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Wireless Personal Communications

, Volume 97, Issue 3, pp 4015–4061 | Cite as

Challenges in the Analysis of Online Social Networks: A Data Collection Tool Perspective

  • Anuradha Goswami
  • Ajey Kumar
Article
  • 426 Downloads

Abstract

The present era of internet has radically changed the way people communicate with each other. Online Social Network platforms have enhanced this to real-time communication where interactions vary from casual relationships to formal bonding. This real-time communication between the users over Online Social Network platforms generates data which directly or indirectly gives lot of information. But extracting this data and mining information out of it is a profound challenge. Researchers need appropriate tools to churn out this data and get valuable information by analyzing and visualizing it. This paper does a comprehensive survey of types of Online Social Network Analysis resulting in segregation of research challenges associated with each of the types. A detailed study of the existing data collection tools and analysis techniques was further carried out to understand the challenges a researcher faces while using it. Finally, mapping analysis was done using research challenges, data collection tools and the types of Online Social Network Analysis, to understand to what extent the existing data collection tools and analysis techniques can meet the research challenges. The mapping analysis shows an absolute requirement of new data collection tools and algorithms by the researchers/developers.

Keywords

OSN SNA Data collection tools Research challenges 

References

  1. 1.
    Chen, Z., Kalashnikov, D. V., & Mehrotra, S. (2009, June). Exploiting context analysis for combining multiple entity resolution systems. In Proceedings of the 2009 ACM SIGMOD international conference on management of data (pp. 207–218). ACM.Google Scholar
  2. 2.
  3. 3.
    Wassaerman, S., & Faust, K. (1994). Social network analysis in the social and behavioural sciences. In Social network analysis: Methods and applications. Cambridge: Cambridge University Press.Google Scholar
  4. 4.
    Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007, October). Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement (pp. 29–42). ACM.Google Scholar
  5. 5.
    Flake, G. W., Lawrence, S., Giles, C. L., & Coetzee, F. M. (2002). Self-organization and identification of web communities. Computer, 35(3), 66–70.CrossRefGoogle Scholar
  6. 6.
    Flake, G. W., Tarjan, R. E., & Tsioutsiouliklis, K. (2004). Graph clustering and minimum cut trees. Internet Mathematics, 1(4), 385–408.MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Hopcroft, J., Khan, O., Kulis, B., & Selman, B. (2003, August). Natural communities in large linked networks. In Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 541–546). ACM.Google Scholar
  9. 9.
    Newman, M. E. (2004). Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2), 321–330.CrossRefGoogle Scholar
  10. 10.
    Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68.CrossRefGoogle Scholar
  11. 11.
    Site of SEO Company, SEO Positive, http://www.seo-positive.co.uk/blog/different-types-of-social-networks. Accessed December 31, 2014.
  12. 12.
    Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRefGoogle Scholar
  13. 13.
    Asur, S., & Huberman, B. (2010). Predicting the future with social network. In Web intelligence and intelligent agent technology (WIIAT), 2010 IEEE/WIC/ACM international conference on (Vol. 1).Google Scholar
  14. 14.
    Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, February). Identifying influencers on twitter. In Fourth ACM international conference on web search and data mining (WSDM).Google Scholar
  15. 15.
    Wen-ying, S. C., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009). Social media use in the United States: Implications for health communication. Journal of Medical Internet Research, 11(4), e48.CrossRefGoogle Scholar
  16. 16.
    Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.CrossRefGoogle Scholar
  17. 17.
    Shin, D. H., & Shin, Y. J. (2011). Why do people play social network games? Computers in Human Behavior, 27(2), 852–861.CrossRefGoogle Scholar
  18. 18.
    Blogger, https://www.blogger.com/. Accessed December, 2015.
  19. 19.
    WordPress.com, https://wordpress.com/. Accessed December, 2015.
  20. 20.
    Facebook, https://www.facebook.com/login/. Accessed December, 2015.
  21. 21.
    Twitter, https://twitter.com/?lang=en. Accessed December, 2015.
  22. 22.
    LinkedIn, https://in.linkedin.com/. Accessed December, 2015.
  23. 23.
    YouTube, https://www.youtube.com/?gl=IN. Accessed December, 2015.
  24. 24.
    Flikr, https://www.flickr.com/. Accessed December, 2015.
  25. 25.
    Podcast Alley, www.podcastalley.com/. Accessed December, 2015.
  26. 26.
    Digg, www.digg.com. Accessed December, 2015.
  27. 27.
    Foursquare, https://foursquare.com/. Accessed December, 2015.
  28. 28.
    Google Groups, https://groups.google.com/. Accessed December 2015.
  29. 29.
    Yang, T. A., Kim, D. J., & Dhalwani, V. (2008). Social networking as a new trend in e-marketing. In Research and practical issues of enterprise information systems II (pp. 847–856). Springer US.Google Scholar
  30. 30.
    Karimzadehgan, M., Agrawal, M., & Zhai, C. (2009). Towards advertising on social networks. Information Retrieval and Advertising (IRA-2009), 28.Google Scholar
  31. 31.
    Huberman, B. A., Romero, D. M., & Wu, F. (2008). Social networks that matter: Twitter under the microscope. Available at SSRN 1313405.Google Scholar
  32. 32.
    Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4), 370–379.CrossRefGoogle Scholar
  33. 33.
    Tracy, E. M., Kim, H., Brown, S., Min, M. O., Jun, M., & McCarty, C. (2012). Substance abuse treatment stage and personal social networks among women in substance abuse treatment. Journal of the Society for Social Work and Research, 3(2), 65–79.CrossRefGoogle Scholar
  34. 34.
    Wipfli, H. L., Fujimoto, K., & Valente, T. W. (2010). Global tobacco control diffusion: the case of the framework convention on tobacco control. American Journal of Public Health, 100(7), 1260–1266.CrossRefGoogle Scholar
  35. 35.
    Perliger, A., & Pedahzur, A. (2011). Social network analysis in the study of terrorism and political violence. PS: Political Science & Politics, 44(01), 45–50.Google Scholar
  36. 36.
    Hewitt, A., & Forte, A. (2006). Crossing boundaries: Identity management and student/faculty relationships on the Facebook. Poster presented at CSCW, Banff, Alberta, 1–2.Google Scholar
  37. 37.
    Sjolander, C., & Ahlstrom, G. (2012). The meaning and validation of social support networks for close family of persons with advanced cancer. BMC Nursing, 11(1), 1.CrossRefGoogle Scholar
  38. 38.
    Dall’Asta, L., Marsili, M., & Pin, P. (2012). Collaboration in social networks. Proceedings of the National Academy of Sciences, 109(12), 4395–4400.CrossRefGoogle Scholar
  39. 39.
    Diesner, J., & Carley, K. M. (2005, April). Exploration of communication networks from the enron email corpus. In SIAM International Conference on Data Mining: Workshop on Link Analysis, Counterterrorism and Security, Newport Beach, CA.Google Scholar
  40. 40.
    Zuber, M. (2014). A survey of data mining techniques for social network analysis. International Journal of Research in Computer Engineering & Electronics, 3(6), 1–8.Google Scholar
  41. 41.
    Shin, H., Byun, C., & Lee, H. (2015). The influence of social media: Twitter usage pattern during the 2014 super bowl game. Life, 10(3), 109–118.Google Scholar
  42. 42.
    Ruhela, A., Tripathy, R. M., Triukose, S., Ardon, S., Bagchi, A., & Seth, A. (2011, December). Towards the use of online social networks for efficient internet content distribution. In Advanced networks and telecommunication systems (ANTS), 2011 IEEE 5th international conference on (pp. 1–6). IEEE.Google Scholar
  43. 43.
    Guille, A., Hacid, H., Favre, C., & Zighed, D. A. (2013). Information diffusion in online social networks: A survey. ACM SIGMOD Record, 42(2), 17–28.CrossRefGoogle Scholar
  44. 44.
    Edward M. Lazzarin, An overview of analysis of online social networks. http://www1.cse.wustl.edu/~jain/cse567-11/ftp/social/index.html. Accessed January, 2015.
  45. 45.
    Baldi, P., Frasconi, P., & Smyth, P. (2003). Modeling the internet and the web—probabilistic methods and algorithms. Chichester, West Sussex: Wiley.Google Scholar
  46. 46.
    Barabási, A. L., Albert, R., & Jeong, H. (1999). The diameter of the world wide web. Nature, 401(6749), 130–131.CrossRefGoogle Scholar
  47. 47.
    Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., et al. (2000). Graph structure in the web. Computer Networks, 33(1), 309–320.CrossRefGoogle Scholar
  48. 48.
    Krapivsky, P. L., Redner, S., & Leyvraz, F. (2000). Connectivity of growing random networks. Physical Review Letters, 85(21), 4629.CrossRefGoogle Scholar
  49. 49.
    Dorogovtsev, S. N., Mendes, J. F. F., & Samukhin, A. N. (2000). Structure of growing networks with preferential linking. Physical Review Letters, 85(21), 4633.CrossRefGoogle Scholar
  50. 50.
    Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.zbMATHCrossRefGoogle Scholar
  51. 51.
    Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.CrossRefGoogle Scholar
  52. 52.
    de Sola Pool, I., & Kochen, M. (1979). Contacts and influence. Social Networks, 1(1), 5–51.MathSciNetCrossRefGoogle Scholar
  53. 53.
    Milgram, S. (1967). The small world problem. Psychology Today, 2(1), 60–67.Google Scholar
  54. 54.
    Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.zbMATHCrossRefGoogle Scholar
  55. 55.
    Amaral, L. A. N., Scala, A., Barthelemy, M., & Stanley, H. E. (2000). Classes of small-world networks. Proceedings of the National Academy of Sciences, 97(21), 11149–11152.CrossRefGoogle Scholar
  56. 56.
    Leskovec, J., & Horvitz, E. (2008, April). Planetary-scale views on a large instant-messaging network. In Proceedings of the 17th international conference on World Wide Web (pp. 915–924). ACM.Google Scholar
  57. 57.
    Cha, M., Mislove, A., Adams, B., & Gummadi, K. P. (2008, August). Characterizing social cascades in flickr. In Proceedings of the first workshop on Online social networks (pp. 13–18). ACM.Google Scholar
  58. 58.
    Ediger, D., Jiang, K., Riedy, J., Bader, D. A., Corley, C., Farber, R., & Reynolds, W. N. (2010, September). Massive social network analysis: Mining twitter for social good. In Parallel Processing (ICPP), 2010 39th International Conference on (pp. 583–593). IEEE.Google Scholar
  59. 59.
    Weisstein, E. W. “Weakly Connected Component.” From MathWorld—A Wolfram Web Resource. http://mathworld.wolfram.com/WeaklyConnectedComponent.html.
  60. 60.
    Myers, S. A., Sharma, A., Gupta, P., & Lin, J. (2014, April). Information network or social network? The structure of the twitter follow graph. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (pp. 493–498). International World Wide Web Conferences Steering Committee.Google Scholar
  61. 61.
    Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440–442.zbMATHCrossRefGoogle Scholar
  62. 62.
    Newman, M. E., Strogatz, S. H., & Watts, D. J. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64(2), 026118.CrossRefGoogle Scholar
  63. 63.
    Li, L., Alderson, D., Doyle, J. C., & Willinger, W. (2005). Towards a theory of scale-free graphs: Definition, properties, and implications. Internet Mathematics, 2(4), 431–523.MathSciNetzbMATHCrossRefGoogle Scholar
  64. 64.
    Garriss, S., Kaminsky, M., Freedman, M. J., Karp, B., Mazières, D., & Yu, H. (2006, May). RE: Reliable Email. In NSDI (Vol. 6, pp. 22–22).Google Scholar
  65. 65.
    Mislove, A., Gummadi, K. P., & Druschel, P. (2006, November). Exploiting social networks for internet search. In 5th Workshop on Hot Topics in Networks (HotNets06). Citeseer (p. 79).Google Scholar
  66. 66.
    Yu, H., Kaminsky, M., Gibbons, P. B., & Flaxman, A. (2006). Sybilguard: defending against sybil attacks via social networks. ACM SIGCOMM Computer Communication Review, 36(4), 267–278.CrossRefGoogle Scholar
  67. 67.
    Krishnamurthy, B. (2009, January). A measure of online social networks. In Communication systems and networks and workshops, 2009. COMSNETS 2009. First international (pp. 1–10). IEEE.Google Scholar
  68. 68.
    Golder, S. A., Wilkinson, D. M., & Huberman, B. A. (2007). Rhythms of social interaction: Messaging within a massive online network. In Communities and technologies 2007 (pp. 41–66). Springer London.Google Scholar
  69. 69.
    Some, R. (2013). A survey on social network analysis and its future trends. International Journal of Advanced Research in Computer and Communication Engineering, 2(6), 2403–2405.Google Scholar
  70. 70.
    Ting, I. (2008, June). Web mining techniques for on-line social networks analysis. In Service Systems and Service Management, 2008 international conference on (pp. 1–5). IEEE.Google Scholar
  71. 71.
    Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. ACM SIGKDD Explorations Newsletter, 7(2), 3–12.CrossRefGoogle Scholar
  72. 72.
    Zhang, M. (2009, January). Exploring adolescent peer relationships online and offline: an empirical and social network analysis. In Communications and mobile computing, 2009. CMC’09. WRI international conference on (Vol. 3, pp. 268–272). IEEE.Google Scholar
  73. 73.
    Zhu, M., Liu, W., Hu, W., & Fang, Z. (2009, December). Social Network Analysis in IT Company. In 2009 International conference on e-learning, E-business, enterprise information systems, and E-government (pp. 305–307). IEEE.Google Scholar
  74. 74.
    Yusof, N., & Rahman, A. A. (2009, November). Analyzing online asynchronous discussion using content and social network analysis. In Intelligent Systems Design and Applications, 2009. ISDA’09. Ninth International Conference on (pp. 872–877). IEEE.Google Scholar
  75. 75.
    Guber, T. (1993). A translational approach to portable ontologies. Knowledge Acquisition, 5(2), 199–229.CrossRefGoogle Scholar
  76. 76.
    Antoniou, G., & Van Harmelen, F. (2004). A semantic web primer. Cambridge: MIT Press.Google Scholar
  77. 77.
    Wennerberg, P. O. (2005). Ontology based knowledge discovery in Social Networks. Final Report, JRC Joint Research Center, 1–34.Google Scholar
  78. 78.
    Orgnet.com. Social network analysis software and services for organizations and their consultants http://www.orgnet.com/.
  79. 79.
    Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. Canada: Empirical Press.Google Scholar
  80. 80.
    Hoser, B., Hotho, A., Jäschke, R., Schmitz, C., & Stumme, G. (2006). Semantic network analysis of ontologies (pp. 514–529). Berlin, Heidelberg: Springer.Google Scholar
  81. 81.
    Fox, S., Karnawat, K., Mydland, M., Dumais, S., & White, T. (2005). Evaluating implicit measures to improve web search. ACM Transactions on Information Systems (TOIS), 23(2), 147–168.CrossRefGoogle Scholar
  82. 82.
    Joachims, T. (2002, July). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133–142). ACM.Google Scholar
  83. 83.
    Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005, August). Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 154–161). ACM.Google Scholar
  84. 84.
    Xue, G. R., Zeng, H. J., Chen, Z., Yu, Y., Ma, W. Y., Xi, W., & Fan, W. (2004, November). Optimizing web search using web click-through data. In Proceedings of the thirteenth ACM international conference on Information and knowledge management (pp. 118–126). ACM.Google Scholar
  85. 85.
    Cooley, R., Mobasher, B., & Srivastava, J. (1997, November). Web mining: Information and pattern discovery on the world wide web. In Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International Conference on (pp. 558–567). IEEE.Google Scholar
  86. 86.
    Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge: MIT Press.Google Scholar
  87. 87.
    Chakrabarti, S. (2003). Mining the web: Discovering knowledge from hypertext data. San Francisco: Morgan Kaufmann Publishers.Google Scholar
  88. 88.
    Chen, H., & Chau, M. (2004). Web mining: Machine learning for web applications. Annual Review of Information Science and Technology (ARIST), 38, 289–329.CrossRefGoogle Scholar
  89. 89.
    Desikan, P., Srivastava, J., Kumar, V., Tan, P.N. (2002). Hyperlink Analysis: Techniques and Applications, Technical Report (TR 2002-0152), Army High Performance Computing Center.Google Scholar
  90. 90.
    Faca, F. M., & Lanzi, P. L. (2005). Mining interesting knowledge from weblogs: A survey. Data Knowledge Engineering, 53(3), 225–241.CrossRefGoogle Scholar
  91. 91.
    Pal, S., Talwar, V., & Mitra, P. (2002). Web mining in soft computing framework: Relevance, state of the art and future directions. IEEE Transactions on Neural Networks, 13(5), 1163–1177.CrossRefGoogle Scholar
  92. 92.
    Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. (2000). Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1, 12–23.CrossRefGoogle Scholar
  93. 93.
    Garg, A. K., Amir, M., Jarrar Ahmed, M. S., & Bansal, S. (2014). Implementation of a Search Engine. International Journal of Science and Research (IJSR) ISSN (Online), 3(4), 2319–7064.Google Scholar
  94. 94.
    Srivastava, J. “Web Mining: Accomplishments & Future Directions”, University of Minnesota USA, srivasta@cs.umn.edu, http://www.cs.umn.edu/faculty/srivasta.html.
  95. 95.
    Mr. Dushyant B. Rathod, Dr. Samrat Khanna, “A Review on Emerging Trends of Web Mining and its Applications” ISSN: 2321-9939.Google Scholar
  96. 96.
    Sona, J. S., & Ambhaikar, A. (2014). A reconciling website system to enhance efficiency with web mining techniques. International Journal of Scientific and Engineering Research, 3(2), 498–500.Google Scholar
  97. 97.
    Sandhya., Chaturvedi, M. (2013). A survey on web mining algorithms. The International Journal Of Engineering And Science (IIJES), 2(3), 25–30.Google Scholar
  98. 98.
    Zhang, Y., Yu, J. X., & Hou, J. (2005). Web communities: Analysis and construction. Berlin: Springer.Google Scholar
  99. 99.
    Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet of things: Literature review and challenges. International Journal of Distributed Sensor Networks, 2015, 12.Google Scholar
  100. 100.
    Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31(3), 264–323.CrossRefGoogle Scholar
  101. 101.
    Tseng, B. L., Tatemura, J., & Wu, Y. (2005, May). Tomographic clustering to visualize blog communities as mountain views. In WWW 2005 Workshop on the weblogging ecosystem.Google Scholar
  102. 102.
    Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-scale sentiment analysis for news and blogs. ICWSM, 7(21), 219–222.Google Scholar
  103. 103.
    Mika, P. (2005). Flink: Semantic web technology for the extraction and analysis of social networks. Web Semantics: Science, Services and Agents on the World Wide Web, 3(2), 211–223.CrossRefGoogle Scholar
  104. 104.
    Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive algorithms and representations for text categorization. In Proceedings of the Seventh ACM International Conference on Information and Knowledge Management (pp. 148–155).Google Scholar
  105. 105.
    Frank, E., Trigg, L. E., Holmes, G., & Witten, I. H. (1998). Naive Bayes for regression. Machine Learning, 41(1), 5–25.CrossRefGoogle Scholar
  106. 106.
    Feldman, R., & Dagan, I, (1995). Knowledge discovery in textual databases (kdt). In The proceeding of the first international conference on knowledge discovery and data mining (KDD-95).Google Scholar
  107. 107.
    Freitag, D., & McCallum, A. (1999, July). Information extraction with HMMs and shrinkage. In Proceedings of the AAAI-99 workshop on machine learning for information extraction (pp. 31–36).Google Scholar
  108. 108.
    Pierrakos, D., Paliouras, G., Papatheodorou, C., & Spyropoulos, C. D. (2003). Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction, 13(4), 311–372.CrossRefGoogle Scholar
  109. 109.
    Lento, T., Welser, H. T., Gu, L., & Smith, M. (2006, May). The ties that blog: Examining the relationship between social ties and continued participation in the wallop weblogging system. In 3rd Annual Workshop on the Weblogging ecosystem (Vol. 12).Google Scholar
  110. 110.
    Patil, U. M., & Patil, J. B. (2012, August). Web data mining trends and techniques. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (pp. 961–965). ACM.Google Scholar
  111. 111.
    Ting, I. H., & Wu, H. J. (2009). Web mining techniques for on-line social networks analysis: An overview. In Web Mining Applications in E-commerce and E-services (pp. 169–179). Springer Berlin Heidelberg.Google Scholar
  112. 112.
    Nina, S. P., Rahaman, M., Bhuiyan, K., and Khandakar E. (2009). Pattern Discovery Of Web Usage Mining, International Conference On Computer Technology and Development, Vol. 1.Google Scholar
  113. 113.
    Kosala, R., & Blockeel, H. (2000). Web mining research: A survey. ACM SIGKDD Explorations Newsletter, 2(1), 1–15.CrossRefGoogle Scholar
  114. 114.
    Büchner, A. G., & Mulvenna, M. D. (1998). Discovering internet marketing intelligence through online analytical web usage mining. ACM Sigmod Record, 27(4), 54–61.CrossRefGoogle Scholar
  115. 115.
    Raju, E., & Sravanthi, K. (2012). Analysis of social networks using the techniques of web mining. International Journal of Advanced Research in Computer Science and Software Engineering, 2(10), 5.Google Scholar
  116. 116.
    Goodreau, S. M. (2007). Advances in exponential random graph (p*) models applied to a large social network. Social Networks, 29(2), 231–248.CrossRefGoogle Scholar
  117. 117.
    Kolari, P., & Joshi, A. (2004). Web mining: Research and practice. Computing in Science & Engineering, 6(4), 49–53.CrossRefGoogle Scholar
  118. 118.
    Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604–632.MathSciNetzbMATHCrossRefGoogle Scholar
  119. 119.
    Biswal, B. (2008). Web site optimization through mining user navigational patterns, web engineering and application. New Delhi: Narosa Publishing House.Google Scholar
  120. 120.
    Li, F. (2008). Extracting structure of web site based on hyperlink analysis, fourth international conference on wireless communication. Networking and Mobile Computing, 1–4.Google Scholar
  121. 121.
    Fang, X., & Sheng, O. (2004). LinkSelector: Web mining approach to hyperlink selection for web portals. ACM Transactions on Internet Technology, 4(2), 209–237.CrossRefGoogle Scholar
  122. 122.
    Brin, S., & Page, L. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer Networks, 56(18), 3825–3833.CrossRefGoogle Scholar
  123. 123.
    Bharat, K., & Henzinger, M. R. (1998, August). Improved algorithms for topic distillation in a hyperlinked environment. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 104–111). ACM.Google Scholar
  124. 124.
    Mladenic, D., Grobelnik, M. (1999). Predicting content from hyperlinks. In Proceedings of the 16th International ICML99 Workshop on Machine Learning in Text Data Analysis (pp. 109–113).Google Scholar
  125. 125.
    Berendt, B. (2002). Using site semantic to analyze, visualize and support navigation. Data Mining and Knowledge Discovery, 6, 37–59.MathSciNetCrossRefGoogle Scholar
  126. 126.
    Dai, H. Mobasher, B. (2003). A road map to more effective Web personalization; Integrating domain knowledge with Web usage mining. In Proceedings of the International Conference on Internet Computing (IC 2003), Las Vegas, Nevada.Google Scholar
  127. 127.
    Oberle, D., Berendt, B., Hotho, A., Gonzalez, J. (2003). Conceptual user tracking. Lecture notes on artificial intelligence (Vol. 2663, pp. 155–164).Google Scholar
  128. 128.
    Spiliopoulou, M., & Pohle, C. (2001). Data mining for measuring and improving the success of Web sites. Data Mining and Knowledge Discover, 5(1–2), 85–114.zbMATHCrossRefGoogle Scholar
  129. 129.
    Mishne, G. (2007, March). Using blog properties to improve retrieval. In ICWSM.Google Scholar
  130. 130.
    Jalali, M., Mustapha, N., Sulaiman, M. N., & Mamat, A. (2010). WebPUM: A Web-based recommendation system to predict user future movements. Expert Systems with Applications, 37(9), 6201–6212.CrossRefGoogle Scholar
  131. 131.
    Yu, J. X., Ou, Y., Zhang, C., & Zhang, S. (2005). Identifying interesting visitors through web log classification. IEEE Intelligent Systems, 20(3), 55–59.CrossRefGoogle Scholar
  132. 132.
    Bommepally, K., Glisa T.K., Prakash, J. J., Singh, R., and Murthy, H. A. (2010). Internet Activity Analysis through Proxy Log, National Conference on Communications (NCC), Chennai, India.Google Scholar
  133. 133.
    Suneetha, K. R., & Krishnamoorthi, R. (2010). Classification of web log data to identify interested user using decision trees. In Proceedings of the International Conference on Computing Communications and Information Technology Applications.Google Scholar
  134. 134.
    Bai, S., Han, Q., Liu, Q., & Gao, Z. (2009). Research of an algorithm based on web usage mining. In IEEE International Workshop on Intelligent Systems and Applications (pp. 1–4).Google Scholar
  135. 135.
    Lappas, G. (2011, July). From web mining to social multimedia mining. In Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on (pp. 336–343). IEEE.Google Scholar
  136. 136.
    Feldman, R. (2002). Link analysis: Current state of the art. In Tutorial at the KDD-02.Google Scholar
  137. 137.
    Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. In Technical Report. Stanford, CA: Stanford University.Google Scholar
  138. 138.
    Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
  139. 139.
    Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170–1182.CrossRefGoogle Scholar
  140. 140.
    O’Madadhain, J., Hutchins, J., & Smyth, P. (2005). Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explorations Newsletter, 7(2), 23–30.CrossRefGoogle Scholar
  141. 141.
    O’Madadhain, J., & Smyth, P. (2005, August). EventRank: A framework for ranking time-varying networks. In Proceedings of the 3rd international workshop on Link discovery (pp. 9–16). ACM.Google Scholar
  142. 142.
    Oh, H. J., Myaeng, S. H., & Lee, M. H. (2000, July). A practical hypertext catergorization method using links and incrementally available class information. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 264–271). ACM.Google Scholar
  143. 143.
    Chakrabarti, S., Dom, B., & Indyk, P. (1998, June). Enhanced hypertext categorization using hyperlinks. In ACM SIGMOD record (Vol. 27, No. 2, pp. 307–318). ACM.Google Scholar
  144. 144.
    Lafferty, J., McCallum, A., & Pereira, F. C. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML (pp. 282–289).Google Scholar
  145. 145.
    Neville, J., & Jensen, D. (2000, July). Iterative classification in relational data. In Proceedings of the AAAI-2000 workshop on learning statistical models from relational data (pp. 13–20).Google Scholar
  146. 146.
    Lu, Q., & Getoor, L. (2003, August). Link-based classification. In ICML (Vol. 3, pp. 496–503).Google Scholar
  147. 147.
    Dzeroski, S., & Lavrac, N. (1993). Inductive logic programming: Techniques and applications. New York: Routledge.Google Scholar
  148. 148.
    Bach, F. R., & Jordan, M. I. (2004). Learning spectral clustering. In Advances in neural information processing systems (pp. 305–312).Google Scholar
  149. 149.
    Tyler, J. R., Wilkinson, D. M., & Huberman, B. A. (2005). E-mail as spectroscopy: Automated discovery of community structure within organizations. The Information Society, 21(2), 143–153.CrossRefGoogle Scholar
  150. 150.
    Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087.MathSciNetzbMATHCrossRefGoogle Scholar
  151. 151.
    Ananthakrishna, R., Chaudhuri, S., & Ganti, V. (2002, August). Eliminating fuzzy duplicates in data warehouses. In Proceedings of the 28th international conference on Very Large Data Bases (pp. 586–597). VLDB Endowment.Google Scholar
  152. 152.
    Kalashnikov, D. V., Mehrotra, S., & Chen, Z. (2005, April). Exploiting relationships for domain-independent data cleaning. In SDM (pp. 262–273).Google Scholar
  153. 153.
    Bhattacharya, I., & Getoor, L. (2004, June). Iterative record linkage for cleaning and integration. In Proceedings of the 9th ACM SIGMOD workshop on research issues in data mining and knowledge discovery (pp. 11–18). ACM.Google Scholar
  154. 154.
    Dong, X., Halevy, A., & Madhavan, J. (2005, June). Reference reconciliation in complex information spaces. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data (pp. 85-96). ACM.Google Scholar
  155. 155.
    Li, X., Morie, P., & Roth, D. (2005). Semantic integration in text: From ambiguous names to identifiable entities. AI Magazine, 26(1), 45.Google Scholar
  156. 156.
    Domingos, P. (2004). Multi-relational record linkage. In Proceedings of the KDD-2004 workshop on multi-relational data mining.Google Scholar
  157. 157.
    Pasula, H., Marthi, B., Milch, B., Russell, S., & Shpitser, I. (2002). Identity uncertainty and citation matching. In Advances in neural information processing systems (pp. 1401–1408).Google Scholar
  158. 158.
    Culotta, A., & McCallum, A. (2005, October). Joint deduplication of multiple record types in relational data. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 257–258). ACM.Google Scholar
  159. 159.
    Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(1), 993–1022.zbMATHGoogle Scholar
  160. 160.
    Gupta, N., & Singh, A. (2014, December). A novel strategy for link prediction in social networks. In Proceedings of the 2014 CoNEXT on student workshop (pp. 12–14). ACM.Google Scholar
  161. 161.
    Al Hasan, M., & Zaki, M. J. (2011). A survey of link prediction in social networks. In Social network data analytics (pp. 243–275). Springer US.Google Scholar
  162. 162.
    Liben-Nowell, David, & Kleinberg, Jon. (2007). The link prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031.CrossRefGoogle Scholar
  163. 163.
    Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.MathSciNetzbMATHCrossRefGoogle Scholar
  164. 164.
    Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.CrossRefGoogle Scholar
  165. 165.
    Bliss, C. A., Frank, M. R., Danforth, C. M., & Dodds, P. S. (2014). An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science, 5(5), 750–764.MathSciNetCrossRefGoogle Scholar
  166. 166.
    Chebotarev, P., & Shamis, E. (2006). The matrix-forest theorem and measuring relations in small social groups. arXiv preprint math/0602070.Google Scholar
  167. 167.
    Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., & Elovici, Y. (2011, October). Link prediction in social networks using computationally efficient topological features. In Privacy, security, risk and trust (PASSAT) and 2011 IEEE third international conference on social computing (SocialCom), 2011 IEEE third international conference on (pp. 73–80). IEEE.Google Scholar
  168. 168.
    Fire, M., Tenenboim-Chekina, L., Puzis, R., Lesser, O., Rokach, L., & Elovici, Y. (2013). Computationally efficient link prediction in a variety of social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 5(1), 10.Google Scholar
  169. 169.
    Popescul, A., & Ungar, L. H. (2003, August). Statistical relational learning for link prediction. In IJCAI workshop on learning statistical models from relational data (Vol. 2003).Google Scholar
  170. 170.
    O’Madadhain, J., Smyth, P., & Adamic, L. (2005, February). Learning predictive models for link formation. In International sunbelt social network conference.Google Scholar
  171. 171.
    Getoor, L. (2003). Link mining: A new data mining challenge. ACM SIGKDD Explorations Newsletter, 5(1), 84–89.CrossRefGoogle Scholar
  172. 172.
    Rattigan, M. J., & Jensen, D. (2005). The case for anomalous link discovery. ACM SIGKDD Explorations Newsletter, 7(2), 41–47.CrossRefGoogle Scholar
  173. 173.
    Chellappa, R., & Jain, A. (1993). Markov random fields. Theory and application. Boston: Academic Press, 1993, edited by Chellappa, Rama; Jain, Anil, 1.Google Scholar
  174. 174.
    Taskar, B., Wong, M. F., Abbeel, P., & Koller, D. (2003). Link prediction in relational data. In Advances in neural information processing systems.Google Scholar
  175. 175.
    Domingos, P., & Richardson, M. (2004). Markov logic: A unifying framework for statistical relational learning. In ICML-2004 Workshop on Statistical Relational Learning (Vol. 1, pp. 49–54).Google Scholar
  176. 176.
    Alavijeh, Z. Z. (2015). The application of link mining in social network analysis. Advances in Computer Science: An International Journal, 4(3), 64–69.Google Scholar
  177. 177.
    Inokuchi, A., Washio, T., & Motoda, H. (2000). An apriori-based algorithm for mining frequent substructures from graph data. In Principles of data mining and knowledge discovery (pp. 13–23). Springer Berlin Heidelberg.Google Scholar
  178. 178.
    Kuramochi, M., & Karypis, G. (2001). Frequent subgraph discovery. In Data Mining, 2001. ICDM 2001, Proceedings IEEE international conference on (pp. 313–320). IEEE.Google Scholar
  179. 179.
    Yan, X., & Han, J. (2002). gspan: Graph-based substructure pattern mining. In Data mining, 2002. ICDM 2003. Proceedings. 2002 IEEE international conference on (pp. 721–724). IEEE.Google Scholar
  180. 180.
    Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proceedings of the 20th international conference very large data bases, VLDB (Vol. 1215, pp. 487–499).Google Scholar
  181. 181.
    Otero, R., & Tamaddoni-Nezhad, A. (1992). In S. Muggleton (Ed.), Inductive logic programming (Vol. 38, pp. 281–298). London: Academic Press.Google Scholar
  182. 182.
    Matsuda, T., Horiuchi, T., Motoda, H., & Washio, T. (2000). Extension of graph-based induction for general graph structured data. In Knowledge discovery and data mining. Current issues and new applications (pp. 420–431). Springer Berlin Heidelberg.Google Scholar
  183. 183.
    Cook, D. J., & Holder, L. B. (1994). Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research, 1, 231–255.Google Scholar
  184. 184.
    Holder, L. B., & Cook, D. J. (2009). Graph-based data mining. Encyclopedia of Data Warehousing and Mining, 2, 943–949.CrossRefGoogle Scholar
  185. 185.
    Yoshida, K., Motoda, H., & Indurkhya, N. (1994). Graph-based induction as a unified learning framework. Applied Intelligence, 4(3), 297–316.CrossRefGoogle Scholar
  186. 186.
    King, R. D., Muggleton, S. H., Srinivasan, A., & Sternberg, M. J. (1996). Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences, 93(1), 438–442.CrossRefGoogle Scholar
  187. 187.
    Gärtner, T., Driessens, K., & Ramon, J. (2002). Exponential and geometric kernels for graphs. In NIPS workshop on unreal data: Principles of modeling nonvectorial Data (Vol. 5, pp. 49–58).Google Scholar
  188. 188.
    Kashima, H., & Inokuchi, A. (2002, July). Kernels for graph classification. In ICDM workshop on active mining (Vol. 2002).Google Scholar
  189. 189.
    Yin, H., Wong, S., Xu, J., & Wong, C. K. (2002). Urban traffic flow prediction using a fuzzy-neural approach. Transportation Research Part C: Emerging Technologies, 10(2), 85–98.CrossRefGoogle Scholar
  190. 190.
    Kazienko, P., Musiał, K., & Kajdanowicz, T. (2011). Multidimensional social network in the social recommender system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 41(4), 746–759.CrossRefGoogle Scholar
  191. 191.
    Kunegis, J., Lommatzsch, A., & Bauckhage, C. (2009, April). The slashdot zoo: Mining a social network with negative edges. In Proceedings of the 18th international conference on World wide web (pp. 741–750). ACM.Google Scholar
  192. 192.
    Zhang, Z. K., Zhou, T., & Zhang, Y. C. (2010). Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs. Physica A: Statistical Mechanics and its Applications, 389(1), 179–186.MathSciNetCrossRefGoogle Scholar
  193. 193.
    Carnegie, J. K., Kubica, J., Moore, A., & Schneider, J. (2003). Tractable Group Detection on Large Link Data Sets. In The third IEEE international conference on data mining.Google Scholar
  194. 194.
    Kubica, J., Moore, A., Schneider, J., & Yang, Y. (2002, July). Stochastic link and group detection. In Proceedings of the national conference on artificial intelligence (pp. 798–806). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Google Scholar
  195. 195.
    Adibi, J., Chalupsky, H., Melz, E., & Valente, A. (2004, July). The KOJAK group finder: Connecting the dots via integrated knowledge-based and statistical reasoning. In Proceedings of the national conference on artificial intelligence (pp. 800–807). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Google Scholar
  196. 196.
    Wang, X., Mohanty, N., & McCallum, A. (2005, August). Group and topic discovery from relations and text. In Proceedings of the 3rd international workshop on Link discovery (pp. 28–35). ACM.Google Scholar
  197. 197.
    Carpenter, T., Karakostas, G., & Shallcross, D. (2002). Practical issues and algorithms for analyzing terrorist networks. In Proceedings of the western simulation multiconference.Google Scholar
  198. 198.
    Huang, Z., & Lin, D. K. (2009). The time-series link prediction problem with applications in communication surveillance. INFORMS Journal on Computing, 21(2), 286–303.CrossRefGoogle Scholar
  199. 199.
    Scripps, J., Nussbaum, R., Tan, P. N., & Esfahanian, A. H. (2011). Link-based network mining. In Structural analysis of complex networks (pp. 403–419). Boston: Birkhäuser.Google Scholar
  200. 200.
    Basuchowdhuri, P., & Chen, J. (2010, August). Detecting communities using social ties. In Granular Computing (GrC), 2010 IEEE International Conference on (pp. 55-60). IEEE.Google Scholar
  201. 201.
    Sugimoto, C., Hank, C., Bowman, T., & Pomerantz, J. (2015). Friend or faculty: Social networking sites, dual relationships, and context collapse in higher education. First Monday. doi: 10.5210/fm.v20i3.5387.Google Scholar
  202. 202.
    Scott, J., & Carrington, P. J. (2011). The SAGE handbook of social network analysis. Thousand Oaks: SAGE Publications.Google Scholar
  203. 203.
    Laumann, E. O., Marsden, P. V., & Prensky, D. (1989). The boundary specification problem in network analysis. Research Methods in Social Network Analysis, 61, 87.Google Scholar
  204. 204.
    Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895.CrossRefGoogle Scholar
  205. 205.
    Yu, S., & Kak, S. (2012). A survey of prediction using social media. arXiv preprint arXiv:1203.1647.
  206. 206.
    Kemp, C., Griffiths, T. L., & Tenenbaum, J. B. (2004). Discovering latent classes in relational data. In Technical Report AI Memo 2004-019. MIT.Google Scholar
  207. 207.
    Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., & Ueda, N. (2006, July). Learning systems of concepts with an infinite relational model. In AAAI (Vol. 3, p. 5).Google Scholar
  208. 208.
    Kurihara, K., Kameya, Y., & Sato, T. (2006). A frequency-based stochastic blockmodel. Bernoulli (R (e1, e2), 1(1), N2.Google Scholar
  209. 209.
    Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (2006). Stochastic block models of mixed membership. Bayesian Analysis, 1(1), 1–23.MathSciNetCrossRefGoogle Scholar
  210. 210.
    De Laat, M. (2002, January). Network and content analysis in an online community discourse. In Proceedings of the conference on computer support for collaborative learning: Foundations for a CSCL community (pp. 625–626). International Society of the Learning Sciences.Google Scholar
  211. 211.
    Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology, 1(1), 49–80.CrossRefGoogle Scholar
  212. 212.
    Wolfe, A. P., & Jensen, D. (2004). Playing multiple roles: Discovering overlapping roles in social networks. In ICML-04 workshop on statistical relational learning and its connections to other fields (p. 75).Google Scholar
  213. 213.
    Choudhary, P., & Singh, U. (2015). A survey on social network analysis for counter-terrorism. International Journal of Computer Applications, 112(9), 24–29.Google Scholar
  214. 214.
    Campbell, W. M., Dagli, C. K., & Weinstein, C. J. (2013). Social network analysis with content and graphs. Lincoln Laboratory Journal, 20(1), 61–81.Google Scholar
  215. 215.
    Youtube. https://www.youtube.com/yt/press/statistics.html. Accessed September 1, 2014.
  216. 216.
    Flickr, https://www.flickr.com/photos/franckmichel/6855169886/. Accessed September 1, 2014.
  217. 217.
    By the Numbers: 400 Amazing Facebook  Statistics and Facts. http://expandedramblings.com/index.php/by-the-numbers-17-amazing-facebook-stats/2/#.VBFGlMKSxrI. Accessed September 1, 2014.
  218. 218.
  219. 219.
  220. 220.
    Szabo, G., & Huberman, B. A. (2010). Predicting the popularity of online content. Communications of the ACM, 53(8), 80–88.CrossRefGoogle Scholar
  221. 221.
    Lerman, K., & Galstyan, A. (2008, August). Analysis of social voting patterns on digg. In Proceedings of the first workshop on Online social networks (pp. 7–12). ACM.Google Scholar
  222. 222.
    Fiebert, M. S., Aliee, A., Yassami, H., & Dorethy, M. D. (2014). The life cycle of a facebook post. The Open Psychology Journal, 7(1), 18–19.CrossRefGoogle Scholar
  223. 223.
    Do, T. M. T., & Gatica-Perez, D. (2013). Human interaction discovery in smartphone proximity networks. Personal and Ubiquitous Computing, 17(3), 413–431.CrossRefGoogle Scholar
  224. 224.
    Olguın, D. O., Gloor, P. A., & Pentland, A. S. (2009). Capturing individual and group behavior with wearable sensors. In Proceedings of the 2009 aaai spring symposium on human behavior modeling, SSS (Vol. 9).Google Scholar
  225. 225.
    Weinstein, C., Campbell, W., Delaney, B., & O’Leary, G. (2009, March). Modeling and detection techniques for counter-terror social network analysis and intent recognition. In Aerospace conference, 2009 IEEE (pp. 1–16). IEEE.Google Scholar
  226. 226.
    Olguín, D. O., Waber, B. N., Kim, T., Mohan, A., Ara, K., & Pentland, A. (2009). Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(1), 43–55.CrossRefGoogle Scholar
  227. 227.
    Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–2188.CrossRefGoogle Scholar
  228. 228.
    Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40(16), 6266–6282.CrossRefGoogle Scholar
  229. 229.
    Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.CrossRefGoogle Scholar
  230. 230.
    Lansdall-Welfare, T., Lampos, V., & Cristianini, N. (2012, April). Effects of the Recession on Public Mood in the UK. In Proceedings of the 21st international conference companion on World Wide Web (pp. 1221–1226). ACM.Google Scholar
  231. 231.
    Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10, 178–185.Google Scholar
  232. 232.
    Culotta, A. (2010, July). Towards detecting influenza epidemics by analyzing Twitter messages. In Proceedings of the first workshop on social media analytics (pp. 115–122). ACM.Google Scholar
  233. 233.
    Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.CrossRefGoogle Scholar
  234. 234.
    Shamma, D. A., Kennedy, L., & Churchill, E. F. (2011, March). Peaks and persistence: modeling the shape of microblog conversations. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (pp. 355–358). ACM.Google Scholar
  235. 235.
    Weng, J., & Lee, B. S. (2011). Event detection in twitter. ICWSM, 11, 401–408.Google Scholar
  236. 236.
    Hu, M., Liu, S., Wei, F., Wu, Y., Stasko, J., & Ma, K. L. (2012, May). Breaking news on twitter. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2751–2754). ACM.Google Scholar
  237. 237.
    Sakaki, T., Okazaki, M., & Matsuo, Y. (2010, April). Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web (pp. 851–860). ACM.Google Scholar
  238. 238.
    Neubig, G., Matsubayashi, Y., Hagiwara, M., & Murakami, K. (2011, November). Safety Information Mining-What can NLP do in a disaster-. In IJCNLP (Vol. 11, pp. 965–973).Google Scholar
  239. 239.
    Chen, J., Nairn, R., Nelson, L., Bernstein, M., & Chi, E. (2010, April). Short and tweet: experiments on recommending content from information streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1185–1194). ACM.Google Scholar
  240. 240.
    Backstrom, L., Kleinberg, J., Lee, L., & Danescu-Niculescu-Mizil, C. (2013, February). Characterizing and curating conversation threads: expansion, focus, volume, re-entry. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 13–22). ACM.Google Scholar
  241. 241.
    Irfan, R., King, C. K., Grages, D., Ewen, S., Khan, S. U., Madani, S. A., et al. (2015). A survey on text mining in social networks. The Knowledge Engineering Review, 30(02), 157–170.CrossRefGoogle Scholar
  242. 242.
    Kurka, D. B., Godoy, A., & Von Zuben, F. J. (2015). Online social network analysis: A survey of research applications in computer science. arXiv preprint arXiv:1504.05655.
  243. 243.
    Yoo, K. (2012). Automatic document archiving for cloud storage using text mining-based topic identification technique. In Proceedings of international conference on information and computer application, Singapore (pp. 189–192).Google Scholar
  244. 244.
    Cimiano, P., Handschuh, S., & Staab, S. (2004, May). Towards the self-annotating web. In Proceedings of the 13th international conference on World Wide Web (pp. 462–471). ACM.Google Scholar
  245. 245.
    Mika, P. (2005). Ontologies are us: A unified model of social networks and semantics. In The Semantic WebISWC 2005 (pp. 522–536). Springer Berlin Heidelberg.Google Scholar
  246. 246.
    Finin, T., Ding, L., Zhou, L., & Joshi, A. (2005). Social networking on the semantic web. The Learning Organization, 12(5), 418–435.CrossRefGoogle Scholar
  247. 247.
    Friend of a Friend. https://en.wikipedia.org/wiki/Friend_of_a_friend. Accessed October, 2015.
  248. 248.
    Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284(5), 28–37.CrossRefGoogle Scholar
  249. 249.
    Maia, M., Almeida, J., & Almeida, V. (2008, April). Identifying user behavior in online social networks. In Proceedings of the 1st workshop on social network systems (pp. 1–6). ACM.Google Scholar
  250. 250.
    Adar, E., & Huberman, B. A. (2000). Free riding on Gnutella. First Monday, 5(10).Google Scholar
  251. 251.
    Feldman, M., Papadimitriou, C., Chuang, J., & Stoica, I. (2004, September). Free-riding and whitewashing in peer-to-peer systems. In Proceedings of the ACM SIGCOMM workshop on Practice and theory of incentives in networked systems (pp. 228–236). ACM.Google Scholar
  252. 252.
    Marques Neto, H. T., Almeida, J. M., Rocha, L. C., Meira, W., Guerra, P. H., & Almeida, V. A. (2004). A characterization of broadband user behavior and their e-business activities. ACM SIGMETRICS Performance Evaluation Review, 32(3), 3–13.CrossRefGoogle Scholar
  253. 253.
    Backstrom, L., Kumar, R., Marlow, C., Novak, J., & Tomkins, A. (2008, February). Preferential behavior in online groups. In Proceedings of the 2008 international conference on web search and data mining (pp. 117–128). ACM.Google Scholar
  254. 254.
    Agichtein, E., Brill, E., & Dumais, S. (2006, August). Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 19–26). ACM.Google Scholar
  255. 255.
    Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008, February). Finding high-quality content in social media. In Proceedings of the 2008 international conference on web search and data mining (pp. 183–194). ACM.Google Scholar
  256. 256.
    Fisher, D., Smith, M., & Welser, H. T. (2006, January). You are who you talk to: Detecting roles in usenet newsgroups. In System Sciences, 2006. HICSS’06. Proceedings of the 39th annual hawaii international conference on (Vol. 3, pp. 59b–59b). IEEE.Google Scholar
  257. 257.
    Menascé, D. A., Almeida, V. A., Fonseca, R., & Mendes, M. A. (2000). Business-oriented resource management policies for e-commerce servers. Performance Evaluation, 42(2), 223–239.zbMATHCrossRefGoogle Scholar
  258. 258.
    Oard, D. W., & Kim, J. (2001). Modeling information content using observable behavior. In Proceedings of the 64th annual conference of the American society for information science and technology (pp. 481–488). Washington.Google Scholar
  259. 259.
    Viswanath, B., Bashir, M. A., Crovella, M., Guha, S., Gummadi, K. P., Krishnamurthy, B., & Mislove, A. (2014, August). Towards detecting anomalous user behavior in online social networks. In Proceedings of the 23rd USENIX Security Symposium (USENIX Security)}.Google Scholar
  260. 260.
    Benevenuto, F., Rodrigues, T., Cha, M., & Almeida, V. (2009, November). Characterizing user behavior in online social networks. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (pp. 49–62). ACM.Google Scholar
  261. 261.
    Jin, L., Chen, Y., Wang, T., Hui, P., & Vasilakos, A. V. (2013). Understanding user behavior in online social networks: A survey. IEEE Communications Magazine, 51(9), 144–150.CrossRefGoogle Scholar
  262. 262.
    Tan, E., Guo, L., Chen, S., Zhang, X., & Zhao, Y. (2012, June). Spammer behavior analysis and detection in user generated content on social networks. In Distributed Computing Systems (ICDCS), 2012 IEEE 32nd International Conference on (pp. 305–314). IEEE.Google Scholar
  263. 263.
    Sato, Y., Utsuro, T., Murakami, Y., Fukuhara, T., Nakagawa, H., Kawada, Y., & Kando, N. (2008, April). Analysing features of Japanese splogs and characteristics of keywords. In Proceedings of the 4th international workshop on Adversarial information retrieval on the web (pp. 33–40). ACM.Google Scholar
  264. 264.
    Wang, Y. M., Ma, M., Niu, Y., & Chen, H. (2007, May). Spam double-funnel: Connecting web spammers with advertisers. In Proceedings of the 16th international conference on World Wide Web (pp. 291–300). ACM.Google Scholar
  265. 265.
    E-mail spam, http://en.wikipedia.org/wiki/E-mail spam. Accessed December, 2014.
  266. 266.
    Stringhini, G., Kruegel, C., & Vigna, G. (2010, December). Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference (pp. 1–9). ACM.Google Scholar
  267. 267.
    Gomes, L. H., Cazita, C., Almeida, J. M., Almeida, V., & Meira Jr, W. (2004, October). Characterizing a spam traffic. In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement (pp. 356–369). ACM.Google Scholar
  268. 268.
    Ramachandran, A., & Feamster, N. (2006). Understanding the network-level behavior of spammers. ACM SIGCOMM Computer Communication Review, 36(4), 291–302.CrossRefGoogle Scholar
  269. 269.
    Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000, July). An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 160–167). ACM.Google Scholar
  270. 270.
    Jung, J., & Sit, E. (2004, October). An empirical study of spam traffic and the use of DNS black lists. In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement (pp. 370–375). ACM.Google Scholar
  271. 271.
    Delany, M. (2007). Domain-based email authentication using public keys advertised in the DNS (DomainKeys). In RFC 4870, Network Working Group. IETF.Google Scholar
  272. 272.
    Xie, Y., Yu, F., Achan, K., Panigrahy, R., Hulten, G., & Osipkov, I. (2008, August). Spamming botnets: signatures and characteristics. In ACM SIGCOMM Computer Communication Review (Vol. 38, No. 4, pp. 171–182). ACM.Google Scholar
  273. 273.
    Hao, S., Syed, N. A., Feamster, N., Gray, A. G., & Krasser, S. (2009, August). Detecting Spammers with SNARE: Spatio-temporal Network-level Automatic Reputation Engine. In USENIX Security Symposium (Vol. 9).Google Scholar
  274. 274.
    Becchetti, L., Castillo, C., Donato, D., Leonardi, S., & Baezayates, R. (2006, December). Linkbased characterization and detection of web spam. In 2nd International workshop on adversarial information retrieval on the web, AIRWeb 2006-29th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR 2006.Google Scholar
  275. 275.
    Castillo, C., Donato, D., Gionis, A., Murdock, V., & Silvestri, F. (2007, July). Know your neighbors: Web spam detection using the web topology. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 423–430). ACM.Google Scholar
  276. 276.
    Gyongyi, Z., & Garcia-Molina, H. (2005). Web spam taxonomy. In First international workshop on adversarial information retrieval on the web (AIRWeb 2005).Google Scholar
  277. 277.
    Niu, Y., Chen, H., Hsu, F., Wang, Y. M., & Ma, M. (2007, February). A quantitative study of forum spamming using context-based analysis. In NDSS.Google Scholar
  278. 278.
    Kolari, P., Java, A., & Finin, T. (2006, May). Characterizing the splogosphere. In Proceedings of the 3rd annual workshop on weblogging ecosystem: Aggregation, analysis and dynamics, 15th World Wid Web conference. University of Maryland, Baltimore County.Google Scholar
  279. 279.
    Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010, October). @ spam: the underground on 140 characters or less. In Proceedings of the 17th ACM conference on computer and communications security (pp. 27–37). ACM.Google Scholar
  280. 280.
    Kolari, P., Finin, T., & Joshi, A. (2006, March). SVMs for the blogosphere: Blog identification and splog detection. In AAAI spring symposium: Computational approaches to analyzing weblogs (pp. 92–99).Google Scholar
  281. 281.
    Kolari, P., Java, A., Finin, T., Oates, T., & Joshi, A. (2006, July). Detecting spam blogs: A machine learning approach. In Proceedings of the national conference on artificial intelligence (Vol. 21, No. 2, p. 1351). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.Google Scholar
  282. 282.
    Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., & Zhao, B. Y. (2010, November). Detecting and characterizing social spam campaigns. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement (pp. 35–47). ACM.Google Scholar
  283. 283.
    Katayama, T., Utsuro, T., Sato, Y., Yoshinaka, T., Kawada, Y., & Fukuhara, T. (2009, April). An empirical study on selective sampling in active learning for splog detection. In Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web (pp. 29-36). ACM.Google Scholar
  284. 284.
    Lee, K., Caverlee, J., & Webb, S. (2010, July). Uncovering social spammers: social honeypots+ machine learning. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (pp. 435–442). ACM.Google Scholar
  285. 285.
    Lin, Y. R., Sundaram, H., Chi, Y., Tatemura, J., & Tseng, B. L. (2007, May). Splog detection using self-similarity analysis on blog temporal dynamics. In Proceedings of the 3rd international workshop on Adversarial information retrieval on the web (pp. 1–8). ACM.Google Scholar
  286. 286.
    Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009, June). Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1245–1254). ACM.Google Scholar
  287. 287.
    Rieder, B. (2013, May). Studying Facebook via data extraction: the Netvizz application. In Proceedings of the 5th Annual ACM Web Science Conference(pp. 346–355). ACM.Google Scholar
  288. 288.
    Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., & Crowcroft, J. (2012, February). The personality of popular facebook users. In Proceedings of the ACM 2012 conference on computer supported cooperative work (pp. 955–964). ACM.Google Scholar
  289. 289.
    Abdesslem, F. B., Parris, I., & Henderson, T. (2012). Reliable online social network data collection. In Computational Social Networks (pp. 183–210). Springer London.Google Scholar
  290. 290.
    Besmer, A., & Richter Lipford, H. (2010, April). Moving beyond untagging: photo privacy in a tagged world. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1563–1572). ACM.Google Scholar
  291. 291.
    Ozok, A. A., & Zaphiris, P. (2009). Online communities and social computing. New York: Springer.CrossRefGoogle Scholar
  292. 292.
    Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.CrossRefGoogle Scholar
  293. 293.
    Krasnova, H., Günther, O., Spiekermann, S., & Koroleva, K. (2009). Privacy concerns and identity in online social networks. Identity in the Information Society, 2(1), 39–63.CrossRefGoogle Scholar
  294. 294.
    Lampe, C., Ellison, N. B., & Steinfield, C. (2008, November). Changes in use and perception of Facebook. In Proceedings of the 2008 ACM conference on Computer supported cooperative work (pp. 721–730). ACM.Google Scholar
  295. 295.
    Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13(3), 134–140.CrossRefGoogle Scholar
  296. 296.
    Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability of the experience-sampling method. In Flow and the Foundations of Positive Psychology (pp. 35–54). Springer Netherlands.Google Scholar
  297. 297.
    Mancini, C., Thomas, K., Rogers, Y., Price, B. A., Jedrzejczyk, L., Bandara, A. K., … & Nuseibeh, B. (2009, September). From spaces to places: emerging contexts in mobile privacy. In Proceedings of the 11th international conference on Ubiquitous computing (pp. 1–10). ACM.Google Scholar
  298. 298.
    Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College students’ social networking experiences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227–238.CrossRefGoogle Scholar
  299. 299.
    Anthony, D., Henderson, T., & Kotz, D. (2007). Privacy in location-aware computing environments. IEEE Pervasive Computing, 4, 64–72.CrossRefGoogle Scholar
  300. 300.
    Schäfer, M. T. (2011). Bastard culture! How user participation transforms cultural production (p. 256). Amsterdam: Amsterdam University Press.CrossRefGoogle Scholar
  301. 301.
    Ugander, J., Karrer, B., Backstrom, L., & Marlow, C. (2011). The anatomy of the facebook social graph. arXiv preprint arXiv:1111.4503.
  302. 302.
    Leskovec, J. (2008). Dynamics of large networks. Doctoral Dissertation, Carnegie Mellon University, Pittsburgh.Google Scholar
  303. 303.
    Ahn, Y. Y., Han, S., Kwak, H., Moon, S., & Jeong, H. (2007, May). Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th international conference on World Wide Web (pp. 835–844). ACM.Google Scholar
  304. 304.
    DATASIFT, http://datasift.com/. Accessed September, 2014.
  305. 305.
    GNIP, http://gnip.com/. Accessed September, 2014.
  306. 306.
    Customer relationship management, https://en.wikipedia.org/wiki/Customer_relationship_management. Accessed September, 2014.
  307. 307.
    Garg, S., Gupta, T., Carlsson, N., & Mahanti, A. (2009, November). Evolution of an online social aggregation network: an empirical study. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (pp. 315–321). ACM.Google Scholar
  308. 308.
    Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring user influence in twitter: The million follower fallacy. ICWSM, 10(10–17), 30.Google Scholar
  309. 309.
    Ghosh, S., Korlam, G., & Ganguly, N. (2010, June). The Effects of Restrictions on Number of Connections in OSNs: A Case-Study on Twitter. In WOSN.Google Scholar
  310. 310.
    Ghosh, S., Zafar, M. B., Bhattacharya, P., Sharma, N., Ganguly, N., & Gummadi, K. (2013, October). On sampling the wisdom of crowds: Random vs. expert sampling of the twitter stream. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 1739–1744). ACM.Google Scholar
  311. 311.
    González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2014). Assessing the bias in samples of large online networks. Social Networks, 38, 16–27.CrossRefGoogle Scholar
  312. 312.
    Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose. arXiv preprint arXiv:1306.5204.
  313. 313.
    Lindamood, J., Heatherly, R., Kantarcioglu, M., & Thuraisingham, B. (2009, April). Inferring private information using social network data. In Proceedings of the 18th international conference on World wide web (pp. 1145–1146). ACM.Google Scholar
  314. 314.
    Gyarmati, L., & Trinh, T. A. (2010). Measuring user behavior in online social networks. IEEE Network, 24(5), 26–31.CrossRefGoogle Scholar
  315. 315.
    Iachello, G., Smith, I., Consolvo, S., Chen, M., & Abowd, G. D. (2005, July). Developing privacy guidelines for social location disclosure applications and services. In Proceedings of the 2005 symposium on Usable privacy and security (pp. 65–76). ACM.Google Scholar
  316. 316.
    Prabaker, M., Rao, J., Fette, I., Kelley, P., Cranor, L., Hong, J., & Sadeh, N. (2007, September). Understanding and capturing people’s privacy policies in a people finder application. In Proceedings of the workshop ubicomp privacy.Google Scholar
  317. 317.
    Foller.me, http://foller.me/. Accessed September, 2014.
  318. 318.
    TAPoR, http://www.tapor.ca/?id=468. Accessed September, 2014.
  319. 319.
    Truthy, http://truthy.indiana.edu/. Accessed September, 2014.
  320. 320.
    Tweet Archivist, http://www.tweetarchivist.com/. Accessed September, 2014.
  321. 321.
    TweetStats, http://www.tweetstats.com/. Accessed September, 2014.
  322. 322.
    Twiangulate, http://twiangulate.com/search/. Accessed September, 2014.
  323. 323.
    Twitonomy, http://www.twitonomy.com/. Accessed September, 2014.
  324. 324.
    YourTwapperKeeper, http://mappingonlinepublics.net/tag/yourtwapperkeeper/. Accessed September, 2014.
  325. 325.
    Tweetnest, https://github.com/graulund/tweetnest. Accessed September, 2014.
  326. 326.
    NodeXL, http://nodexl.codeplex.com/. Accessed September, 2014.
  327. 327.
    Netlytic, https://netlytic.org/. Accessed September, 2014.
  328. 328.
    Textexture, http://textexture.com/. Accessed September, 2014.
  329. 329.
    ThinkUp, https://www.thinkup.com/. Accessed September, 2014.
  330. 330.
    Aggarwal, C. C., & Wang, H. (2011). Text mining in social networks. In Social Network Data Analytics (pp. 353–378). Springer US.Google Scholar
  331. 331.
    ClusterHQ, https://clusterhq.com/flocker/introduction/. Accessed November, 2015.
  332. 332.
    Followthehashtag, http://www.followthehashtag.com/. Accessed November, 2015.
  333. 333.
    iSciencemaps, http://maps.iscience.deusto.es/. Accessed November, 2015.
  334. 334.
    QSR, http://www.qsrinternational.com/trial-nvivo. Accessed November, 2015.
  335. 335.
    Mozdeh, http://mozdeh.wlv.ac.uk/. Accessed November, 2015.
  336. 336.
    The Chorus project. http://chorusanalytics.co.uk/. Accessed November, 2015.
  337. 337.
    Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), 12–27.CrossRefGoogle Scholar
  338. 338.
    Stonebraker, Michael. (2010). SQL databases v. NoSQL databases. Communications of the ACM, 53(4), 10–11.CrossRefGoogle Scholar
  339. 339.
    Gjoka, M., Kurant, M., Butts, C. T., & Markopoulou, A. (2010, March). Walking in Facebook: A case study of unbiased sampling of OSNs. In INFOCOM, 2010 Proceedings IEEE (pp. 1–9). IEEE.Google Scholar
  340. 340.
    Lewis, K., Kaufman, J., & Christakis, N. (2008). The taste for privacy: An analysis of college student privacy settings in an online social network. Journal of Computer-Mediated Communication, 14(1), 79–100.CrossRefGoogle Scholar
  341. 341.
    Doddington, G. R., Mitchell, A., Przybocki, M. A., Ramshaw, L. A., Strassel, S., & Weischedel, R. M. (2004, May). The Automatic Content Extraction (ACE) Program-Tasks, Data, and Evaluation. In LREC (Vol. 2, p. 1).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Symbiosis Centre for Information Technology (SCIT)Symbiosis International University (SIU)PuneIndia

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