دانلود مقاله اینفلوئنسر مجازی ایجاد شده با هوش مصنوعی
خلاصه
1. معرفی
2. بررسی ادبیات
3. روش شناسی
4. نتایج
5. بحث
6. نتیجه گیری
اعلامیه منافع رقابتی
سپاسگزاریها
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. Literature review
3. Methodology
4. Results
5. Discussion
6. Conclusion
Declaration of Competing interest
Acknowledgements
Data availability
References
چکیده
این مطالعه با تمرکز بر کاربرد هوش مصنوعی، تأثیر نمایش احساسی بر تعامل کاربر با تصاویر اینفلوئنسر تولید شده توسط رایانه را از طریق چارچوب رایانه‌های بازیگران اجتماعی (CASA) بررسی می‌کند. احساسات را به حرکات ماهیچه ای منفرد (به عنوان مثال، واحدهای کنش صورت) تجزیه می کند. با استفاده از تشخیص چهره بر اساس 1028 تصویر به اشتراک گذاشته شده توسط لیل میکولا، یافته ها اهمیت شادی، غم، انزجار و شگفتی را در ایجاد مشارکت کاربر در هنگام تبلیغ محصولات متنوع با محتوای بصری جذاب نشان می دهد. این یافته‌ها اهمیت متعادل کردن شدت حرکت عضلات را برای ساده‌سازی تعامل بین فناوری، رفتار انسانی و ارتباطات دیجیتالی نشان می‌دهد.
Abstract
Focusing on the application of artificial intelligence, this study investigates the impact of emotional display on user engagement with computer-generated imagery influencers through the lens of the computers are social actors (CASA) framework. It breaks down emotions into individual muscle movements (i.e., facial action units). By using facial recognition based on 1,028 pictures shared by Lil Miquela, the findings disclose the significance of happiness, sadness, disgust, and surprise in triggering user engagement when promoting diverse products with visually captivating content. The findings highlight the importance of balancing the intensity of muscle movement to streamline the interplay between technology, human behaviour, and digital communication.
Introduction
The rapid advancement of artificial intelligence (AI) has enabled widespread digital transformation and given rise to avatars, content-generation AI, and computer-generated universes that promote unparalleled levels of social connectivity (Ahn et al., 2022; Miao et al., 2022). Content-generation AI systems such as ChatGPT and DALL·E2 have the capability to generate textual and pictorial content, allowing for the creation of immersive and engaging content across various industries. This revolutionization enables more immersive interactions with consumers in various industries, such as entertainment (Kim and Yoo, 2021) and retailing (Chuah and Yu, 2021). Through far-reaching and impactful non-human digital communications that simulate a more realistic experience, this transformative technology results in enhanced customer engagement (Rahman et al., 2023). Although anthropomorphic characters have been met with criticism over uneasiness and eeriness resulting from the uncanny valley effect (Lou et al., 2022; Mori, 1970), the increasing sophistication of computer graphics has begun to shift public attitudes towards humanlike characters, demonstrating that viewers may not always respond negatively. For instance, computer-generated imagery (CGI) influencers, such as Lil Miquela and Imma, are a remarkable innovation that leverages the power of AI to create digital personas that look and behave like real humans, thus pushing the boundaries of what is possible in the realm of virtual media (Drenten and Brooks, 2020).
In addition to their curated online presence, CGI influencers have the potential to express emotions in a way that avatars cannot. The use of animation and rendering techniques allows CGI influencers to mimic the subtleties of human expressions (Ahn et al., 2022). CGI influencers can consistently convey a broad spectrum of emotions, which is a feat that may prove challenging for their human counterparts to maintain. Adding on to the six basic emotions (i.e., happiness, sadness, surprise, fear, anger, and disgust) identified by Ekman (1992), a more sophisticated approach for assessing facial expressions is the examination of facial muscle movements through action units (AUs) (Ngan and Yu, 2019). Prior research has demonstrated the utility of AUs in enabling an objective deconstruction of potential facial muscle activations that lead to specific emotional expressions (Schoner-Schatz et al., 2021), such as in the context of service encounters (Ngan and Yu, 2019). This offers a more nuanced and detailed understanding of the emotions being exhibited by computer-generated imagery influencers and their impact on user engagement.
Conclusion
6.1. Theoretical contribution
By exploring how CGI influencers can effectively communicate emotions to their audience, this research provides valuable insights into the interplay between technology, human behaviour, and marketing strategies. As CGI influencers blur the line between reality and fiction, this study reinforces the theoretical lens of the CASA paradigm (Ahn et al., 2022; Nass et al., 1994) that is pivotal to the connections between the audience and media figures (Chen, 2016). Different from the traditional CASA paradigm that concentrates on the connection between humans and agents (Arsenyan and Mirowska, 2021; Gambino et al., 2020), the underpinning logic of CASA allows mutual communication at an emotional level for CGI influencers and the public (Mrad et al., 2022), laying a solid foundation for sentimental and experiential communication of virtual agents. By analysing their facial expressions, this study reinforces the assumption that optimising emotional experiences in computer-mediated communication serves as a fundamental factor in influencing viewers’ reactions in marketing (Chuah and Yu, 2021; Grundner and Neuhofer, 2021).
Besides the commonly studied positive emotions (e.g., happiness) (Baek et al., 2022; Campos et al., 2013), this research uncovers the effectiveness of specific emotions such as surprise, sadness, and disgust in distinct situational contexts. Specifically, it contributes to the understanding of AI surprise by examining the nuanced variations in different intensities of AUs (Chuah and Yu, 2021). Additionally, it reveals insights into less commonly observed expressions on social media, such as sadness and disgust. By analysing facial muscle movements, the research delves into the subtle changes within specific muscles that are associated with each emotion. Besides focusing solely on the overall intensity of emotions (Bharadwaj et al., 2022; Lin et al., 2021), this approach provides a deeper understanding of the intricacies involved in emotional expression.