文化分析
社会化媒体
数据科学
视觉分析
社交媒体分析
计算机科学
分析
大数据
数据分析
社会网络分析
万维网
人机交互
可视化
人工智能
语义分析
互联网
数据挖掘
Web建模
作者
Donghyuk Shin,Shu He,Gene Moo Lee,Andrew B. Whinston,Suleyman Cetintas,Kuang-Chih Lee
标识
DOI:10.25300/misq/2020/14870
摘要
This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. 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’s 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.
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