Data Mining with the Use of Mobile Technologies Information and its Potential Implementations for Social Media Marketing

• Autorzy: Marcin Kuna
• Języki publikacji: EN

Abstrakty

EN

With vast majority of online traffic coming through mobile devices only in UK, our daily use of social media channels via smartphones or tablets generates tremendous quantities of data that can be now stored and analysed. Also called "Big Data", gigantic pools of categorized, online based statistics, provide a generous resource for the marketers to base their strategies on and scientists to perform their research on. Social media are in that context also an unbelievably generous source of constantly flowing data stream. Through the evaluation of selected available sources, following article takes a descriptive approach to finding the potential implementations of data mining through the use of mobile technologies in social media marketing. Author also attempts to segment the available analysed knowledge. The analysis reveals that the core concept that links all the disciplines and might be the result of mobile data mining implementation, is context aware advertising. (original abstract)

Słowa kluczowe

EN

Data Mining; Social media; Big Data; Mobile technologies

PL

Data Mining; Media społecznościowe; Big Data; Technologie mobilne

• Czasopismo: Przedsiębiorczość i Zarządzanie
• Rocznik: 2016
• Numer: t. 17, z. 11, cz. 1 Agile Commerce - zarządzanie informacją i technologią w biznesie
• Strony: 75--93

Twórcy

autor

Marcin Kuna

• Społeczna Akademia Nauk

Bibliografia

• Aalto L. (2004), Bluetooth and WAP Push Based Location-Aware Mobile Advertising System, Proceedings of the 2nd international conference on Mobile systems, applications, and services, ACM New York, NY, USA c2004, New York.
• Ali A.H. (2011), The Power of Social Media in Developing Nations: New Tools for Closing the Global Digital Divide and Beyond, "Harvard Human Rights Journal", Vol. 24.
• Asur S., Huberman B.A. (2010), Predicting the Future With Social Media, Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on http://www.arxiv.org arXiv:1003.5699v1., vol. 1, no. 6.
• Barreto A.M. (2013), Do users look at banner ads on Facebook?, "Journal of Research in Interactive Marketing", Vol. 7, No. 2.
• Beach A., Gartrell M., Xing X., Han R., Lv Q., Mishra S., Seada K. (2010), Fusing Mobile, Sensor, and Social Data To Fully Enable Context-Aware Computing [in:] Workshop on Mobile Computing Systems & Applications, Maryland.
• Carphone Warehouse (2016), All-Star Galaxy S7 360 Review by Carphone Warehouse UK, available at: https://www.facebook.com/carphonewarehouse (accessed 24 March 2016).
• Chen H., Chiang R.H.L., Storey V.C. (2012), Business Intelligence and Analytics: From Big Data To Big Impact, "Mis Quarterly", Vol. 36, No. 4.
• Cheong C. (2011), Social Media Data Mining: A Social Network Analysis Of Tweets During The 2010- 2011 Australian Floods, 15th Pacific Asia Conference on Information Systems (PACIS 2011).
• Correa T., Hinsley A.W., de Zúniga H.G. (2010), Who interacts on the Web?: The intersection of users' personality and social media use, "Computers in Human Behavior", Elsevier Ltd, Vol. 26 No. 2.
• Cui, Y., Cui, Y., Roto V. (2008), How People Use the Web on Mobile Devices, WWW '08 Proceedings of the 17th international conference on World Wide Web, ACM New York, NY, USA c2008, Beijing, China.
• Dell (2015), Data Mining Techniques, available at: http://documents.software.dell.com/Statistics/Textbook/Data-Mining-Techniques (accessed 22 March 2016).
• Evans L. (2011), Social Media Marketing. Odkryj potencjał Facebooka, Twittera i innych portali społecznościowych,Helion, Gliwice, Poland.
• Fan W., Bifet A. (2013), Mining Big Data: Current Status, and Forecast to the Future, "ACM SIGKDD Explorations Newsletter", Vol. 14, No. 2.
• Fayyad U., Piatetsky-Shapiro G., Smyth P. (1996), From data mining to knowledge discovery in databases, "AI magazine", Vol. 17, No. 3.
• Feng J., Barbosa L., Torres V. (2010), Systems and Methods for Social Media Data Mining, US Patent App. 12/858,852, No. 19.
• Gaber M.M. (2012), Advances in data stream mining, "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery", Vol. 2, No. 1.
• Goh J.Y., Taniar D. (2004), Mobile Data Mining by Location Dependencies in Yang, Z.R., Yin, H. and Everson, R.M. (Eds.), Intelligent Data Engineering and Automated Learning -- IDEAL 2004: 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings, Springer Berlin Heidelberg, Berlin, Heidelberg.
• Gu T., Pung H.K., Zhang D.Q. (2004), Toward an OSGi-based infrastructure for context-aware applications, "IEEE Pervasive Computing", Vol. 3, No. 4.
• Guo Q., Jin H., Lagun D., Yuan S., Agichtein E. (2013), Mining touch interaction data on mobile devices to predict web search result relevance, Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval.
• He W., Zha S., Li L. (2013), Social media competitive analysis and text mining: A case study in the pizza industry, "International Journal of Information Management", No. JUNE, doi:10.1016/j.ijinfomgt.2013.01.001.
• Hendler J. (2009), "Web 3.0 emerging", Computer, Vol. 42 No. 1.
• Howard D., Mangold W.G., Johnston T. (2014), Managing your social campaign strategy using facebook, twitter, instagram, youtube & pinterest: An interview with dana howard, social media marketing manager, "Business Horizons", Kelley School of Business, Indiana University, Vol. 57, No. 5,.
• Hsu J. (2002), Data mining trends and developments: The key data mining technologies and applications for the 21st century, Proceedings of the 19th Annual Information Systems ..., available at: http://www.csse.monash.edu.au/~mgaber/DATA MINING TRENDS AND DEVELOPMENTS The Key Data Mining Technologies and Applications for 21 century.pdf.
• Jansen B.J., Spink A. (2006), How are we searching the World Wide Web? A comparison of nine search engine transaction logs, "Information Processing and Management", Vol. 42, No. 1.
• Kaplan A.M., Haenlein M. (2010), Users of the world, unite! The challenges and opportunities of Social Media, "Business Horizons", Vol. 53, No. 1.
• Kinsky E.S., Gerlich R.N., Brock Baskin M.E., Drumheller K. (2014), Pulling ads, making apologies: Lowe's use of Facebook to communicate with stakeholders, "Public Relations Review", Elsevier Inc., Vol. 40, No. 3.
• Korpipää P., Mäntyjärvi J., Kela J., Keränen H., Malm E.J. (2003), Managing context information in mobile devices, "IEEE Pervasive Computing", Vol. 2, No. 3.
• Lane N.D., Miluzzo E., Lu H., Peebles D., Choudhury T., Campbell A.T. (2010), A survey of mobile phone sensing, "IEEE Communications Magazine", Vol. 48, No. 9.
• Lee S., Paik J., Ok J., Song I., Kim U.M. (2007), Efficient Mining of User Behaviors by Temporal Mobile Access Patterns, "International Journal of Computer Science and Network Security", Vol. 7 No. 2.
• Lemlouma T., Layaida N. (2009), Context-aware adaptation for mobile devices, IEEE International Conference on Mobile Data Management, 2004. Proceedings. 2004, IEEE.
• Linoff G.S., Berry M.J.A. (2001), Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Wiley Publishing Inc., Indianapolis, 3rd Editio.
• Lohr S. (2012), The age of big data, "New York Times", 11.
• Makino T., Jung C., Phan D. (2015), Finding more mobile-friendly search results, available at: https://webmasters.googleblog.com/2015/02/finding-more-mobile-friendly-search.html (accessed 21 March 2016).
• Mäntyjärvi J., Himberg J., Kangas P., Tuomela U., Huuskonen P. (2004), Sensor signal data set for exploring context recognition, 2nd Int. Conf. on Pervasive Computing, No. March.
• Mayzlin D. (2006), Promotional Chat on the Internet, "Marketing Science", Vol. 25, No. 2.
• McAlexander J.H.J., Schouten J.J.W., Koenig H.F.H., Communily B.B. (2002), Building brand community, "The Journal of Marketing", Vol. 66, No. January, pp. 38-54.
• Mena J. (2013), Data Mining Mobile Devices, Auerbach Publications, Boca Raton.
• Miller P. (2005), Web 2.0: Building the new library, "Ariadne", No. 45.
• O'Reilly T. (2007), What is Web 2.0: Design patterns and business models for the next generation of software, "Communications & strategies", No. 65.
• Office for National Statistics (UK), (2016), available at: www.ons.gov.uk (accessed 21 March 2016).
• Okazaki S., Taylor C.R. (2013), Social media and international advertising: theoretical challenges and future directions, "International Marketing Review", Vol. 30, No. 1.
• Phang C.W., Kankanhalli A., Sabherwal R. (2009), Usability and Sociability in Online Communities: A Comparative Study of Knowledge Seeking and Contribution, "Journal of the Association for Information Systems", Vol. 10, No. 10.
• Quercia D., Lathia N., Calabrese F., Di Lorenzo G., Crowcroft J. (2010), Recommending social events from mobile phone location data, Proceedings - IEEE International Conference on Data Mining, ICDM.
• Russel M.A. (2013), Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, Girhub, and more, O'Reilly Media, Inc, Sebastopol, Second Edi.
• Ryan D. (2014), Understanding digital marketing, Kogan Page, London, 3rded.
• statista.com (2016), available at: http://www.statista.com/ (accessed 20 March 2016).
• Szetela D., Kerschbaum J. (2010), Pay-Per-Click Search Engine Marketing - An Hour A Day, Optimization, Indianapolis, Ind. : Wiley, c2010., Indianapolis, 1st ed.
• Thackeray R., Neiger B.L., Hanson C.L., McKenzie J.F. (2008), Enhancing promotional strategies within social marketing programs: use of Web 2.0 social media, "Health promotion practice", Vol. 9 No. 4.
• Tiago M.T.P.M.B., Veríssimo J.M.C. (2014), Digital marketing and social media: Why bother?, "Business Horizons", Vol. 57 No. 6,.
• Wang Y., Lim E.-P., Hwang, S.-Y. (2003), On Mining Group Patterns of Mobile Users [in:] Database and Expert Systems Applications, Prague, Springer.
• Wilkinson I., Young L. (2002), Marketing theory in the next millennium, "Journal of Business Research", Vol. 55, No. 2.
• Wu X., Zhu X., Wu G.-Q., Ding W. (2014), Data Mining with Big Data, "Knowledge and Data Engineering, IEEE Transactions on", Vol. 26.
• YouTube Statistics (2016), available at: https://www.youtube.com/yt/press/en-GB/statistics.html (accessed 22 March 2016).