Citation
He, W. and Xu, G. (2016), "Social media analytics: unveiling the value, impact and implications of social media analytics for the management and use of online information", Online Information Review, Vol. 40 No. 1. https://doi.org/10.1108/OIR-12-2015-0393
Publisher
:Emerald Group Publishing Limited
Social media analytics: unveiling the value, impact and implications of social media analytics for the management and use of online information
Article Type: Guest Editorial From: Online Information Review, Volume 40, Issue 1.
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 et al., 2013; Jansen et al., 2009; Schoen et al., 2013; He et al., 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 and 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 et al., 2012; Pang and 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 and Gordon, 2014; He et al., 2015). A number of studies from various research communities have been devoted to unveil the value, impact and implications of social media analytics.
This special issue is to showcase the cutting edge research advances in social media analytics in order to provide a landscape of recent research progress and novel applications, techniques, case studies and strategies in analyzing online information posted on various social media platforms. This special issue contains eight interesting research papers related to social media analytics that unveil the value, impact and implications of social media analytics for the management and use of online information.
The article, “Informing Brand Messaging Strategies via Social Media Analytics” by Constantinos K. Coursaris, Wietske van Osch and Brigitte A. Balogh, describes a study that uses longitudinal data from three Fortune 200 companies – Delta Airlines, Wal-Mart and McDonald’s and develops a brand’s messaging strategies on social media encompassing three messaging dimensions, namely, appeal, content and richness. Findings reveal significant relations between purchase involvement and appeal. Furthermore, they found that abstract content categories are best combined with richer media. The authors also found that both transformation appeal and richer media have a highly significant and positive effect on engagement.
The article, “Visual Twitter Analytics (Vista): Temporally Changing Sentiment and the Discovery of Emergent Themes within Sport Event Tweets” by Orland Hoeber and others, describes a case study of using Visual Twitter Analytics (Vista) to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France). Vista was developed as a tool to support the exploration of the temporally changing sentiment within sport-related tweets, guided by the fundamental principles of visual analytics. The authors illustrate how emergent themes can be identified and isolated from the large collection of data and provide an example of the power of visual analytics methods for supporting the study of public opinion posted on Twitter.
The article, “Competitive Intelligence in Social Media Twitter: iPhone 6 vs. Galaxy S5” by Seung Jeong and others, uses a multiple case study approach to compare two competing smartphone manufacturers. A total of 229,948 tweets mentioning the iPhone6 or the Galaxy S5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing (NLP), lexicon-based sentiment analysis and purchase intention classification. Then the results have been validated using statistical analysis. This paper demonstrates the competitive intelligence via the consumer opinion mining of social media data.
The article, “Discovering Shilling Groups in a Real E-Commerce Platform” by Zhiang Wu and others, focusses on discovering Shilling Groups from Amazon China, a typical real e-commerce website. Shilling attack is becoming rampant in online shopping websites and shilling attackers publish mendacious ratings as well as reviews for promoting or suppressing target products. This paper presents a set of solutions for discovering shilling groups from a large number of consumer reviews.
The article, “How to Strengthen the Social Media Interactivity of E-government: Evidence from China” by Xiaoling Hao, Daqing Zheng, Qingfeng Zeng and Weiguo Fan, explores how to use social media in e-government to strengthen interactivity between government and the general public. This study employs general linear model and ANOVA method to analyze 14,910 posts belonged to the top list of the 96 most popular government accounts of Sina, one of the largest social media platforms in China. The authors found that both variables of the ratio of multimedia elements, and the ratio of external links have positive effects on the breadth of interactivity, while the ratio of multimedia features, and the ratio of originality have significant effects on the depth of interactivity.
The article, “The Role of Trust Management in Reward-based Crowdfunding” by Haichao Zheng, Jui-Long Hung, Zihao Qi and Bo Xu, investigates the role of trust management on the fundraising performance in reward-based crowdfunding. Data were collected from www.demohour.com – the first and one of the largest reward-based crowdfunding platforms in China. Partial least squares was used to analyze data of entrepreneur/sponsor profiles, entrepreneur/sponsor behaviors and crowdfunding projects. Results indicated trust management significantly promoted fundraising performance via central (entrepreneur’s creditworthiness) and peripheral (entrepreneur-sponsor interactions) routes.
The article, “Sentimental Interplay between Structured and Unstructured User Generated Contents – An Empirical Study on Online Hotel Reviews” by Xianfeng Zhang, Yang Yu, Hongxiu Li and Zhangxi Lin, explores the effects of satisfaction level, opinion dispersion and cultural context background on the interrelationship between structured and unstructured UGC by using online feedback on hotel services. NLP techniques including topic classification and sentiment analysis on the sentence level were used for the data analysis. The authors found that the variety of cognitions displayed by individuals affects the general significant interrelationship between structured and unstructured UGC. Extremely dissatisfied consumers or those with heterogeneous opinions tend to have a closer interconnection, and the interaction between valence and dispersion further strengthens or loosens the relationship. The satisfied or neutral consumers tend to show confounding sentiment signals in relation to the two different UGC. Chinese consumers behave differently from non-Chinese consumers, resulting in a relatively looser interplay.
The article, “Method of Potential Customer Searching from Opinions of Network Villagers in Virtual Communities” by Tsung-Yi Chen, Yan-Chen Liu and Yuh-Min Chen, proposes a mechanism that automatically searches for potential customers in virtual communities. Using the food industry as an example, the authors adopted the case study method to screen potential customers based on 400 articles from virtual communities, and combined a latent semantic analysis method with a back propagation neural network to verify the effectiveness of the proposed method.
The papers presented in this special issue illustrate the extensiveness and potential of social media analytics. A lot of challenges still exist in the social media analytics field. As big data have emerged as a new scientific paradigm, developing more effective and efficient big data techniques for social media analytics and use these techniques to create new value or assist in making more informed decisions in big data environment become increasingly important. We, the guest editors, look forward to an exciting future of research and contributions in the area of social media analytics and big data!
Assistant Professor Wu He -Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, Virginia, USA
Guandong Xu - Advanced Analytics Institute, University of Technology Sydney, Sydney, Australia
Acknowledgements
The guest editors would like to thank the reviewers who dedicated their time to reviewing the manuscripts submitted to this special issue.
References
Chen, H., Chiang, R.H. and Storey, V.C. (2012), “Business intelligence and analytics: from big data to big impact”, MIS Quarterly, Vol. 36 No. 4, pp. 1165-1188
Duan, W., Cao, Q., Yu, Y. and Levy, S. (2013), “Mining online user-generated content: using sentiment analysis technique to study hotel service quality”, Proceedings of the 46th Hawaii International Conference on System Sciences, pp. 3119-3128
Fan, W. and Gordon, M.D. (2014), “The power of social media analytics”, Communications of the ACM, Vol. 57 No. 6, pp. 74-81
He, W., Zha, S.H. and Li, L. (2013), “Social media competitive analysis and text mining: a case study in the pizza industry”, International Journal of Information Management, Vol. 33 No. 3, pp. 464-472
He, W., Wu, H., Yan, G., Akula, V. and Shen, J. (2015), “A novel social media competitive analytics framework with sentiment benchmarks”, Information & Management, Vol. 52 No. 7, pp. 801-812
Jansen, B.J., Zhang, M., Sobel, K. and Chowdury, A. (2009), “Twitter power: tweets as electronic word of mouth”, Journal of the American Society for Information Science and Technology, Vol. 60 No. 11, pp. 2169-2188
Pang, B. and Lee, L. (2008), “Opinion mining and sentiment analysis”, Foundations and Trends in Information Retrieval, Vol. 2 Nos 1-2, pp. 1-135
Schoen, H., Gayo-Avello, D., Metaxas, P.T., Mustafaraj, E., Strohmaier, M. and Gloor, P. (2013), “The power of prediction with social media”, Internet Research, Vol. 23 No. 5, pp. 528-543
Zeng, D., Chen, H., Lusch, R. and Li, S.H. (2010), “Social media analytics and intelligence”, IEEE Intelligent Systems, Vol. 25 No. 6, pp. 13-16
Further reading
Bollen, J., Mao, H. and Zeng, X. (2011), “Twitter mood predicts the stock market”, Journal of Computational Science, Vol. 2 No. 1, pp. 1-8
He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G. and Tao, R. (2015), “Gaining competitive intelligence from social media data: evidence from two largest retail chains in the world”, Industrial Management & Data Systems, Vol. 115 No. 9, pp. 1622-1636
About the Guest Editors
Assistant Professor Wu He received the BS Degree in Computer Science from the DongHua University, China, in 1998, and the PhD Degree in Information Science from the University of Missouri, USA, in 2006. He is currently an Assistant Professor of Information Technology at the Old Dominion University, Norfolk, VA. His research interests include cyber security, data mining, social media, knowledge management and e-learning technologies. He has received research funding from the US National Science Foundation. He has published more than 70 articles in peer-reviewed journals such as the Journal of the Association for Information Science and Technology, Information & Management, International Journal of Information Management, IEEE Transactions on Industrial Informatics, Internet Research, Information Technology and Management, Journal of Computer Information Systems, and Expert Systems with Applications. Assistant Professor Wu He is the corresponding author and can be contacted at: mailto:whe@odu.edu
Dr Guandong Xu is a Senior Lecturer in the Advanced Analytics Institute at the University of Technology Sydney. He received MSc and BSc Degree in Computer Science and Engineering from the Zhejiang University, China. He gained PhD degree in Computer Science from the Victoria University. Guandong has had over 100 publications in the areas of data mining, data analytics, recommender systems, social media and social network analysis and web and text analytics. He is the Assistant Editor-in-Chief of World Wide Web Journal, has been serving in editorial board or as Guest Editors for several international journals, such as the Computer Journal, Journal of Systems and Software, World Wide Web Journal and Multimedia Tools and Applications.