The Effects of User Engagements for User and Company Generated Videos on Music Sales: Empirical Evidence From YouTube

Does platform type matter? A semantic analysis of user attitude formation on online platforms

An online platform is a setting where users may express their attitude in text or visual content. The doctrine thinking in consumer psychology is that greater perceived product value (e.g., more product features or lower price) gives more positive consumer attitude. Because of different types of platforms, however, online users might form their product/brand attitudes in different ways. We gathered 7,264 lines of online reviews about two famous brands on two types of social media platforms: online text-based forums and live-streaming platforms. The data were collected through a web crawler, and semantic analysis was employed to process the data before hypothesis testing. The findings of this study indicate that users’ perception of product features, price levels and brand culture significantly influence user attitude. The more product characteristics communicated on online platforms, the more difficult to formulate a positive user attitude, and users tend to have more positive attitude with higher perceived price. Compared with traditional text-based platforms, contents in live-streaming platforms (e.g., Tik Tok) with less product features, wider culture distance and lower perceived price are favored among users.

An Investigation Into the Effects of Destination Sensory Experiences at Visitors’ Digital Engagement: Empirical Evidence From Sanya, China

This study investigates the mechanism of how sensory experiences influence visitors’ digital engagement with a destination through establishing a strong bond and identification between a destination and tourist utilizing a two-step process. First, visitors’ sensory experiences in a destination are identified through a content analysis of online review comments posted by visitors. Afterward, the effects of those sensory experiences on visitors’ digital engagement through destination dependence and identification with that destination are examined. Findings suggest that sensory experiences are critical antecedents of visitors’ bond and identification with a destination. Visitors’ positive destination-related sensory experiences increase their dependence on and identification with the destination, and this dependence and identification positively influence their digital engagement behavior on social media.

Music video emotion classification using slow-fast audio-video network and unsupervised feature representation

Affective computing has suffered by the precise annotation because the emotions are highly subjective and vague. The music video emotion is complex due to the diverse textual, acoustic, and visual information which can take the form of lyrics, singer voice, sounds from the different instruments, and visual representations. This can be one reason why there has been a limited study in this domain and no standard dataset has been produced before now. In this study, we proposed an unsupervised method for music video emotion analysis using music video contents on the Internet. We also produced a labelled dataset and compared the supervised and unsupervised methods for emotion classification. The music and video information are processed through a multimodal architecture with audio-video information exchange and boosting method. The general 2D and 3D convolution networks compared with the slow-fast network with filter and channel separable convolution in multimodal architecture. Several supervised and unsupervised networks were trained in an end-to-end manner and results were evaluated using various evaluation metrics. The proposed method used a large dataset for unsupervised emotion classification and interpreted the results quantitatively and qualitatively in the music video that had never been applied in the past. The result shows a large increment in classification score using unsupervised features and information sharing techniques on audio and video network. Our best classifier attained 77% accuracy, an f1-score of 0.77, and an area under the curve score of 0.94 with minimum computational cost.

Deep-Learning-Based Multimodal Emotion Classification for Music Videos

Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving efficient affective computing. This paper presents an affective computing system that relies on music, video, and facial expression cues, making it useful for emotional analysis. We applied the audio–video information exchange and boosting methods to regularize the training process and reduced the computational costs by using a separable convolution strategy. In sum, our empirical findings are as follows: (1) Multimodal representations efficiently capture all acoustic and visual emotional clues included in each music video, (2) the computational cost of each neural network is significantly reduced by factorizing the standard 2D/3D convolution into separate channels and spatiotemporal interactions, and (3) information-sharing methods incorporated into multimodal representations are helpful in guiding individual information flow and boosting overall performance. We tested our findings across several unimodal and multimodal networks against various evaluation metrics and visual analyzers. Our best classifier attained 74% accuracy, an f1-score of 0.73, and an area under the curve score of 0.926.

Understanding Transmedia Music on YouTube through Disney Storytelling

Transmedia storytelling has been integrated into contemporary society through social media, where influencers have enabled the building of worlds. Within this environment of human-interaction, fiction and converging social realities have become an essential tool to tell stories. On YouTube, storytelling has expanded to music, where cover videos take on great relevance. The aim of this study is to understand the transmedia music phenomenon due to the impact of music on the platform. To this end, we applied a methodology that stemmed from Grounded Theory principles in the analysis of 300 Disney animation song covers in three stages: (1) deductive and inductive codebook development; (2) social network analysis; and (3) statistical test. The results showed that youtubers highlight specific audiovisual codes from the film and cultural industries. Furthermore, we observed these productions often display configurations that expand the original story through performance, location, costumes, make-up, among others. We argue that, on the digital sphere, a sustainable transmedia music paradigm is developing, where performers construct more meaningful and valuable stories.