The Dual Micro/Macro Informing Role of Social Network Sites: Can Twitter Macro Messages Help Predict Stock Prices?

Introduction Emergence and proliferation of social media is one of the most significant trends of the 2000s. Starting with the establishment of Friendster in 2003 as the first large-scale social networking site (Boyd, 2006), a mere 8 years has seen the Internet territory explode with a variety of Social Networking Sites (SNS) which cater to different user categories and are tailored for different purposes. The widespread adoption and popularity of these websites has generated interest from consumers, companies, and researchers alike. Consumers have discovered the value of social network sites and are flocking to them in droves. Social networking sites as a whole almost doubled in unique visitors per month from around 115 million in July of 2007, to just over 200 million in April of 2011 according to one measure (MarketingProfs.com, 2011), and as of June 2012, unique visits to social networking sites have increased by 6% every year (Perez, 2012). Companies are striving to capitalize on the trend of consumer engagement in a variety of ways. Some are using SNS to find new customers (Kulikowski, 2011), to market to existing customers and build their brand (R2Integrated, 2010), and as a new avenue for customer communication for many top U.S. companies (Farfan, 2011). An emerging group of financial traders is closely monitoring SNS for information, crowd pulse, and connection with each other (Harris & Rogers, 2011). The majority of research on SNS is focused on use, adoption, privacy and security issues of SNS (Fogel & Nehmad, 2009; Shen & Khalifa, 2010). SNS generate large volumes of data which in aggregate contain useful information about views and opinions of SNS users. Several studies have investigated the ways to interpret and utilize the information contained in SNS-generated data (e.g., Naaman, Becker, & Gravano, 2011; Taraszow, Aristodemou, Shitta, Laouris, & Arsoy, 2010). This suggests that SNS play a dual role. On the one hand they provide infrastructure for individual informers to deliver messages to individual clients. On the other hand, due to a largely public nature of the individual message exchange, SNS themselves serve as informers providing aggregate information to clients. The goal of this paper is to introduce and validate a framework for understanding the dual role of SNS as platforms for exchanging micro messages and as macro informers. To validate the micro/macro informing framework, we examine if Twitter can serve as a macro informer to the stock market. By applying text mining techniques to SNS messages for the purpose of stock market prediction, this paper represents transdisciplinary research in the fields of information systems and finance, and thus contributes to the goal of Informing Science transdiscipline to reward and encourage research that crosses disciplinary boundaries (Cohen, 2009). In the next section we provide a brief overview of key types of SNS and discuss prior work related to mining the information from SNS sites. We then formally introduce the micro/macro informing framework. We continue with our research methodology and present the results of our analysis. We conclude with a discussion of implications for practice and research. Overview The term social networking site generally refers to those websites and their derived applications that deliver innovative online communication within and among a person's various social networks. SNS typically share a common set of features which include a profile (representation and/or description) for each user, the means to build and manage a personal relational network (i.e., friends, family, acquaintances, etc.), and access to creative methods to communicate with members of their relational network and the online community (Magro, Ryan, Sharp, & Ryan, 2008). SNS have grown in popularity at a tremendous rate in the past several years. Facebook, which has emerged as the dominant SNS, reported 900 million active users in April of 2012 (Goldman, 2012). …