Modelling the influence of personality and culture on affect and enjoyment in multimedia

Guntuku, Sharath Chandra; Wang, Lin; Scott, Michael James; Ghinea, Gheorghiţă

doi:10.1109/acii.2015.7344577

Cited by 27 publications

(11 citation statements)

References 35 publications

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“…However, it is of great interest to start analyzing the effect of personality and culture on the perception of video content quality and affect. First results confirm that personality and culture play an important role and have to be taken into account in future computational models [104], [105]. Bigger affective datasets, composed of self-assessments across highly diverse viewer groups with different cultural and socio-demographical backgrounds, are needed to generalize and confirm these findings.…”

Section: Putational Modelsmentioning

confidence: 66%

Affective Video Content Analysis: A Multidisciplinary Insight

Baveye

Chamaret

Dellandréa

et al. 2018

IEEE Trans. Affective Comput.

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International audienceIn our present society, the cinema has become one of the major forms of entertainment providing unlimited contexts of emotion elicitation for the emotional needs of human beings. Since emotions are universal and shape all aspects of our interpersonal and intellectual experience, they have proved to be a highly multidisciplinary research field, ranging from psychology, sociology, neuroscience, etc., to computer science. However, affective multimedia content analysis work from the computer science community benefits but little from the progress achieved in other research fields. In this paper, a multidisciplinary state-of-the-art for affective movie content analysis is given, in order to promote and encourage exchanges between researchers from a very wide range of fields. In contrast to other state-of-the-art papers on affective video content analysis, this work confronts the ideas and models of psychology, sociology, neuroscience, and computer science. The concepts of aesthetic emotions and emotion induction, as well as the different representations of emotions are introduced, based on psychological and sociological theories. Previous global and continuous affective video content analysis work, including video emotion recognition and violence detection, are also presented in order to point out the limitations of affective video content analysis work

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Section: Putational Modelsmentioning

confidence: 66%

Affective Video Content Analysis: A Multidisciplinary Insight

Baveye

Chamaret

Dellandréa

et al. 2018

IEEE Trans. Affective Comput.

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show abstract

“…In the literature, to the authors' best knowledge, only few studies propose QoE models for video streaming in VANET. This pushes us to extend the studied models designed for Wireless Networks, Mobile Networks and MANETs to include specificities of VANETs, which is itself a sub class of MANETs [58] and can be formed in pure cellular or mobile network [59].…”

Section: Qoe Models For Video Streaming Over Vanetmentioning

confidence: 99%

“…We present some QoE models for video streaming over Wireless Networks, Mobile Networks and MANETs, which could be extended to VANETs by considering their characteristics such as high speed of vehicles on predefined roads that makes the topology highly dynamic. The highly dynamic topology generates frequent network disconnection [58].…”

Section: Qoe Models For Video Streaming Over Wireless Network Mobilementioning

confidence: 99%

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

2019

CIT. J. comput. inf. technol

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Vehicular Ad-hoc Networks (VANETs) are a new emerging technology which has attracted enormous interest over the last few years. It enables vehicles to communicate with each other and with roadside infrastructures for many applications. One of the promising applications is multimedia services for traffic safety or infotainment. The video service requires a good quality to satisfy the end-user known as the Quality of Experience (QoE). Several models have been suggested in the literature to measure or predict this metric. In this paper, we present an overview of interesting researches, which propose QoE models for video streaming over VANETs. The limits and deficiencies of these models are identified, which shed light on the challenges and real problems to overcome in the future.

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“…Extraversion is a personality dimension that includes facets such as gregariousness, seeking for excitement, and positive affect, and it has been related to the experience of both positive [21] and ambivalent affects with movies [17]. Regarding age, there has been inconsistent results, except that older participants tend to report more gratification from movies that provide contemplative experiences [1].…”

Section: Introductionmentioning

confidence: 99%

Why Do We Watch? The Role of Emotion Gratifications and Individual Differences in Predicting Rewatchability and Movie Recommendation

Arriaga

Alexandre

Postolache

et al. 2019

Behavioral Sciences

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Background: The present study’s main aim was to determine the predictors of movie rewatchability and recommendations. Methods: Using a sample of 318 participants, we first tested the structure of a gratification scale from watching a movie. Then, we examined the role of age, need for cognition, need for affect, extraversion, and emotional gratifications, in predicting individuals’ interest in rewatching the movie and in making recommendations. Results: As in the original proposal of the emotional gratification scale, the following dimensions were identified: fun, thrill, empathic sadness, release of emotions, social sharing, contemplative experiences, and character engagement, with acceptable model fit and reliability, convergent and divergent validity. Social sharing, contemplate experiences, need for affect and age were significant predictors of movie recommendation; whereas social sharing, thrill, extraversion, and age contributed most to explaining rewatching interest. Conclusion: This study highlights the importance of considering distinct gratifications and individual differences in predicting rewatching and movie recommendation.

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Modelling the influence of personality and culture on affect and enjoyment in multimedia

Cited by 27 publications

References 35 publications

Affective Video Content Analysis: A Multidisciplinary Insight

Affective Video Content Analysis: A Multidisciplinary Insight

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

Why Do We Watch? The Role of Emotion Gratifications and Individual Differences in Predicting Rewatchability and Movie Recommendation

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