Publisher's Synopsis
Social media have become ubiquitous and are playing an increasingly critical role in society. The wide use of social media platforms has generated massive user generated contents (UGCs). To leverage these UGCs, it is important for organizations to develop capability of collecting, storing and analyzing social media data for the purpose of harvesting information and actionable knowledge for decision making and forecasting (Duan, Cao, Yu, & Levy, 2013; Jansen, Zhang, Sobel, & Chowdury, 2009; Schoen et al., 2013; He, Zha & Li, 2013). As one consequence of these developments, social media analytics has emerged as an important area of study. Social media analytics is concerned with "developing and evaluating informatics tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data to facilitate conversations and interactions to extract useful patterns and intelligence" (Fan & Gordon, 2014; Zeng et al. 2010). Therefore, the development of effective and efficient analytics techniques for social media analysis becomes essential. To conduct social media analytics, data mining, text analysis and related advanced analytics techniques, e.g., sentiment analysis and semantic analysis techniques are frequently adopted (Chen, Chiang & Storey, 2012; Pang & Lee 2008). Recently there has been strong interest in the power of social media analytics in creating new value, supporting decision-making and enhancing competitive advantage (Fan & Gordon, 2014; He, Wu, Yan, Akula, & Shen, 2015). A number of studies from various research communities have been devoted to unveil the value, impact, and implications of social media analytics.
