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Sentiment Manipulation in Online Platforms: An Analysis of Movie Tweets

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  • Shun†Yang Lee
  • Liangfei Qiu
  • Andrew Whinston

Abstract

Online platforms are prone to abuse and manipulations from strategic parties. For example, social media and review websites suffer from sentiment manipulations, manifested in the form of opinion spam and fake reviews. The consequence of such manipulations is the deterioration of information quality as well as loss in consumer welfare. We study one of movie studios' operation activities, sentiment manipulation, in the context of movie tweets. Using the movie release and movie studios' earning announcement dates as sources of exogenous shocks, we find that both the average Twitter sentiment and the proportion of highly positive tweets exhibit a significant drop on the movie's release day or movie studios' earnings announcement day. In addition, independent productions and low budget movies tend to experience a larger drop than major studio productions and high budget movies. To examine the effect of competition on firm manipulation, we construct a movie competition measure based on both the time and theme dimensions through topic modeling, and we find that a higher level of competition leads to a larger drop in Twitter sentiment. Overall, these observations suggest that firms might be actively managing online sentiment in a strategic manner. Our study sheds light on the reliability of sentiment analysis and contributes to our understanding of potential strategic manipulation in the operation of movie studios.

Suggested Citation

  • Shun†Yang Lee & Liangfei Qiu & Andrew Whinston, 2018. "Sentiment Manipulation in Online Platforms: An Analysis of Movie Tweets," Production and Operations Management, Production and Operations Management Society, vol. 27(3), pages 393-416, March.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:3:p:393-416
    DOI: 10.1111/poms.12805
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    Cited by:

    1. Sulin Ba & Yuan Jin & Xinxin Li & Xianghua Lu, 2020. "One Size Fits All? The Differential Impact of Online Reviews and Coupons," Production and Operations Management, Production and Operations Management Society, vol. 29(10), pages 2403-2424, October.
    2. Jiang, Meiling & Gao, Qingwu & Zhuang, Jun, 2021. "Reciprocal spreading and debunking processes of online misinformation: A new rumor spreading–debunking model with a case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Liangfei Qiu & Yili (Kevin) Hong & Andrew Whinston, 2022. "Special Issue of Production and Operations Management “Social Technologies in Operations”," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 868-869, February.
    4. Xunyi Wang & Meiling Jiang & Wencui Han & Liangfei Qiu, 2022. "Do Emotions Sell? The Impact of Emotional Expressions on Sales in the Space‐Sharing Economy," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 65-82, January.
    5. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.
    6. Agarwal, Puneet & Aziz, Ridwan Al & Zhuang, Jun, 2022. "Interplay of rumor propagation and clarification on social media during crisis events - A game-theoretic approach," European Journal of Operational Research, Elsevier, vol. 298(2), pages 714-733.
    7. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    8. Petrescu, Maria & Ajjan, Haya & Harrison, Dana L., 2023. "Man vs machine – Detecting deception in online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    9. Ka Chung Ng & Ping Fan Ke & Mike K. P. So & Kar Yan Tam, 2023. "Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2101-2122, July.
    10. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    11. Ronny Behrens & Natasha Zhang Foutz & Michael Franklin & Jannis Funk & Fernanda Gutierrez-Navratil & Julian Hofmann & Ulrike Leibfried, 2021. "Leveraging analytics to produce compelling and profitable film content," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 171-211, June.
    12. Zaiyan Wei & Mo Xiao & Rong Rong, 2021. "Network Size and Content Generation on Social Media Platforms," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1406-1426, May.
    13. Liangfei Qiu & Subodha Kumar & Arun Sen & Atish P. Sinha, 2022. "Impact of the Hospital Readmission Reduction Program on hospital readmission and mortality: An economic analysis," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2341-2360, May.
    14. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    15. Zibo Liu & Zhijie Lin & Ying Zhang & Yong Tan, 2022. "The Signaling Effect of Sampling Size in Physical Goods Sampling Via Online Channels," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 529-546, February.
    16. Tianshi Li & Wenli Li & Yuqing Zhao & Jingpei Ma, 2023. "Rationality manipulation during consumer decision-making process: an analysis of Alibaba’s online shopping carnival," Electronic Commerce Research, Springer, vol. 23(1), pages 331-364, March.
    17. Marios Kokkodis & Theodoros Lappas & Gerald C. Kane, 2022. "Optional purchase verification in e‐commerce platforms: More representative product ratings and higher quality reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2943-2961, July.
    18. Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.
    19. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    20. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    21. Liu, Chun-Yi & Oliveira, Fernando S. & You, Tian-Hui, 2024. "The impact of platform audits on the manipulation of online reviews," International Journal of Production Economics, Elsevier, vol. 275(C).

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