A Framework-based Mapping and Filtering for Social Media
Keywords:
Big data, filtering, mapping, MapReduce, NodeXl, social mediaAbstract
The aim of this paper is to develop a framework to extract value from social media data based on mapping and filtering, using the data processing approach based on MapReduce. In order to test the application of this framework, researchers used NodeXL to obtain and analyze data from Twitter. The results show the application of framework to capture relevant data for efficient decision-making. The outcome of this paper contributes to a significant achievement that provides an important innovation in research methods in big data era to trace how data flows across the social media and how to analyze this data.
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Berber, M., Graupner, E., & Maedche, A. (2014). The information panopticon in the big data era. Journal of Organization Design, 3(1), 14-19.
Brunswicker, S., Bertino, E., & Matei, S. (2015). Big data for open digital innovation- A research roadmap. Big Data Research, 2, 53-58.
Chen, G., Wu, S., & Wang, Y. (2015). The evolvement of big data systems: from the perspective of an information security application. Big Data Research, 2, 65-73.
Damianos, L. E., Cuomo, D. L., & Drozdetski, S. (2011). Handshake: A case study for exploring business networking for enterprise, inside and out. Paper presented at the International Conference on Human Computer Interaction.
Damianos, L. E., Cuomo, D., Griffith, J., Hirst, D. M., & Smallwood, J. (2007). Exploring the adoption, utility and social influences of social bookmarking in a corporate environment. Paper presented at the International Conference on System Sciences, Hawaii.
Davenport, T. H., & Dyche, J. (2013). Big data in big companies: International Institute for Analytics.
Doreian, P. (2001). Causality in social network analysis. Sociological Methods & Research, 30(1), 81-114.
Galbraith, J. R. (2014). Organization design challengers resulting from big data. Journal of Organization Design, 3(1), 2-13.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods and anlytics. International Journal of Information Management, 35, 137-144.
Gangadharbatla, H., Bright, L. F., & Logan, K. (2014). Social media and news gathering: tapping into the millennial mindset. The Journal of Social Media in Society, 3(1), 45-63.
Ghani, N. A., & Kamal, S. S. M. (2015). A Sentiment-based filteration and data analysis framework for social media 5th International Conference on Computing and Informatics ICOCI (pp. 632-637). Istanbul, Turkey.
Governatori, G., & Iannella, R. (2011). A modeling and reasoning framework for social networks policies. Enterprise Information Systems, 5(1), 145–167.
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in pizza industry. International Journal of Information Management, 33, 464-472.
Holtzblatt, L., Drury, J. L., Weiss, D., Damianos, L. E., & Cuomo, D. (2013). Evaluating the uses and benefits of an enterprise social media platform. Journal of Social Media for Organizations, 1(1), 1-21.
Huang, T., Lan, L., Fang, X., An, P., Min, J., & Wang, F. (2015). Promises and challenges of big data computing in health sciences. Big Data Research, 2(1), 2-11.
Izhar, T. A. T., Torabi, T., Bhatti, M. I., & Liu, F. (2013). Recent developments in the organization goals conformance using ontology. Expert Systems with Applications, 40(10), 4252-4267.
Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2, 59-64.
Kaplan, A. M., & Haelein, M. (2010). Users of the worl, united! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68.
Keckley, P. H. (2010). Social networks in health care: Communication, collaboration and insights. from http://www.deloitte.com/ assets/Dcom-UnitedStates/Local%20Assets/Documents/US CHS 2010Social Networks 070710.pdf
Krause, D., & Smith, M. (2014). Twitter as mythmaker in storytelling: The emergence of hero status by the Boston police department in the aftermath of the 2013 marathon bombing. The Journal of Social Media in Society, 3(1), 8-27.
McIntyre, K. (2014). The evolution of social media from 1969 to 2013: A change in competition and a trend toward complementary. The Journal of Social Media in Society, 3(2), 6-25.
Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729–733.
Polato, I., Ré, R., Goldman, A., & Kon, F. (2014). A comprehensive view of Hadoop research- A systematic literature review. Journal of Networking and Computer Application, 46, 1-25.
Qualman, E. (2009). Socialnomics how social media transforms the way we live and do business. Hoboken: Wiley John & Sons, Inc.
Raacke, J., & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the uses and gratifications theory to exploring friend-networking sites. CyberPsychology & Behavior, 11(2), 169–174.
Roth, C., & Cointet, J. P. (2010). Social and semantic coevolution in knowledge networks. Social Networks, 32(1), 16-29.
Safko, L., & Brake, D. K. (2009). The social media bible: Tactics, tools, and strategies for business success. Hoboken: Wiley John & Sons, Inc.
da Silva, N. F. F., Hruschka, E. R., & Hruschka, Jr., E. R. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170-179.
Smith, M., Ceni A., Milic-Frayling, N., Shneiderman, B., Mendes Rodrigues, E., Leskovec, J., Dunne, C., (2010). NodeXL: a free and open network overview, discovery and exploration add-in for Excel 2007/2010/2013/2016, from the Social Media Research Foundation: https://www.smrfoundation.org
Zhang, H., Chen, G., Ooi, B. C., Tan, K.-L., & Zhang, M. (2015). In-memory big data management and processing: A survey. IEEE Transaction on Knowledge and Data Engineering, 27(7), 1920-1948.
Zhi, Q., Zhao-Wen, L., & Yan, M. (2011). Research of Hadoop-based data flow management system. The Journal of China Universities of Post and Telecommunications, 18, 164-168.
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2018-05-31
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