Huaxia Rui

Information or attention? An empirical study of user contribution on Twitter

Social broadcasting services, such as Twitter and YouTube, are revolutionizing the way we access information and publish our own content. What is the key innovation of such services? We argue that the key innovation of social broadcasting services is recognizing and connecting people’s need for information and attention. While the value of information is widely studied, the importance of attention is less well understood. We use a collection of nearly 3 million Twitter user profiles to study the cross-sectional characteristics of user behavior; we also monitor 521 active Twitter users over a period of 282 days to carry out time-series analyses and a panel data analysis of user behavior. The empirical results consistently suggest that people’s search for attention is an important motivation for them to contribute content on Twitter. This finding supports our conceptual view of social broadcasting services as innovative platforms connecting people’s need for information and attention. It also has important implications for practitioners in this booming field.

Designing a social-broadcasting-based business intelligence system

The rise of social media has fundamentally changed the way information is produced, disseminated, and consumed in the digital age, which has profound economic and business effects. Among many different types of social media, social broadcasting networks such as Twitter in the U.S. and “Weibo” in China are particularly interesting from a business perspective. In the case of Twitter, the huge amounts of real-time data with extremely rich text, along with valuable structural information, makes Twitter a great platform to build Business Intelligence (BI) systems. We propose a framework of social-broadcasting-based BI systems that utilizes real-time information extracted from these data with text mining techniques. To demonstrate this framework, we designed and implemented a Twitter-based BI system that forecasts movie box office revenues during the opening weekend and forecasts daily revenue after 4 weeks. We found that incorporating information from Twitter could reduce the Mean Absolute Percentage Error (MAPE) by 44% for the opening weekend and by 36% for total revenue. For daily revenue forecasting, including Twitter information into a baseline model could reduce forecasting errors by 17.5% on average. On the basis of these results, we conclude that social-broadcasting-based BI systems have great potential and should be explored by both researchers and practitioners.

Effects of Social Networks on Prediction Markets: Examination in a Controlled Experiment

This paper examines the effect of a social network on prediction markets using a controlled laboratory experiment that allows us to identify causal relationships between a social network and the performance of an individual participant, as well as the performance of the prediction market as a whole. Through a randomized experiment, we first confirm the theoretical predictions that participants with more social connections are less likely to invest in information acquisition from outside information sources but perform significantly better than other participants in prediction markets. We further show that when the cost of information acquisition is low, a social-network-embedded prediction market outperforms a non-networked prediction market. We also find strong support for peer effects in prediction accuracy among participants. These results have direct managerial implications for the business practice of prediction markets and are critical to understanding how to use social networks to improve the performance of prediction markets.

The Impact of Social Network Structures on Prediction Market Accuracy in the Presence of Insider Information

This paper examines the effects of social network structures on prediction market accuracy in the presence of insider information through a randomized laboratory experiment. In the experiment, insider information is operationalized as signals on the state of nature with high precision. Motivated by the literature on insider information in the context of financial markets, we test and confirm two characterizations of insider information in the context of prediction markets: abnormal performance and less diffusion. Experimental results suggest that a more balanced social network structure is crucial to the success of prediction markets, whereas network structures akin to star networks are ill suited to prediction markets. As compared with other network structures, insider information has less positive effects on prediction market accuracy in star networks. We also find that the bias of the public information has a larger negative effect on prediction market accuracy in star networks.

Whose and What Social Media Complaints Have Happier Resolutions? Evidence from Twitter

Abstract Many brands try to manage customer complaints on social media, helping their customers on a real-time basis. Inspired by this popular practice, in this study, we aim to understand whose and what complaints on social media are likely to have happier resolutions. We analyzed the complaint resolution experience of customers of a major U.S. airline, by exploiting a unique data set combining both customer–brand interactions on Twitter and how customers felt at the end of these interactions. We find that complaining customers who are more influential in online social networks are more likely to be satisfied. Customers who have previously complained to the brand on social media, and customers who complain about process-related rather than outcome-related issues are less likely to feel better in the end. To the best of our knowledge, this study is the first to identify the key factors that shape customer feelings toward their brand–customer interactions on social media. Our results provide practical guidance for successfully resolving customers’ complaints through the use of social media—an area that expects exponential growth in the coming decade.

When Cellular Capacity Meets WiFi Hotspots: A Smart Auction System for Mobile Data Offloading

The surge of social networking and video streaming on the go has led to the explosion of mobile data traffic. To minimize congestion costs for under-served demand (e.g., Dissatisfied customers, or churn), the cellular service provider is willing to pay WiFi hotspots to serve the demand that exceeds capacity. In the present study, we propose an optimal procurement mechanism with contingent contracts for cellular service providers to leverage the advantages of both cellular and WiFi resources. We show the procedure of computing the optimal procurement mechanism with a tight integration of economics and computational technology. Simulation results show that the proposed procurement mechanism significantly outperforms the standard Vickrey-Clarke-Groves (VCG) auction in terms of the cellular service providers expected payoff.

Customer Service on Social Media: The Effect of Customer Popularity and Sentiment on Airline Response

Many companies are now providing customer service through social media, helping and engaging their customers on a real-time basis. To study this increasingly popular practice, we examine how major airlines respond to customer comments on Twitter by exploiting a large data set containing all Twitter exchanges between customers and four major airlines from June 2013 to August 2014. We find that these airlines pay significantly more attention to Twitter users with more followers, suggesting that companies literarily discriminate customers based on their social influence. Moreover, our findings suggest that companies in the digital age are increasingly more sensitive to the need to answer both customer complaints and customer compliments.

Optimal Risk Sharing with Limited Liability

We solve the general problem of optimal risk sharing among a nite number of agents with limited liability. We show that the optimal allocation is characterized by endogenously determined ranks assigned to the participating agents and a hierarchical structure of risk sharing, where all agents take on risks only above the agent-speci fic thresholds determined by their ranks. When all agents have CARA utilities, linear risk sharing is optimal between two adjacent thresholds. We use our general characterization of optimal risk sharing with limited liability to solve the problem of optimal insurance design with multiple insurers. We show that the optimal thresholds, or deductibles, can be efficiently calculated through the fixed point of a contraction mapping. We then use this contraction mapping technique to derive a number of comparative statics results for optimal insurance design and its dependence on microeconomic characteristics.

Information Exchange in Prediction Markets: Do Social Networks Promote Forecast Efficiency?

This paper studies the effects of information transmission on wisdom of the crowd. We provide a game-theoretic framework to resolve the question: Do social networks promote the forecast efficiency in prediction markets? Our study shows that a social network is not a panacea in terms of improving forecast accuracy. The use of social networks could be detrimental to the forecast performance when the cost of information acquisition is high. We also study the effects of social networks on information acquisition in prediction markets. In the symmetric Bayes-Nash equilibrium, all participants use a threshold strategy, and the equilibrium information acquisition is decreasing in the number of participants friends and increasing in the network density. The aforementioned results are robust to two commonly used mechanisms of prediction markets: a forecast-report mechanism and a security-trading mechanism.

What Drives Successful Complaint Resolutions on Social Media?: Evidence from the Airline Industry

Several companies effectively manage customer complaints on social media today, interacting with their customers on a real time basis. To study this increasingly popular practice, we examine brands’ complaint resolution efforts on social media, by exploiting a unique dataset of complaint-based customer interactions on Twitter, with a major airline. We find that complaining customers with a higher number of followers are more likely to be satisfied about their social media interaction with the brand. Moreover, the customers having an outcome related complaint, rather than a process related complaint, and also the customers who do not experience handoffs during the conversation, are more likely to be satisfied about their complaining experience on social media. To the best of our knowledge, this study is the first to empirically investigate the potential drivers of successful complaint resolutions in the context of social media customer service.