Tag Archives: social media

How Does AI-Generated Voice Affect Online Content Creation? Evidence from TikTok

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee (2022) How Does AI-Generated Voice Affect Online Content Creation? Evidence from TikTokWork-in-progress.

Video is one of the fastest-growing online services offered to consumers. A growing number of people today are participating in video creation and consumption in the digital economy. We study whether and how AI-generated voice affects users’ routine efforts and creative efforts in online video creation. Using a unique dataset of 2,617 creators and 273,244 videos collected from TikTok over a 25-week period, we first exploit deep learning models to detect the adoption of AI-generated voice from massive video data. Then we estimate its treatment effect on creators and viewers using a difference-in-differences model coupled with propensity score matching. We find that AI-generated voice increases creators’ routine effort and creative effort in the short term. While it has a long-lasting effect on improving the efficiency of video creation, AI-generated voice cannot consistently motivate creators to include more information in videos, and might even be detrimental to their creative effort in the long term. Our study provides the first empirical evidence on how AI tools reshape video content creation patterns on online platforms, which carries important managerial implications for individual creators, platforms, and policymakers in the digital economy.

Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach (MISQ 2020)

Shin, Donghyuk, Shu He, Gene Moo Lee, Andrew B. Whinston, Suleyman Cetintas, Kuang-Chih Lee (2020) “Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,” MIS Quarterly, 44(4), pp. 1459-1492. [SSRN]

  • Based on an industry collaboration with Yahoo! Research
  • The first MISQ methods article based on machine learning
  • Presented in WeB (Fort Worth, TX 2015), WITS (Dallas, TX 2015), UT Arlington (2016), Texas FreshAIR (San Antonio, TX 2016), SKKU (2016), Korea Univ. (2016), Hanyang (2016), Kyung Hee (2016), Chung-Ang (2016), Yonsei (2016), Seoul National Univ. (2016), Kyungpook National Univ. (2016), UKC (Dallas, TX 2016), UBC (2016), INFORMS CIST (Nashville, TN 2016), DSI (Austin, TX 2016), Univ. of North Texas (2017), Arizona State (2018), Simon Fraser (2019), Saarland (2021), Kyung Hee (2021), Tennessee Chattanooga (2021), Rochester (2021), KAIST (2021), Yonsei (2021), UBC (2022)

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.

Does Deceptive Marketing Pay? The Evolution of Consumer Sentiment Surrounding a Pseudo-Product-Harm Crisis (J. Business Ethics 2019)

Song, Reo, Ho Kim, Gene Moo Lee, and Sungha Jang (2019) Does Deceptive Marketing Pay? The Evolution of Consumer Sentiment Surrounding a Pseudo-Product-Harm CrisisJournal of Business Ethics, 158(3), pp. 743-761.

The slandering of a firm’s products by competing firms poses significant threats to the victim firm, with the resulting damage often being as harmful as that from product-harm crises. In contrast to a true product-harm crisis, however, this disparagement is based on a false claim or fake news; thus, we call it a pseudo-product-harm crisis. Using a pseudo-product-harm crisis event that involved two competing firms, this research examines how consumer sentiments about the two firms evolved in response to the crisis. Our analyses show that while both firms suffered, the damage to the offending firm (which spread fake news to cause the crisis) was more detrimental, in terms of advertising effectiveness and negative news publicity, than that to the victim firm (which suffered from the false claim). Our study indicates that, even apart from ethical concerns, the false claim about the victim firm was not an effective business strategy to increase the offending firm’s performance.