Link Formation on Twitter: The Role of Achieved Status and Value Homophily

Homophily has been a widely recognized dominant factor in offline social network connection, which refers to one’s propensity to seek interactions with others of similar status or values. Existing studies regarding homophily factors have been limited mostly to offline sociodemographic characteristics, such as race, gender, religion, education and occupation, which may not necessarily manifest homophily in online social network. Some researchers dabble in online social network, but they extract homophily characteristics from static user profile or link data, which has not incorporated the dynamic process of social network. To better understand the key factors in the establishment of online relationship, we explore a large data set on Twitter, which contains all initiated links by 1453 organizational Twitter users over three months. An initiated link refers to organization following a user who is currently not a follower of the organization. We crawl data on a daily basis and monitor whether the initiated one-way link ends up with a two-way relationship. Based on the established homophily theory, we define two online homophily factors: achieved status homophily (estimated by the gap of the followers count), value homophily (measured by the overlap ratio of common followee, Pearson correlation, and Cosine similarity between two users’ tweets, respectively). We find that both homophily factors play a key role in the formation of online reciprocal relationship, and the effect of status homophily is larger for superior followee (one who has more followers than the corresponding organization) than for inferior followee (one who has less followers than the corresponding organization). Our finding not only extends the offline “individual- individual” homophily theory to the new online “organization- individual” relationship, but also provides Twitter users insight into extending their social network by strategically targeting followee

Whose and What Chatter Matters? The Impact of Tweets on Movie Sales

Social broadcasting networks such as Twitter in the U.S. and \Weibo" in China are transforming the way online word-of-mouth (WOM) is disseminated and consumed in the digital age. We investigate whether and how Twitter WOM aects movie sales by estimating a dynamic panel data model using publicly available data and well known machine learning algorithms. We nd that chatter on Twitter does matter, however, the magnitude and direction of the eect depends on whom the WOM is from and what the WOM is about. Measuring Twitter users' in uence by how many followers they have, we nd that the eect of WOM from more in uential users is signicantly larger than that from less in uential users. In support of some recent ndings about the importance of WOM valence on product sales, we also nd that positive Twitter WOM increases movie sales while negative WOM decreases them. Interestingly, we nd that the strongest eect on movie sales comes from those tweets where the authors express their intention to watch a certain movie. We attribute this to the dual eects of such intention tweets on movie sales: the direct eect through the WOM author's own purchase behavior, and the indirect eect through either the awareness eect or the persuasive eect of the WOM on its recipients. Our ndings provide new perspectives to understand the eect of WOM on product sales and have important managerial implications. For example, our study reveals the potential values of monitoring people's intention and sentiment on Twitter and identifying in uential users for companies wishing to harness the power of social broadcasting networks

A Twitter-Based Prediction Market: Social Network Approach

Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. Prediction markets represent one of the best examples of how this kind of wisdom of the crowds can be used. We use a Twitter-based prediction market to suggest that carefully designed market mechanisms can bring to light trends in dispersed information that improves the accuracy of our predictions. The information system we are developing combines the power of prediction markets with the popularity of Twitter. Simulation results show that our network-embedded prediction market can produce better predictions using information exchange in social networks and can outperform other prediction markets that do not use social networks. We also demonstrate that as cost decreases and more and more agents acquire information, the prediction market prices fully incorporate all available information, and the forecasting performance of the network-embedded prediction market is better

Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machine-learning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts’ scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse next-quarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machine-powered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data

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 Twitter exchanges between customers and three major airlines in North America. 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 while the actual time-to-response depends on customer’s social influence and sentiment as well as the firm’s social media strategy

Information Exchange in Prediction Markets: How Social Networks Promote Forecast Efficiency

This paper studies the effects of social networks on the performance of prediction markets with endogenous information acquisition. We provide a game-theoretic framework to resolve the question: Can social networks and information exchange promote the forecast efficiency in prediction markets? Our study shows that the use of social networks could be detrimental to forecast performance when the cost of information acquisition is high. Although social networks can provide internal communications among participants, they reduce the incentive to acquire information because of free riding. 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 action of information acquisition is decreasing in the number of participant\u27s friends and increasing in the network density

The Value of Humanization in Customer Service

As algorithm-based agents become increasingly capable of handling customer service queries, customers are often uncertain whether they are served by humans or algorithms, and managers are left to question the value of human agents once the technology matures. The current paper studies this question by quantifying the impact of customers\u27 enhanced perception of being served by human agents on customer service interactions. Our identification strategy hinges on the abrupt implementation by Southwest Airlines of a signature policy, which requires the inclusion of an agent\u27s first name in responses on Twitter, thereby making the agent more humanized in the eyes of customers. Multiple empirical analyses consistently show that customers are more willing to engage, and upon engagement, more likely to reach a resolution, with more humanized agents. Furthermore, we find that customers do not behave more aggressively to more humanized agents, hence humanization incurs no additional cost to agents

Racial Discrimination in Social Media Customer Service: Evidence from a Popular Microblogging Platform

The concept of racial inequality has existed from the early days of service provision, with evidence dating back to ancient civilizations. While the emergence of the Internet and social media has drastically transformed almost every aspect of everyday life, including the intrinsic values of social relationships, the impact of racial disparities on receiving services on online platforms is not so evident. Although many consumer brands provide customer service on social media today, little is known regarding the prevalence and magnitude of racial discrimination in the context of social media customer service. Thus, in this study, we examine the existence and the extent of racial discrimination against African-Americans in social media customer service. We analyzed all complaints to seven major U.S. airlines on Twitter for a period of nine months. Interestingly, our empirical analysis finds that African-American customers are less likely to receive brand responses to their complaints on social media. To the best of our knowledge, this is the first study to empirically analyze the racial discrimination phenomenon in the context of social media customer service

AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform

Artificial intelligence (AI) refers to the ability of machines or software to mimic or even surpass human intelligence in a given cognitive task. While humans learn by both induction and deduction, the success of current AI is rooted in induction, relying on its ability to detect statistical regularities in task input -- an ability learnt from a vast amount of training data using enormous computation resources. We examine the performance of such a statistical AI in a human task through the lens of four factors, including task learnability, statistical resource, computation resource, and learning techniques, and then propose a three-phase visual framework to understand the evolving relation between AI and jobs. Based on this conceptual framework, we develop a simple economic model of competition to show the existence of an inflection point for each occupation. Before AI performance crosses the inflection point, human workers always benefit from an improvement in AI performance, but after the inflection point, human workers become worse off whenever such an improvement occurs. To offer empirical evidence, we first argue that AI performance has passed the inflection point for the occupation of translation but not for the occupation of web development. We then study how the launch of ChatGPT, which led to significant improvement of AI performance on many tasks, has affected workers in these two occupations on a large online labor platform. Consistent with the inflection point conjecture, we find that translators are negatively affected by the shock both in terms of the number of accepted jobs and the earnings from those jobs, while web developers are positively affected by the very same shock. Given the potentially large disruption of AI on employment, more studies on more occupations using data from different platforms are urgently needed.Comment: 42 pages, 6 figures, 9 table