Visual social media have emerged as an essential brand communication channel for advertisers and brands. The active use of hashtags has enabled advertisers to identify customers interested in their brands and better understand their consumers. However, some users post brand-incongruent content-for example, posts composed of brand-irrelevant images with brand-relevant hashtags. Such visual information mismatch can be problematic because it hinders other consumers' information search processes and advertisers' insight generation from consumer-initiated social media data. This study aims to characterize visually mismatched content in brand-related posts on Instagram and builds a visual information mismatch detection model using computer vision. We propose a machine-learning model based on three cues: image, text, and metadata. Our analysis shows the effectiveness of deep learning and the importance of combining text and image features for mismatch detection. We discuss the advantages of machine-learning methods as a novel research tool for advertising research and conclude with implications of our findings.Instagram is one of the fastest growing photo-and video-sharing social media platforms and has attracted more than 1 billion monthly users worldwide. In the United States, there were approximately 107.2 million Instagram users by 2018, and this number is expected to grow to 120.3 million by 2023 (Nuñez 2020). With its increasing popularity, advertisers and brands have paid attention to Instagram's potential as a brand communication channel in social media. For the term brand communication in social media, we follow the definition of Alhabash, Mundel, and Hussain (2017) and refer to it as brand-related communication distributed via social media that enables Internet users to access, share, engage with, add to, and co-create. This definition includes both brand-generated posts (e.g., advertisements) and user-generated content (UGC) (Voorveld 2019), but we particularly focus on consumer-generated brand communication messages in this study because we are interested in examining how consumers use images and texts when they create brand-related posts in Instagram. Consumers create brand-related posts by using an image of a product with a hashtag indicating the brand (e.g., #apple, #chanel) as a form of brand engagement and loyalty (Phua, Jin, and Kim 2017). For consumers, this combined use of brand-related images and hashtags is a way to find brand-related information and connect with other consumers who have similar tastes (Sung, Kim, and Choi 2018). For advertisers and brands, consumer-generated social media data help to better
This research proposes a new process of designing wearable computers, which combines interaction design methodology and actual stage costume design processes. The performing arts have achieved an extension of space and time on stage and the enhancement in expressivity by introducing a new technology to theater, resulting in the strengthened "liveness" of performance. Performers, considered as the primary medium of performance communication by showing their characters, lively on stage, are the most important factor in achieving "presence", which is the key aesthetic concept in performing arts. From this perspective, liveness is re-mediated and strengthened by the performer's capabilities of expression, and wearable computer technology can further extend the performer's expression, thereby creating a new media effect on stage. However, literature on performing arts lacks an adequate study of the design processes of wearable computers to help actual performers understand them. This study provides artists an understanding of this process and presents a new method of design that integrates interaction design and stage costume design. This new process is applied to the design and construction process of costumes using wearable computer technology in a live performance work,. Through this case study, artists can understand the concept of wearable computer technology more easily and potentially engage with wearable computers with a deeper understanding.
The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of 24,752 labeled images on fashion conversations, containing visual and textual cues, available for the research community.
The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of 24,752 labeled images on fashion conversations, containing visual and textual cues, available for the research community.Social media has become an important platform for the fashion industry for testing new marketing strategies and monitoring trends (Kim and Ko 2012). Already thousands of luxury and high street fashion brands around the world are present online and communicate with their followers and potential customers (Hu et al. 2014). While fashion brands have unilaterally set their polished brand images through traditional media such as television channels and magazines, two unique properties of social media serve as a very powerful tool for promoting and sharing fashion information to both industry and people.Firstly, the interactive nature of social media allows anyone to generate content and participate in establishing brand images. Not only large fashion houses launch advertising campaigns and share their latest runway looks through social media platforms, individuals and local stores also contribute
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