Emotional expressions have been widely used in online news. Existing research on the perception of online news has primarily focused on the effect of contextual cues on readers’ reasoning and deliberation behavior; the role of discrete emotions such as anger and sadness, however, has been overlooked. This paper addresses this research gap by investigating the influence of angry and sad expressions in online news on readers’ perception of the news. Drawing on the emotions as social information (EASI) theory and the appraisal-tendency framework (ATF), we find that expressions of anger in online news decrease its believability. However, sad expressions do not trigger the same effect. A further test reveals that the effect of angry expressions can be explained by the readers’ perception of the author’s cognitive effort: readers perceive that expressions of anger in the headlines denote a lack of cognitive effort of the author in writing the news, which subsequently lowers the believability of the news. We also show that news believability has downstream implications and can impact various social media behaviors including reading, liking, commenting, and sharing. This research extends current knowledge of the cognitive appraisals and interpersonal effects of discrete emotions (i.e., anger, sadness) on online news. The results also offer practical implications for social media platforms, news aggregators, and regulators that need to manage digital content and control the spread of fake news.
- Ph.D. (Management Information Systems), The University of Arizona
- B.Sc. (Computer Science (Information Systems)), The University of Hong Kong
Michael Chau is a Professor in Innovation and Information Management in the HKU Business School at the University of Hong Kong. He served as the Warden of Lee Chi Hung Hall (2009-2021) and the Program Director/Coordinator of the BBA (Information Systems) program (2006-2009, 2012-2018). He is also an Honorary Fellow of the HKU-HKJC Centre for Suicide Research and Prevention. He received a Ph.D. degree in Management Information Systems from the University of Arizona and a B.Sc. degree in Computer Science (Information Systems) from the University of Hong Kong. His research interests include business analytics, artificial intelligence, web mining and social media, electronic commerce, fintech, smart health, security informatics, human-computer interaction, and IT in education.
He has published more than 150 articles in premier journals and conferences in information systems, computer science, and information science. He has received multiple international research awards and has been highly ranked in several research productivity studies.
Michael has been active in serving the research community. He is a member of the AIS College of Senior Scholars and the Program Co-chair of PACIS 2024 and ICIS 2013. He has served on the organization committee and program committee of many information systems and computer science conferences, as well as the editorial board of multiple journals. He is a founding co-chair of the Pacific-Asia Workshop on Intelligence and Security Informatics (PAISI 2006-2019).
Michael has taught a wide range of courses at HKU at both the undergraduate and postgraduate levels, including database management, computer networking, business analytics and big data, artificial intelligence, project management, spreadsheet modelling, and computer programming.
- Business analytics and big data
- Artificial intelligence
- Web mining and social media
- Electronic commerce
- Smart health
- Security informatics
- Human-computer interaction
- IT in education
- Xu, J. J., Chen, D., Chau, M., Li, L., and Zheng, H. “Peer-to-Peer Loan Fraud Detection: Constructing Features from Transaction Data,” MIS Quarterly (MISQ), accepted for publication, forthcoming.
- Deng, B. and Chau, M. “The Effect of the Expressed Anger and Sadness on Online News Believability,” Journal of Management Information Systems (JMIS), 38(4), pp. 959-988, 2021.
- Chau, M., Li, W., Yang, B., Lee, A., and Bao, Z. “Incorporating the Time-Order Effect of Feedback in Online Auction Markets through a Bayesian Updating Model,” MIS Quarterly (MISQ), 45(2), pp. 985-1006, 2021.
- Chau, M., Li, T. M. H., Wong, P. W. C., Xu, J. J., Yip, P. S. F., and Chen, H. “Finding People with Emotional Distress in Online Social Media: A Design Combining Machine Learning and Rule-based Classification,” MIS Quarterly (MISQ), 44(2), pp. 933-955, 2020.
- Xu, J. J. and Chau, M. “Cheap Talk? The Impact of Lender-Borrower Communication on P2P Lending Outcomes,” Journal of Management Information Systems (JMIS), 35(1), pp. 53-85, 2018.
- Xu, J. J., Chau, M., and Tan, B. “The Development of Social Capital in the Collaboration Network of Information Systems Scholars,” Journal of the Association for Information Systems (JAIS), 15(12), pp. 835-859, 2014.
- Fang, X., Hu, P. J., Chau, M., Hu, H., Yang, Z., and Sheng, O. R. L. “A Data-Driven Approach to Measure Web Site Navigability,” Journal of Management Information Systems (JMIS), 29(2), pp. 173-212, 2012.
- Chau, M. and Xu, J. “Business Intelligence in Blogs: Understanding Consumer Interactions and Communities,” MIS Quarterly (MISQ), 36(4), pp. 1189-1216, 2012.
- Chau, M. “Visualizing Web Search Results Using Glyphs: Design and Evaluation of a Flower Metaphor,” ACM Transactions on Management Information Systems (ACM TMIS), 2(1), pp. 1-27, 2011.
- Cheng, R., Chau, M., Garofalakis, M., and Yu, J. X. “Mining Large Uncertain and Probabilistic Databases,” IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 22(9), 1201-1202, 2010.
- Roussinov, D. and Chau, M. “Combining Information Seeking Services into a Meta Supply Chain of Facts,” Journal of the Association for Information Systems (JAIS), 9(3), 175-199, 2008.
- Xu, J., Wang, G., Li, J., and Chau, M. “Complex Problem Solving: Identity Matching Based on Social Contextual Information,” Journal of the Association for Information Systems (JAIS), 8(10), 525-545, 2007.
- Schroeder, J., Xu, J., Chen, H., and Chau, M. “Automated Criminal Link Analysis Based on Domain Knowledge,” Journal of the American Society for Information Science and Technology (JASIST), 58(6), 842-855, 2007.
- Chen, H., Chung, W., Xu. J., Wang, G., Qin, Y., and Chau, M. “Crime Data Mining: A General Framework and Some Examples,” IEEE Computer, 37(4), 50-56, 2004.
Dr. Chau’s research has appeared in more than 150 publications. Please refer to https://pweb.fbe.hku.hk/~mchau/publications.html for a complete list.
- INFORMS ISS Design Science Award (2020)
- IEEE ITSS Leadership Award in Intelligence and Security Informatics (2020)
- AIS Sandra Slaughter Service Award (2016)
- HKU Outstanding Young Researcher Award (2014)
- HKU Faculty Research Postgraduate Supervision Award (2020)
- HKU Faculty Knowledge Exchange Award (2013, 2016)
- Journal on Information Systems Education Best Paper Runner-up (2019)
- IEEE ISI Best Conference Paper Runner-up (2016)
- PACIS Best Conference Paper (2006)
- Keynote speaker/Invited speaker at more than 10 conferences and workshops
- Warden, Lee Chi Hung Hall (2009-2021)
- BBA(IS) Program Director/Coordinator (2006-2009, 2012-2018)
- Program Co-chair, Pacific-Asia Conference on Information Systems (PACIS 2024)
- Program Co-chair, International Conference on Information Systems (ICIS 2013)
- Program Co-chair, IEEE International Conference on Intelligence and Security Informatics (ISI 2019)
- Founding Co-chair, Pacific-Asia Workshop on Intelligence and Security Informatics (PAISI 2006-2019)
Online auction markets host a large number of transactions every day. The transaction data in auction markets are useful for understanding the buyers and sellers in the market. Previous research has shown that sellers with different levels of reputation, as shown by the ratings and comments left in feedback systems, enjoy different levels of price premiums for their transactions. Feedback scores and feedback texts have been shown to correlate with buyers’ level of trust in a seller and the price premium that buyers are willing to pay (Ba and Pavlou 2002; Pavlou and Dimoka 2006). However, existing models do not consider the time-order effect, which means that feedback posted more recently may be considered more important than feedback posted less recently. This paper addresses this shortcoming by (1) testing the existence of the time-order effect, and (2) proposing a Bayesian updating model to represent buyers’ perceived reputation considering the time-order effect and assessing how well it can explain the variation in buyers’ trust and price premiums. In order to validate the time-order effect and evaluate the proposed model, we conducted a user experiment and collected real-life transaction data from the eBay online auction market. Our results confirm the existence of the time-order effect and the proposed model explains the variation in price premiums better than the benchmark models. The contribution of this research is threefold. First, we verify the time-order effect in the feedback mechanism on price premiums in online markets. Second, we propose a model that provides better explanatory power for price premiums in online auction markets than existing models by incorporating the time-order effect. Third, we provide further evidence for trust building via textual feedback in online auction markets. The study advances the understanding of the feedback mechanism in online auction markets.
Many people face problems of emotional distress. Early detection of high-risk individuals is the key to prevent suicidal behavior. There is increasing evidence that the Internet and social media provide clues of people’s emotional distress. In particular, some people leave messages showing emotional distress or even suicide notes on the Internet. Identifying emotionally distressed people and examining their posts on the Internet are important steps for health and social work professionals to provide assistance, but the process is very time-consuming and ineffective if conducted manually using standard search engines. Following the design science approach, we present the design of a system called KAREN, which identifies individuals who blog about their emotional distress in the Chinese language, using a combination of machine learning classification and rule-based classification with rules obtained from experts. A controlled experiment and a user study were conducted to evaluate system performance in searching and analyzing blogs written by people who might be emotionally distressed. The results show that the proposed system achieved better classification performance than the benchmark methods and that professionals perceived the system to be more useful and effective for identifying bloggers with emotional distress than benchmark approaches.
It’s no secret that private bloggers tend to speak freely in their conversations about firms and their products. But how can firms tap into blogs and build their Business Intelligence?