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Automatically Detecting Image–Text Mismatch on Instagram with Deep Learning

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Title
Automatically Detecting Image–Text Mismatch on Instagram with Deep Learning
Author(s)
Ha, Yui; Park, Kunwoo; Kim, Su Jung; Joo, Jungseock; Meeyoung Cha
Publication Date
2021-01
Journal
Journal of Advertising, v.50, no.1, pp.52 - 62
Publisher
Routledge
Abstract
© 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.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.
URI
https://pr.ibs.re.kr/handle/8788114/10011
DOI
10.1080/00913367.2020.1843091
ISSN
0091-3367
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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