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Wireless Personal Communications
Published in: Wireless Personal Communications 3/2017

18-08-2017

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

Authors: Anuradha Goswami, Ajey Kumar

Published in: Wireless Personal Communications | Issue 3/2017

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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.
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Literature
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.
2.
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.
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.
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.
6.
Flake, G. W., Tarjan, R. E., & Tsioutsiouliklis, K. (2004). Graph clustering and minimum cut trees. Internet Mathematics, 1(4), 385–408.MathSciNetMATH
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.MathSciNetMATH
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.
9.
Newman, M. E. (2004). Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2), 321–330.
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.
11.
12.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
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).
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).
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.
16.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.
17.
Shin, D. H., & Shin, Y. J. (2011). Why do people play social network games? Computers in Human Behavior, 27(2), 852–861.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
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.
30.
Karimzadehgan, M., Agrawal, M., & Zhai, C. (2009). Towards advertising on social networks. Information Retrieval and Advertising (IRA-2009), 28.
31.
Huberman, B. A., Romero, D. M., & Wu, F. (2008). Social networks that matter: Twitter under the microscope. Available at SSRN 1313405.
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.
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.
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.
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.
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.
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.
38.
Dall’Asta, L., Marsili, M., & Pin, P. (2012). Collaboration in social networks. Proceedings of the National Academy of Sciences, 109(12), 4395–4400.
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.
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.
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.
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.
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.
44.
45.
Baldi, P., Frasconi, P., & Smyth, P. (2003). Modeling the internet and the web—probabilistic methods and algorithms. Chichester, West Sussex: Wiley.
46.
Barabási, A. L., Albert, R., & Jeong, H. (1999). The diameter of the world wide web. Nature, 401(6749), 130–131.
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.
48.
Krapivsky, P. L., Redner, S., & Leyvraz, F. (2000). Connectivity of growing random networks. Physical Review Letters, 85(21), 4629.
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.
50.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.MATH
51.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
52.
de Sola Pool, I., & Kochen, M. (1979). Contacts and influence. Social Networks, 1(1), 5–51.MathSciNet
53.
Milgram, S. (1967). The small world problem. Psychology Today, 2(1), 60–67.
54.
Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.MATH
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.
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.
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.
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.
59.
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.
61.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440–442.MATH
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.
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.MathSciNetMATH
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).
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).
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.
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.
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.
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.
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.
71.
Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. ACM SIGKDD Explorations Newsletter, 7(2), 3–12.
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.
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.
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.
75.
Guber, T. (1993). A translational approach to portable ontologies. Knowledge Acquisition, 5(2), 199–229.
76.
Antoniou, G., & Van Harmelen, F. (2004). A semantic web primer. Cambridge: MIT Press.
77.
Wennerberg, P. O. (2005). Ontology based knowledge discovery in Social Networks. Final Report, JRC Joint Research Center, 1–34.
78.
79.
Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. Canada: Empirical Press.
80.
Hoser, B., Hotho, A., Jäschke, R., Schmitz, C., & Stumme, G. (2006). Semantic network analysis of ontologies (pp. 514–529). Berlin, Heidelberg: Springer.
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.
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.
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.
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.
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.
86.
Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge: MIT Press.
87.
Chakrabarti, S. (2003). Mining the web: Discovering knowledge from hypertext data. San Francisco: Morgan Kaufmann Publishers.
88.
Chen, H., & Chau, M. (2004). Web mining: Machine learning for web applications. Annual Review of Information Science and Technology (ARIST), 38, 289–329.
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.
90.
Faca, F. M., & Lanzi, P. L. (2005). Mining interesting knowledge from weblogs: A survey. Data Knowledge Engineering, 53(3), 225–241.
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.
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.
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.
94.
95.
Mr. Dushyant B. Rathod, Dr. Samrat Khanna, “A Review on Emerging Trends of Web Mining and its Applications” ISSN: 2321-9939.
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.
97.
Sandhya., Chaturvedi, M. (2013). A survey on web mining algorithms. The International Journal Of Engineering And Science (IIJES), 2(3), 25–30.
98.
Zhang, Y., Yu, J. X., & Hou, J. (2005). Web communities: Analysis and construction. Berlin: Springer.
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.
100.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31(3), 264–323.
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.
102.
Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-scale sentiment analysis for news and blogs. ICWSM, 7(21), 219–222.
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.
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).
105.
Frank, E., Trigg, L. E., Holmes, G., & Witten, I. H. (1998). Naive Bayes for regression. Machine Learning, 41(1), 5–25.
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).
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).
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.
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).
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.
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.
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.
113.
Kosala, R., & Blockeel, H. (2000). Web mining research: A survey. ACM SIGKDD Explorations Newsletter, 2(1), 1–15.
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.
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.
116.
Goodreau, S. M. (2007). Advances in exponential random graph (p*) models applied to a large social network. Social Networks, 29(2), 231–248.
117.
Kolari, P., & Joshi, A. (2004). Web mining: Research and practice. Computing in Science & Engineering, 6(4), 49–53.
118.
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604–632.MathSciNetMATH
119.
Biswal, B. (2008). Web site optimization through mining user navigational patterns, web engineering and application. New Delhi: Narosa Publishing House.
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.
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.
122.
Brin, S., & Page, L. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer Networks, 56(18), 3825–3833.
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.
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).
125.
Berendt, B. (2002). Using site semantic to analyze, visualize and support navigation. Data Mining and Knowledge Discovery, 6, 37–59.MathSciNet
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.
127.
Oberle, D., Berendt, B., Hotho, A., Gonzalez, J. (2003). Conceptual user tracking. Lecture notes on artificial intelligence (Vol. 2663, pp. 155–164).
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.MATH
129.
Mishne, G. (2007, March). Using blog properties to improve retrieval. In ICWSM.
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.
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.
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.
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.
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).
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.
136.
Feldman, R. (2002). Link analysis: Current state of the art. In Tutorial at the KDD-02.
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.
138.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.
139.
Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170–1182.
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.
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.
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.
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.
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).
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).
146.
Lu, Q., & Getoor, L. (2003, August). Link-based classification. In ICML (Vol. 3, pp. 496–503).
147.
Dzeroski, S., & Lavrac, N. (1993). Inductive logic programming: Techniques and applications. New York: Routledge.
148.
Bach, F. R., & Jordan, M. I. (2004). Learning spectral clustering. In Advances in neural information processing systems (pp. 305–312).
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.
150.
Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087.MathSciNetMATH
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.
152.
Kalashnikov, D. V., Mehrotra, S., & Chen, Z. (2005, April). Exploiting relationships for domain-independent data cleaning. In SDM (pp. 262–273).
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.
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.
155.
Li, X., Morie, P., & Roth, D. (2005). Semantic integration in text: From ambiguous names to identifiable entities. AI Magazine, 26(1), 45.
156.
Domingos, P. (2004). Multi-relational record linkage. In Proceedings of the KDD-2004 workshop on multi-relational data mining.
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).
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.
159.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(1), 993–1022.MATH
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.
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.
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.
163.
Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.MathSciNetMATH
164.
Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.
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.MathSciNet
166.
Chebotarev, P., & Shamis, E. (2006). The matrix-forest theorem and measuring relations in small social groups. arXiv preprint math/0602070.
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.
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.
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).
170.
O’Madadhain, J., Smyth, P., & Adamic, L. (2005, February). Learning predictive models for link formation. In International sunbelt social network conference.
171.
Getoor, L. (2003). Link mining: A new data mining challenge. ACM SIGKDD Explorations Newsletter, 5(1), 84–89.
172.
Rattigan, M. J., & Jensen, D. (2005). The case for anomalous link discovery. ACM SIGKDD Explorations Newsletter, 7(2), 41–47.
173.
Chellappa, R., & Jain, A. (1993). Markov random fields. Theory and application. Boston: Academic Press, 1993, edited by Chellappa, Rama; Jain, Anil, 1.
174.
Taskar, B., Wong, M. F., Abbeel, P., & Koller, D. (2003). Link prediction in relational data. In Advances in neural information processing systems.
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).
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.
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.
178.
Kuramochi, M., & Karypis, G. (2001). Frequent subgraph discovery. In Data Mining, 2001. ICDM 2001, Proceedings IEEE international conference on (pp. 313–320). IEEE.
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.
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).
181.
Otero, R., & Tamaddoni-Nezhad, A. (1992). In S. Muggleton (Ed.), Inductive logic programming (Vol. 38, pp. 281–298). London: Academic Press.
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.
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.
184.
Holder, L. B., & Cook, D. J. (2009). Graph-based data mining. Encyclopedia of Data Warehousing and Mining, 2, 943–949.
185.
Yoshida, K., Motoda, H., & Indurkhya, N. (1994). Graph-based induction as a unified learning framework. Applied Intelligence, 4(3), 297–316.
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.
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).
188.
Kashima, H., & Inokuchi, A. (2002, July). Kernels for graph classification. In ICDM workshop on active mining (Vol. 2002).
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.
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.
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.
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.MathSciNet
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.
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.
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.
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.
197.
Carpenter, T., Karakostas, G., & Shallcross, D. (2002). Practical issues and algorithms for analyzing terrorist networks. In Proceedings of the western simulation multiconference.
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.
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.
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.
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.CrossRef
202.
Scott, J., & Carrington, P. J. (2011). The SAGE handbook of social network analysis. Thousand Oaks: SAGE Publications.
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.
204.
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895.
205.
206.
Kemp, C., Griffiths, T. L., & Tenenbaum, J. B. (2004). Discovering latent classes in relational data. In Technical Report AI Memo 2004-019. MIT.
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).
208.
Kurihara, K., Kameya, Y., & Sato, T. (2006). A frequency-based stochastic blockmodel. Bernoulli (R (e1, e2), 1(1), N2.
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.MathSciNet
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.
211.
Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology, 1(1), 49–80.
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).
213.
Choudhary, P., & Singh, U. (2015). A survey on social network analysis for counter-terrorism. International Journal of Computer Applications, 112(9), 24–29.
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.
215.
216.
217.
218.
219.
220.
Szabo, G., & Huberman, B. A. (2010). Predicting the popularity of online content. Communications of the ACM, 53(8), 80–88.
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.
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.
223.
Do, T. M. T., & Gatica-Perez, D. (2013). Human interaction discovery in smartphone proximity networks. Personal and Ubiquitous Computing, 17(3), 413–431.
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).
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.
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.
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.
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.
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.
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.
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.
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.
233.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
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.
235.
Weng, J., & Lee, B. S. (2011). Event detection in twitter. ICWSM, 11, 401–408.
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.
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.
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).
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.
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.
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.
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.
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).
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.
245.
Mika, P. (2005). Ontologies are us: A unified model of social networks and semantics. In The Semantic Web–ISWC 2005 (pp. 522–536). Springer Berlin Heidelberg.
246.
Finin, T., Ding, L., Zhou, L., & Joshi, A. (2005). Social networking on the semantic web. The Learning Organization, 12(5), 418–435.
247.
248.
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284(5), 28–37.
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.
250.
Adar, E., & Huberman, B. A. (2000). Free riding on Gnutella. First Monday, 5(10).
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.
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.
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.
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.
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.
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.
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.MATH
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.
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)}.
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.
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.
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.
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.
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.
265.
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.
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.
268.
Ramachandran, A., & Feamster, N. (2006). Understanding the network-level behavior of spammers. ACM SIGCOMM Computer Communication Review, 36(4), 291–302.
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.
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.
271.
Delany, M. (2007). Domain-based email authentication using public keys advertised in the DNS (DomainKeys). In RFC 4870, Network Working Group. IETF.
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.
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).
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.
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.
276.
Gyongyi, Z., & Garcia-Molina, H. (2005). Web spam taxonomy. In First international workshop on adversarial information retrieval on the web (AIRWeb 2005).
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
289.
Abdesslem, F. B., Parris, I., & Henderson, T. (2012). Reliable online social network data collection. In Computational Social Networks (pp. 183–210). Springer London.
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.
291.
Ozok, A. A., & Zaphiris, P. (2009). Online communities and social computing. New York: Springer.
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.
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.
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.
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.
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.
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.
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.
299.
Anthony, D., Henderson, T., & Kotz, D. (2007). Privacy in location-aware computing environments. IEEE Pervasive Computing, 4, 64–72.
300.
Schäfer, M. T. (2011). Bastard culture! How user participation transforms cultural production (p. 256). Amsterdam: Amsterdam University Press.
301.
Ugander, J., Karrer, B., Backstrom, L., & Marlow, C. (2011). The anatomy of the facebook social graph. arXiv preprint arXiv:​1111.​4503.
302.
Leskovec, J. (2008). Dynamics of large networks. Doctoral Dissertation, Carnegie Mellon University, Pittsburgh.
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.
304.
305.
306.
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.
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.
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.
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.
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.
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.
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.
314.
Gyarmati, L., & Trinh, T. A. (2010). Measuring user behavior in online social networks. IEEE Network, 24(5), 26–31.
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.
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.
317.
318.
319.
320.
321.
322.
323.
324.
325.
326.
327.
328.
329.
330.
Aggarwal, C. C., & Wang, H. (2011). Text mining in social networks. In Social Network Data Analytics (pp. 353–378). Springer US.
331.
332.
333.
334.
335.
336.
337.
Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), 12–27.
338.
Stonebraker, Michael. (2010). SQL databases v. NoSQL databases. Communications of the ACM, 53(4), 10–11.
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.
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.
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).
Metadata
Title
Challenges in the Analysis of Online Social Networks: A Data Collection Tool Perspective
Authors
Anuradha Goswami
Ajey Kumar
Publication date
18-08-2017
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2017
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4712-3

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