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We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model\u2019s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.<\/jats:p>","DOI":"10.25300\/misq\/2020\/14870","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T15:28:39Z","timestamp":1610638119000},"page":"1459-1492","source":"Crossref","is-referenced-by-count":205,"title":["Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach"],"prefix":"10.25300","volume":"44","author":[{"given":"Donghyuk","family":"Shin","sequence":"first","affiliation":[{"name":"W. P. 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