (2024) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur61395.2024.10597440 · Cited by 1
“Preparation process for data acquisition perceived based on static facial images [16].”
BackgroundAn Iterative Approach to Build a Semantic Dataset for Facial Expression of Personality
Kodavati, S. N. S., Kanade, A., Mei, W. A. H., & Durupınar, F. (2024) · Motion in Games · doi:10.1145/3677388.3696333 · Cited by 1
“The dataset was initially created in 2011 [Biel et al. 2011] and thoroughly analyzed in 2013 [Biel and Gatica-Perez 2013].”
Rosewarne, L. (2017) · Springer eBooks · doi:10.1007/978-981-10-2779-6_140-1 · Cited by 1
Character-IoT (CIoT): Toward Human-Centered Ubiquitous Computing
Mougy, A. E. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-15954-2_7 · Cited by 1
Inferring Neuroticism of Twitter Users by Utilizing their Following Interests
Cornelisse, J. (2020) · Cited by 1
Multimodal Dyadic Impression Recognition via Listener Adaptive Cross-Domain Fusion
Li, Y., Bell, P., & Lai, C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095072 · Cited by 1
“Social research has pointed out the importance of impressions in human interactions, where it is natural to perceive impressions from the partners via non-verbal behaviors such as eye gaze, body pose, speaking activity, and prosody variation [2].”
Component-Wise Boosting of Targets for Multi-Output PredictionPreprint
Au, Q., Schalk, D., Casalicchio, G., Schoedel, R., Stachl, C., & Bischl, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03943 · Cited by 2
“The mixedtype datasets are both personality prediction datasets [2, 22].”
BackgroundBassignana, E., Nissim, M., & Patti, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07009 · Cited by 1
“For the 2014 edition, the data consisted of the “YouTube Personality Dataset” (Biel and Gatica-Perez, 2013), which contains a collection of behavioural features, speech transcriptions, and personality impression scores for a set of 404 YouTube vloggers, and “Mobile Phones”, which is a”
MethodGiertz, J. N. (2022) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-9352 · Cited by 1
Oviedo, M. J. (2013) · doi:10.17077/etd.6zow3juv
Deriving Psychodynamics Among Consumers Through Social Media
Moctezuma, N. P. B. (2017) · Advances in business strategy and competitive advantage book series · doi:10.4018/978-1-5225-4831-7.ch019
“…consumer behaviour through social media, there is paucity of research concerning psychodynamics among consumers derived by online social networks since existing research has focused on analysing user’s profiles and text blogs mainly in developed countries (Biel and Gatica-Pérez, 2013).”
BackgroundAutomatic Assessment of Personality Traits Using Non-verbal Cues in a Saudi Sample
Alghowinem, S., & AlKadhi, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92141-9_22
Modelling and Analysing Behaviours and Emotions via Complex User InteractionsPreprint
Mostafa, M. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07683
“Research in an image or video analysis includes promising studies on YouTube videos for classification of specific behaviours and indicators of personality traits [27].”
BackgroundThe validity of cues in personality judgment
Wu, T., & Zheng, Y. (2019) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2019.00533
An In-Depth Analysis of Personality Prediction
Despotovski, F., & Gievska, S. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-33110-8_12
User Profiling Using Hinge-loss Markov Random FieldsPreprint
Farnadi, G., Getoor, L. C., Moens, M., & Cock, M. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01177
“In [2], Biel and Gatica-Perez focus on predicting personality of Vloggers (YouTube bloggers) based on their visual and audio content.”
Distinguishing Engagement Facets: An Essential Component for AI-based HealthcarePreprint
Salam, H. (2021) · arXiv (Cornell University)
DIPS: A Dyadic Impression Prediction System for Group Interaction Videos
Bai, C., Bolonkin, M., Regunath, V., & Subrahmanian, V. S. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3532865
“Kindiroglu et al. [31] used multi-task and transfer learning for extraversion and leadership prediction on the ELEA and VLOG datasets with the same set of high-level audio-visual features.”
BackgroundRosewarne, L. (2019) · Springer eBooks · doi:10.1007/978-981-10-5251-4_140
Profissão youtuber: consequências sociais e precarização do trabalho em comunicação social
Sammur, J. T. E. D., Cortêz, P. A., & Paganotti, I. (2022) · Novos Olhares · doi:10.11606/issn.2238-7714.no.2022.193511
Loe, B. S., Abrahams, M., & Riley, P. (2018) · Assessment and Development Matters · doi:10.53841/bpsadm.2018.10.2.29
Reducing Gender and Ethnicity Bias In Personality Trait Prediction
Singh, S., Shah, Y., Vajani, Y., & Godbole, A. (2024) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci59607.2024.10497190
Maswanganyi, F. T. (2024) · Journal of Public Administration and Development Alternatives · doi:10.55190/jpada.2024.314
Augmented Language Dataset for Enhanced Personality Profiling
Teli, M. A., & Chachoo, M. A. (2025) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.16.1.7
“We considered publicly available datasets such as Essays [16] based on formally written student Essays, YouTube Vlogs [41] based on YouTube Video Blog Transliterations, MyPersonality [42] based on Facebook statuses and PAN-AP-15 [24] based on tweets, ensuring they are sufficiently annotated with…”
Bias-Aware Method for Combining Audio and Video Data in Personality Assessment AI Models
Kandage, M. P., & Lakmal, H. K. I. S. (2025) · SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) · doi:10.1109/slaai-icai68534.2025.11318477
“The Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) are all crowdsourced assessments and each of the clips has the ground truth label of these five personality traits represented by a value on the [0,1] scale [15],[16].”
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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