CFP last date
15 November 2024
Call for Paper
December Edition
IJAIS solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 15 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

An Overview on User Profiling in Online Social Networks

by Vasanthakumar G. U., Sunithamma K., P. Deepa Shenoy, Venugopal K. R.
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 8
Year of Publication: 2017
Authors: Vasanthakumar G. U., Sunithamma K., P. Deepa Shenoy, Venugopal K. R.
10.5120/ijais2017451639

Vasanthakumar G. U., Sunithamma K., P. Deepa Shenoy, Venugopal K. R. . An Overview on User Profiling in Online Social Networks. International Journal of Applied Information Systems. 11, 8 ( Jan 2017), 25-42. DOI=10.5120/ijais2017451639

@article{ 10.5120/ijais2017451639,
author = { Vasanthakumar G. U., Sunithamma K., P. Deepa Shenoy, Venugopal K. R. },
title = { An Overview on User Profiling in Online Social Networks },
journal = { International Journal of Applied Information Systems },
issue_date = { Jan 2017 },
volume = { 11 },
number = { 8 },
month = { Jan },
year = { 2017 },
issn = { 2249-0868 },
pages = { 25-42 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number8/960-2017451639/ },
doi = { 10.5120/ijais2017451639 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:04:34.927419+05:30
%A Vasanthakumar G. U.
%A Sunithamma K.
%A P. Deepa Shenoy
%A Venugopal K. R.
%T An Overview on User Profiling in Online Social Networks
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 8
%P 25-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors.

References
  1. Zhang, Lejun, Tongxin Zhou, Qi Zhixin, Lin Guo and Li Xu. “The research on e-mail Users' behavior of participating in Subjects based on social network analysis,” China Communications, vol. 13, no. 4, pp. 70-80, 2016.
  2. Veena H Bhat, V R Malkani, P Deepa Shenoy, K R Venugopal and L M Patnaik, “Classification of Email using BeaKS: Behavior and Keyword Stemming,” IEEE TENCON, 2011.
  3. Leena Giri G, Praveen Gowda I V, Manjula S H, Venugopal K R and L M Patnaik, “In Page Semantic Ranking of Snippets for WebPages,” Sixth International Conference on advances in Computing, Control and Telecommunication Technologies – ACT 2015, Trivandrum, India, October 2015.
  4. Itoh, Masahiko, Daisaku Yokoyama, Masashi Toyoda, Yoshimitsu Tomita, Satoshi Kawamura and Masaru Kitsuregawa. “Visual Exploration of Changes in Passenger Flows and Tweets on Mega-City Metro Network,” IEEE Transactions on Big Data, vol. 2, no. 1, pp. 85-99, 2016.
  5. Kim, Youngsoo, Felicia Natali, Feida Zhu and Eepeng Lim, “Investigating the Influence of Offline Friendship on Twitter Networking Behaviors,” 49th IEEE Hawaii International Conference on System Sciences (HICSS), pp. 736-745, 2016.
  6. Roy, Suman Deb, Gilad Lotan and Wenjun Kevin Zeng, “The Attention Automaton: Sensing Collective User Interests in Social Network Communities,” IEEE Transactions on Network Science and Engineering, vol. 2, no. 1, pp. 40-52, 2015.
  7. D'Andrea, Eleonora, Pietro Ducange, Beatrice Lazzerini and Francesco Marcelloni, “Real-time detection of traffic from twitter stream analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 2269-2283, 2015.
  8. Fabijan, Aleksander, Helena Holmström Olsson and Jan Bosch. “Customer feedback and data collection techniques in software R&D: a literature review,” In International Conference of Software Business, pp. 139-153, Springer International Publishing, 2015.
  9. Li, Bo, Duoyong Sun, Julei Fu and Zihan Lin, “Social Network Construction and Analysis Based on Community Photo Collections with Face Recognition,” IEEE Sixth International Conference on Business Intelligence and Financial Engineering (BIFE), pp. 463-467, 2013.
  10. Riek, Markus, Rainer Bohme and Tyler Moore, “Measuring the influence of perceived cybercrime risk on online service avoidance,” IEEE Transactions on Dependable and Secure Computing, vol. 13, no. 2, pp. 261-273, 2016.
  11. Ramachandra A C, Pavithra K, Yashasvini K, Raja K B, Venugopal K R and Lalit M Patnaik, “Cross-validation for graph matching based offline signature verification,” IEEE Annual India Conference, INDICON 2008, vol. 1, pp. 17–22, 2008.
  12. Chetana Hegde, P Deepa Shenoy, K R Venugopal and L M Patnaik, “Authentication Using Finger Knuckle Prints”, Signal, Image and Video Processing, Springer,P-ISSN:1863-1703, E-ISSN: 1863-1711, vol. 7, no. 4, pp. 633–645, July 2013.
  13. Ramesha K, K B Raja, Venugopal K R and L M Patnaik, “Template based Mole Detection for Face Recognition,” International Journal of Computer Theory and Engineering, (IJCTE), ISSN : 1793-8201, vol. 2, no. 5, pp. 797-804, October 2010.
  14. Vibha L, Chetana Hegde, Deepa Shenoy P, Venugopal K R and L M Patnaik, “Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining,” In IAENG International Journal of Computer Science, e-ISSN : 1819-9224, p-ISSN : 1819-656x, vol. 35, no. 3, pp. 382-391, 2008.
  15. Akay, Altug, Andrei Dragomir and Björn-Erik Erlandsson, “A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 1, pp. 389-396, 2015.
  16. Naini, Farid M., Jayakrishnan Unnikrishnan, Patrick Thiran and Martin Vetterli, “Where you are is who you are: User identification by matching statistics,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 2, pp. 358-372, 2016.
  17. Qiao, Xiuquan, Wei Yu, Jinsong Zhang, Wei Tan, Jianchong Su, Wangli Xu and Junliang Chen, “Recommending Nearby Strangers Instantly Based on Similar Check-In Behaviors,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 3, pp. 1114-1124, 2015.
  18. Valenza, Gaetano, Vladimir Carli, Antonio Lanata, Wei Chen, Roozbeh Jafari and Enzo Pasquale Scilingo, “Guest Editorial Sensor Informatics for Managing Mental Health,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 4, pp. 975-976, 2016.
  19. Mertz Leslie, “What Can Big Data Tell Us About Health?: Finding Gold Through Data Mining,” IEEE Pulse, vol. 7, no. 5, pp. 40-44, 2016.
  20. Rubiano, Sandra Milena Merchan and Jorge Alberto Duarte Garcia, “Analysis of Data Mining Techniques for Constructing a Predictive Model for Academic Performance,” IEEE Latin America Transactions, vol. 14, no. 6, pp. 2783-2788, 2016.
  21. Da Silva, Luis Alexandre Estevao, “A Data Mining Approach for Standardization of Collectors Names in Herbarium Database,” IEEE Latin America Transactions, vol. 14, no. 2, pp. 805-810, 2016.
  22. Dong, Boxiang, Ruilin Liu and Hui Wendy Wang, “Trust-but-Verify: Verifying Result Correctness of Outsourced Frequent Itemset Mining in Data-Mining-As-a-Service Paradigm,” IEEE Transactions on Services Computing, vol. 9, no. 1, pp. 18-32, 2016.
  23. Liu Bing, “Opinion mining and sentiment analysis,” Web Data Mining, pp. 459-526, Springer Berlin Heidelberg, 2011.
  24. P Deepa Shenoy, Srinivasa K G, Venugopal K R and Lalit M Patnaik, “Evolutionary approach for mining association rules on dynamic databases,” Advances in Knowledge Discovery and Data Mining, pp. 325–336, April 2003.
  25. P Deepa Shenoy, Srinivasa K G, Venugopal K R and Lalit M Patnaik, “Dynamic association rule mining using genetic algorithms,” Intelligent Data Analysis, vol. 9, no. 5, pp. 439–453, September 2005.
  26. Heredia, Diana, Yegny Amaya and Edwin Barrientos, “Student Dropout Predictive Model Using Data Mining Techniques,” IEEE Latin America Transactions, vol. 13, no. 9, pp. 3127-3134, 2015.
  27. Saâdaoui, Foued, Pierre R. Bertrand, Gil Boudet, Karine Rouffiac, Frédéric Dutheil and Alain Chamoux, “A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining,” IEEE Transactions on Nanobioscience, vol. 14, no. 7, pp. 707-715, 2015.
  28. Chen, Hongmei, Tianrui Li, Chuan Luo, Shi-Jinn Horng and Guoyin Wang, “A decision-theoretic rough set approach for dynamic data mining,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 1958-1970, 2015.
  29. Veena H Bhat, Prashanth G Rao, Abhilash, P Deepa Shenoy, Venugopal K R and L M Patnaik, “A Novel Data Generation Approach for Digital Forensic Application In Data Mining,” IEEE Second International Conference on Machine Learning and Computing, February-2010.
  30. Kalegele, Khamisi, Kazuto Sasai, Hideyuki Takahashi, Gen Kitagata and Tetsuo Kinoshita, “Four Decades of Data Mining in Network and Systems Management,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 10, pp. 2700-2716, 2015.
  31. Cereda, Paulo Roberto Massa and J. José Neto, “Adaptive data mining: Preliminary studies,” IEEE Latin America Transactions, vol. 12, no. 7, pp. 1258-1270, 2014.
  32. Baker and Ryan S, “Educational Data Mining: An Advance for Intelligent Systems in Education,” IEEE Intelligent Systems, vol. 29, no. 3, pp. 78-82, 2014.
  33. Fisch, Dominik, Edgar Kalkowski and Bernhard Sick, “Knowledge fusion for probabilistic generative classifiers with data mining applications,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 652-666, 2014.
  34. Shmiel, Oren, Tomer Shmiel, Yaron Dagan and Mina Teicher, “Processing of multichannel recordings for data-mining algorithms,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 3, pp. 444-453, 2007.
  35. Zhou, Xiaoping, Xun Liang, Haiyan Zhang and Yuefeng Ma, “Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 411-424, 2016.
  36. Alameda-Pineda, Xavier, Yan Yan, Elisa Ricci, Oswald Lanz and Nicu Sebe, “Analyzing free-standing conversational groups: a multimodal approach,” 23rd ACM International Conference on Multimedia, pp. 5-14, 2015.
  37. Ruan, Xin, Zhenyu Wu, Haining Wang and Sushil Jajodia, “Profiling Online Social Behaviors for Compromised Account Detection,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 1, pp. 176-187, 2016.
  38. Cao, Nan, Conglei Shi, Sabrina Lin, Jie Lu, Yu-Ru Lin and Ching-Yung Lin, “Targetvue: Visual analysis of anomalous user behaviors in online communication systems,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 280-289, 2016.
  39. Shen, Haiying, Ze Li, Jinwei Liu and Joseph Edward Grant, “Knowledge sharing in the online social network of yahoo! answers and its implications,” IEEE Transactions on Computers, vol. 64, no. 6, pp. 1715-1728, 2015.
  40. Liu, Siyuan, Shuhui Wang and Feida Zhu, “Structured Learning from Heterogeneous Behavior for Social Identity Linkage,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 7, pp. 2005-2019, 2015.
  41. Miro-llinares f and j. J. Rodriguez-sala, “Cyber Hate Speech On Twitter: Analyzing Disruptive Events From Social Media To Build A Violent Communication And Hate Speech Taxonomy,” International Journal of Design & Nature and Ecodynamics, vol. 11, no. 3, pp. 406-415, 2016.
  42. Dong, Lijun, Kui Wu and Guoming Tang, “A Data-Centric Approach to Quality Estimation of Role Mining Results,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 12, pp. 2678-2692, 2016.
  43. Univaso, Pedro, Juan Maria Ale and Jorge A. Gurlekian, “Data Mining applied to Forensic Speaker Identification,” IEEE Latin America Transactions, vol. 13, no. 4, pp. 1098-1111, 2015.
  44. Lin, Cheng-Jhe, Changxu Wu and Wanpracha A. Chaovalitwongse, “Integrating human behavior modeling and data mining techniques to predict human errors in numerical typing,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 1, pp. 39-50, 2015.
  45. Angiulli, Fabrizio, Stefano Basta, Stefano Lodi and Claudio Sartori, “Distributed strategies for mining outliers in large data sets,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 7, pp. 1520-1532, 2013.
  46. Marquez-Vera, Carlos, Cristóbal Romero Morales and Sebastián Ventura Soto, “Predicting school failure and dropout by using data mining techniques,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 8, no. 1, pp. 7-14, 2013.
  47. Cao Longbing, “Social security and social welfare data mining: An Overview,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 837-853, 2012.
  48. Huang, Xiaoge, Liping Chen, Qianbin Chen and Bin Shen, “Joint malicious user detection and resource allocation in cognitive radio networks,” IEEE 10th International Conference on Communications and Networking in China (ChinaCom), pp. 278-282, 2015.
  49. Terzi, Ramazan, Uraz Yavanoglu, Duygu Sinanc, Dogac Oguz and Semra Cakir, “An Intelligent Technique for Detecting Malicious Users on Mobile Stores,” IEEE 13th International Conference on Machine Learning and Applications (ICMLA), pp. 470-477, 2014.
  50. Jnanamurthy H. K and Sanjay Singh, “Detection and filtering of collaborative malicious users in reputation system using quality repository approach,” IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 466-471, 2013.
  51. Rabinovitch Eddie, “Protect your users against the latest web-based threat: malicious code on caching servers [Your Internet Connection],” IEEE Communications Magazine, vol. 45, no. 3, pp. 20-22, 2007.
  52. Vasanthakumar G U, Bagul Prajakta, P Deepa Shenoy, Venugopal K R and Lalit M Patnaik, “PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach,” Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015), Procedia Computer Science, Elsevier B.V., vol. 54, pp. 362–370, August 2015.
  53. Vasanthakumar G U, P Deepa Shenoy and Venugopal K R, “PTIB: Profiling Top Influential Blogger in Online Social Networks,” International Journal of Information Processing (IJIP-2016), IK International Publishing, vol. 10, no. 1, pp. 77–91, June 2016.
  54. Vasanthakumar G U, Priyanka R, Vanitha Raj K C, Bhavani S, Asha Rani B R, P Deepa Shenoy and Venugopal K R, “PTMIB: Profiling Top Most Influential Blogger using Content Based Data Mining Approach,” IEEE International Conference on Data Science and Engineering (ICDSE-2016), Cochin, India, August 2016.
  55. Zhu, Jiaqi, Kaijun Wang, Yunkun Wu, Zhongyi Hu and Hongan Wang, “Mining User-Aware Rare Sequential Topic Patterns in Document Streams,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, pp. 1790-1804, 2016.
  56. Wang, Yufeng, Athanasios V. Vasilakos, Jianhua Ma and Naixue Xiong, “On studying the impact of uncertainty on behavior diffusion in social networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 2, pp. 185-197, 2015.
  57. Rawassizadeh, Reza, Elaheh Momeni, Chelsea Dobbins, Joobin Gharibshah and Michael Pazzani, “Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 11, 2016.
  58. Agreste, Santa, Pasquale De Meo, Emilio Ferrara, Sebastiano Piccolo and Alessandro Provetti, “Analysis of a heterogeneous social network of humans and cultural objects,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 4, pp. 559-570, 2015.
  59. Chen, Yi-Cheng, Wen-Chih Peng and Suh-Yin Lee, “Mining Temporal Patterns in Time Interval-Based Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 12, pp. 3318-3331, 2015.
  60. Soft Computing for Data Mining Applications, Venugopal K R, Srinivasa K G and L M Patnaik, ISBN 978-3-642-00192-5, e-ISBN 978-3-642-00193-2, Do1 10.1007/978-3-642, 00193-2, pp. 342, Springer Verlag, 2009.
  61. Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” 2nd International Conference on Knowledge Discovery and Data Mining, 1996.
  62. Christopher C. Yang and Tobun Dorbin Ng, “Analysing and Visualizing Web Opinion Development and Social Interactions with Density-based clustering,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 41, no. 6, pp. 1144-1155, 2011.
  63. J.R. Dcouth and T. Mohanraj, “Analyzing and Extracting Social Mining Trends Through Web Opinion Developments Via Density Based Clustering,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 2, Issue 1, Jan 2013.
  64. Sander F, M Ester and P. Kriegelh, “The algorithm GDBSCAN and its application,” Data Mining and Knowledge Discovery, no.2, pp. 178-192, 1998.
  65. Sharma Sanjeev and R. K. Gupta. "Improved BSP clustering algorithm for social network analysis," International journal of grid and Distributed Computing, vol. 3, no. 3, pp. 67-76, 2010.
  66. Ertöz, Levent, Michael Steinbach and Vipin Kumar, “Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data,” SDM, pp. 47-58. 2003.
  67. Biçici, Ergun, and Deniz Yuret, “Locally scaled density based clustering,” International Conference on Adaptive and Natural Computing Algorithms, pp. 739-748. Springer Berlin Heidelberg, 2007.
  68. Das, Swagatam, Ajith Abraham and Amit Konar, “Automatic clustering using an improved differential evolution algorithm,” IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, vol. 38, no. 1, pp. 218-237, 2008.
  69. Christopher C. Yang, Nan Liu and Marc Sageman, “Analyzing the Terrorist Social Networks with Visualization Tools,” IEEE International Conference on Intelligence and Security Informatics, pp. 331-342, 2006.
  70. Sheng He, Petros Samara, Jan Burgers and Lambert Schomaker, “A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization,” IEEE Transactions on Image Processing, vol. 25, no. 11, 2016.
  71. Nagaraju S, Manish Kashyap and Mahua Bhattacharya, “A variant of DBSCAN algorithm to find embedded and nested adjacent clusters,” IEEE 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 486-491, 2016.
  72. Baydoun, Mohammed, Mohammad Dawi and Hassan Ghaziri, “Enhanced parallel implementation of the K-Means clustering algorithm,” IEEE 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 7-11, 2016.
  73. Leonidas Akritidis, Dimitrios Katsaros and Panayiotis Bozanis, “Identifying the productive and influential bloggers in a community,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 41, no. 5, pp. 759–764, 2011.
  74. Yichuan Cai and Yi Chen, “Mass: a multi-facet domain-specific influential blogger mining system,” 2010 IEEE 26th International Conference on Data Engineering (ICDE), pp. 1109–1112, 2010.
  75. Eunyoung Moon and Sangki Han, “A qualitative method to find influencers using similarity-based approach in the blogosphere,” International Journal of Social Computing and Cyber-Physical Systems, vol. 1, no. 1, pp. 56–78, 2011.
  76. Chang Sun, Bing-quan Liu, Cheng-jie Sun, De-Yuan Zhang and Xiaolong Wang, “Simrank: A link analysis based blogger recommendation algorithm using text similarity,” 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 6, pp. 3368–3373, 2010.
  77. Mohammad Alodadi and Vandana P Janeja, “Similarity in patient support forums using tf-idf and cosine similarity metrics,” IEEE International Conference on Healthcare Informatics (ICHI), pp. 521– 522, 2015.
  78. Emily Hill, Shivani Rao and Avinash Kak, “On the use of stemming for concern location and bug localization in java,” IEEE 12th International Working Conference on Source Code Analysis and Manipulation (SCAM), pp. 184–193, 2012.
  79. Mohamed H Haggag, “Keyword Extraction using Semantic Analysis,” International Journal of Computer Applications, vol. 61, no. 1, 2013.
  80. Cristian Moral, Ange´lica de Antonio, Ricardo Imbert and Jaime Ram´irez, “A survey of stemming algorithms in information retrieval,” Information Research: An International Electronic Journal, vol. 19, no. 1, p. 1, 2014.
  81. S Megala, A Kavitha and A Marimuthu, “Improvised Stemming Algorithm–TWIG,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 168–171, 2013.
  82. De Boom, Cedric, Steven Van Canneyt, Steven Bohez, Thomas Demeester and Bart Dhoedt, “Learning Semantic Similarity for Very Short Texts,” IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1229-1234, November 2015.
  83. Itoh, Masahiko, Naoki Yoshinaga, Masashi Toyoda and Masaru Kitsuregawa, “Analysis and visualization of temporal changes in bloggers’ activities and interests,” Visualization Symposium (PacificVis), pp. 57-64, February 2012.
  84. Lu Fuxi Zhu, “Discovering the important bloggers in blogspace,” IEEE International Conference on Arti?cial Intelligence and Education (ICAIE), pp. 151-154, October 2010.
  85. Macskassy and Sofus A, “Leveraging Contextual Information to Explore Posting and Linking Behaviors of Bloggers,” IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 64-71, August 2010.
  86. Rui, Cai, Qi Jia-yin and Wang Mian, “Forecasting bloggers’ online behavior based on improved Pareto/NBD model,” IEEE International Conference on Management Science and Engineering (ICMSE), pp. 8490, July 2013.
  87. Zhang, Yuan and Yuqian Bai, “Research on the In?uence of Microbloggers, Take Sina Celebrity Micro-blog as an Example,” IEEE Eighth International Conference on Semantics, Knowledge and Grids (SKG), pp. 189-192, October 2012.
  88. Seung-Hwan Lim, Sang-Wook Kim, Sunju Park and Joon Ho Lee, “Determining content power users in a blog network: an approach and its applications,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 41, no. 5, pp. 853–862, 2011.
  89. Riccardo Cognini, Damiano Falcioni and Alberto Polzonetti, “Social networks: Analysis for integrated social profiles,” IEEE Internet Technologies and Applications (ITA), pp. 68–72, 2015.
  90. B. Erlin, Norazah Yusof and Azizah Abdul Rahman, “Analyzing online asynchronous discussion using content and social network analysis,” IEEE Ninth International Conference on Intelligent Systems Design and Applications, pp. 872–877, 2009.
  91. Boudiba Tahar-Rafik and Ahmed-Ouamer Rachid, “Towards a new approach for generating user profile from folksonomies,” IEEE 4th International Symposium on ISKO-Maghreb: Concepts and Tools for knowledge Management (ISKO-Maghreb), pp. 1–6, 2014.
  92. Yi Cai and Qing Li, “Personalized search by tag-based user profile and resource profile in collaborative tagging systems,” 19th ACM International Conference on Information and Knowledge Management, pp. 969–978, 2010.
  93. Bo Wang, Yingjun Sun, Cheng Tang and Yang Liu, “A visualization toolkit for online social network propagation and influence analysis with content features,” IEEE International Conference on Orange Technologies (ICOT), pp. 129–132, 2014.
  94. Christopher C Yang and Tobun D. Ng, “Terrorism and crime related weblog social network: Link, content analysis and information visualization,” IEEE Intelligence and Security Informatics, pp. 55–58, 2007.
  95. Hong-Jun Yoon and Georgia Tourassi, “Analysis of online social networks to understand information sharing behaviors through social cognitive theory,” IEEE Annual Oak Ridge National Laboratory Biomedical Science and Engineering Center Conference (BSEC), pp. 1–4, 2014.
  96. Noor Izzati Ariff and Zaidatun Tasir, “Meta-analysis of content analysis models for analysing online problem solving discussion,” IEEE Conference on e-Learning, e-Management and e-Services (IC3e), pp. 148–152, 2015.
  97. Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung and Svetha Venkatesh, “Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis,” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1354–1361, 2015.
  98. Yung-Chung Tsao, Kevin Chihcheng Hsu and Yin-Te Tsai, “Using content analysis to analyze the trend of information technology toward the academic researchers at the design departments of universities in Taiwan,” IEEE 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 3691–3694, 2012.
  99. Nitin Agarwal, Huan Liu, Shankara Subramanya, John J. Salerno and S. Yu Philip, “Connecting sparsely distributed similar bloggers,” Ninth IEEE International Conference on Data Mining, pp. 11–20, 2009.
  100. Faiza Belbachir, Khadidja Henni and Lynda Zaoui, “Automatic detection of gender on the blogs,” IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pp. 1–4, 2013.
  101. Bi Chen, Qiankun Zhao, Bingjun Sun and Prasenjit Mitra, “Predicting blogging behavior using temporal and social networks,” Seventh IEEE International Conference on Data Mining (ICDM 2007), pp. 439–444, 2007.
  102. Kang-Seo Park,Young-Gon Kim and Rae-Hong Park, “Face Detection Using The 33 Block Rank Patterns Of Gradient Magnitude Images,” Signal and Image Processing : An International Journal (SIPIJ), vol. 4, no. 5, October-2013.
  103. Kailash Devrari and K.Vinay Kumar, “Fast Face Detection Using Graphics Processor,” International Journal of Computer Science and Information Technologies (IJCSIT), vol. 2, no. 3, pp. 1082–1086, 2011.
  104. Arundhati Das, Mameeta Pukhrambam and Ashim Saha, “Real-Time Robust Face Detection and Tracking using extended Haar functions and improved Boosting Algorithm,” IEEE International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 981-985, 2015.
  105. Shi Jianbo and Carlo Tomasi, “Good features to track." IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'94), pp. 593-600, 1994.
  106. Adrian Wong Yoong Wai, Shahirina Mohd Tahir and Yoong Choon Chang, “GPU Acceleration of Real Time Viola-Jones Face Detection,” IEEE International Conference on Control System, Computing and Engineering, pp. 27-29, 2015.
  107. Ijaz Khan, Hadi Abdullah and Mohd Shamian Bin Zainal, “Efficient Eyes and Mouth Detection Algorithm using Combination of Viola Jones and Skin Color Pixel Detection,” International Journal of Engineering and Applied Sciences, vol. 3, no. 4, June-2013.
  108. Wei Liuliu and Liu Mingyang, “Multi-pose Face Detection Research based on Adaboost,” IEEE International Conference on Measuring Technology and Mechatronics Automation, pp. 409-412, 2015.
  109. Wang Yi-Qing. "An Analysis of the Viola-Jones face detection algorithm," Image Processing On Line, vol. 4, pp. 128-148, 2014.
  110. R.Sureshkumar and N.Arthi, “Generate Attribute-Enhanced Sparse Codewords To Retrieve Image From Large Image Database,” International Journal of Engineering Science Invention, vol. 2, no. 1, pp. 2319–6726, October 2013.
  111. Seyed Mohammad Hassan Anvar, Wei-Yun Yau and Eam Khwang Teoh, “Multiview Face Detection and Registration Requiring Minimal Manual Intervention,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 10, October-2013.
  112. Raphael Sznitman and Bruno Jedynak, “Active Testing for Face Detection and Localization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, October-2010.
  113. Hongliang Li, King N. Ngan and Qiang Liu, “FaceSeg: Automatic Face Segmentation for Real-Time Video,” IEEE Transactions on Multimedia, vol. 11, no. 1, pp. 77 – 88, 2009.
  114. Paul Viola and Michael Jones, “Robust Real-time Object Detection,” Second International Workshop on Statistical and Computational Theories of Vision Modeling, Learning, Computing and Sampling, July- 2001.
  115. Hatice Gunes and Massimo Piccardi, “Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display,” IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol. 39, no. 1, February 2009.
  116. Mauricio Pamplona Segundo, Luciano Silva, Olga Regina Pereira Bellon and Chaua C. Queirolo, “Automatic Face Segmentation and Facial Landmark Detection in Range Images,” IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, vol. 40, no. 5, October-2010.
  117. Jie Pan, Xue-Song Wang and Yu-Hu Cheng, “Single-sample Face Recognition Based on LPP Feature Transfer,” IEEE Access, vol. 4, pp. 2873 – 2884, 2016.
  118. M. Ko and A. Barkana, “A new solution to one sample problem in face recognition using FLDA,” Applied Mathematical Computations, vol. 217, no. 24, pp. 10368-10376, Aug. 2011.
  119. J. Wu and Z. H. Zhou, “Face recognition with one training image per person,” Pattern Recognition Letters, vol. 23, no. 14, pp. 1711-1719. Dec. 2002.
  120. Huang, Yea-Shuan and Suen-Yu Chen, “A geometrical-model-based face recognition,” IEEE International Conference on Image Processing (ICIP), pp. 3106-3110, 2015.
  121. Wiskott L, Fellous, J.M, Kuiger N, and von der Malsburg C, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.
  122. Ngoc-Son Vu and Alice Caplier, “Face Recognition with Patterns of Oriented Edge Magnitudes,” European Conference on Computer Vision, pp. 313–326, Springer Berlin Heidelberg, 2010.
  123. Chih-Rung Chen, Wei-Su Wong and Ching-Te Chiu, “A 0.64mm RealTime Cascade Face Detection Design Based on Reduced Two-Field Extraction,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 19, no. 11, November 2011.
  124. Mandeep Kaur, Rajeev Vashisht and Nirvair Neeru, “Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition,” International Journal of Computer Applications, vol. 9, no. 12, pp. 36–40, November 2010.
  125. Muwei Jian and Kin-Man Lam, “Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 11, pp. 1761-1772, 2015.
  126. Kyungjoong Jeong, Jaesik Choi and Gil-Jin Jang, “Semi-Local Structure Patterns for Robust Face Detection,” IEEE Signal Processing Letters, vol. 22, no. 9, September 2015.
  127. Felix Juefei-Xu, Dipan K. Pal, Karanhaar Singh and Marios Savvides, “A Preliminary Investigation on the Sensitivity of COTS Face Recognition Systems to Forensic Analyst-style Face Processing for Occlusions,” IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 25-33, 2015.
  128. Dhara Marvadi, Maulin Joshi, Chirag Paunwala and Aarohi Vora, “Comparative Analysis of 3D Face Recognition Using 2D-PCA and 2D-LDA Approaches,” IEEE 5th Nirma University International Conference on Engineering (NUiCONE), pp. 1-5, 2015.
  129. Ali Moeini and Hossein Moeini, “Real-World and Rapid Face Recognition towards Pose and Expression Variations via Feature Library Matrix,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 5, pp. 969-984, May 2015.
  130. Christina Joy, Roshlin Anie Abraham and Raji, “A Survey on Face Matching and Retrieval of Images,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 2, pp. 33–37, February 2015.
  131. Himanshu Sharma, Sumeet Saurav, Sanjay Singh, Anil K Saini and Ravi Saini, “Analyzing Impact of Image Scaling Algorithms on Viola-Jones Face Detection Framework,” IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1715-1718, 2015.
  132. H. S. Hou and H. C. Andrews, “Cubic splines for image interpolation and digital filtering,” IEEE Transaction on Acoustic, Speech, Signal Processing, vol. 26, no. 6, pp. 508–517, 1978.
  133. J. A. Parker, R. V. Kenyon and D.E. Troxel, “Comparison of interpolation methods for image resampling,” IEEE Transaction on Medical Imaging, vol. 2, no. 1, pp. 31 – 39, 1983.
  134. T. M. Lehamann, C. Gonner and K. Spitzer, “Survey: Interpolation Methods in Medical Image Processing,” IEEE Transaction on Medical Imaging, vol. 18, no. 11, pp. 1049- 1075, 1999.
  135. P. N. Gour, S. Narumanchi, S. Saurav and S. Singh, “Hardware accelerator for real- time image resizing,” IEEE 18th International Symposium on VLSI Design and Test, pp. 1-6, 2014.
  136. C. Lin, M. Sheu, H. Chiang, W. Tsai and Z. Wu, “Real-Time FPGA architecture of extended linear convolution for digital image scaling,” IEEE International Conference on Field- Programmable Technology, pp. 381–384, 2008.
  137. Zhang, Yi-Qing and Xiang Li, “Temporal dynamics and impact of event interactions in cyber-social populations,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 23, no. 1, 2013.
  138. Kefalas, Pavlos, Panagiotis Symeonidis and Yannis Manolopoulos, “New perspectives for recommendations in location-based social networks: Time, privacy and explainability,” Fifth ACM International Conference on Management of Emergent Digital EcoSystems, pp. 1-8, 2013.
  139. Song, Yang, Zheng Hu, Xiaoming Leng, Hui Tian, Kun Yang and Xin Ke, “Friendship influence on mobile behavior of location based social network users,” Journal of Communications and Networks, vol. 17, no. 2, pp. 126-132, 2015.
  140. Berjani, Betim and Thorsten Strufe, “A recommendation system for spots in location-based online social networks,” ACM 4th Workshop on Social Network Systems, p. 4, 2011.
  141. Cranshaw, Justin, Eran Toch, Jason Hong, Aniket Kittur and Norman Sadeh, “Bridging the gap between physical location and online social networks,” ACM 12th International Conference on Ubiquitous Computing, pp. 119-128, 2010.
  142. Cho, Eunjoon, Seth A. Myers and Jure Leskovec, “Friendship and mobility: user movement in location-based social networks,” ACM 17th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082-1090, 2011.
  143. Silva, Nitai B., Ren Tsang, George DC Cavalcanti and Jyh Tsang, “A graph-based friend recommendation system using genetic algorithm,” IEEE Congress on Evolutionary Computation, pp. 1-7, 2010.
  144. Narayanam, Ramasuri and Yadati Narahari. "A shapley value-based approach to discover influential nodes in social networks,” IEEE Transactions on Automation Science and Engineering, vol. 8, no. 1, pp. 130-147, 2011.
  145. Debnath, Souvik, Niloy Ganguly and Pabitra Mitra, “Feature weighting in content based recommendation system using social network analysis,” 17th ACM International Conference on World Wide Web, pp. 1041-1042, 2008.
  146. Wan, Shengxian, Yanyan Lan, Jiafeng Guo, Chaosheng Fan and Xueqi Cheng, “Informational friend recommendation in social media,” 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1045-1048, 2013.
  147. Gao Yang, Yan Chen and KJ Ray Liu, “Understanding sequential user behavior in social computing: To answer or to vote?,” IEEE Transactions on Network Science and Engineering, vol. 2, no. 3, pp. 112-126, 2015.
  148. Yu Chung-Kai, Mihaela van der Schaar and Ali H. Sayed, “Information-Sharing Over Adaptive Networks With Self-Interested Agents,” IEEE Transactions on Signal and Information Processing over Networks, vol. 1, no. 1, pp. 2-19, 2015.
  149. Yunlong Wang and Petar M. Djuric, “Social Learning With Bayesian Agents and Random Decision Making,” IEEE Transactions on Signal Processing, vol. 63, no. 12, 2015.
  150. Mahmud, Jalal, Michelle X. Zhou, Nimrod Megiddo, Jeffrey Nichols, and Clemens Drews, “Recommending targeted strangers from whom to solicit information on social media,” ACM International Conference on Intelligent User Interfaces, pp. 37-48, 2013.
  151. Zhang, Lizi, Hui Fang, Wee Keong Ng and Jie Zhang, “IntRank: Interaction ranking-based trustworthy friend recommendation,” IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, pp. 266-273, 2011.
  152. Lan Zhang, Xiang-Yang Li, Kebin Liu, Taeho Jung and Yunhao Liu, “Message in a Sealed Bottle: Privacy PreservingFriending in Mobile Social Networks,” IEEE Transactions on Mobile Computing, vol. 14, issue 9, 2013.
  153. Sharad Goel and Daniel G. Goldstein, “Predicting Individual Behavior with Social Networks,” Marketing Science, vol. 33, no. 1, pp. 82–93, 2014.
  154. Benevenuto, Fabrício, Tiago Rodrigues, Meeyoung Cha and Virgílio Almeida, “Characterizing user behavior in online social networks,” 9th ACM SIGCOMM Conference on Internet Measurement, pp. 49-62, 2009.
  155. Papagelis, Manos, Vanessa Murdock and Roelof van Zwol, “Individual behavior and social influence in online social systems,” 22nd ACM Conference on Hypertext and Hypermedia, pp. 241-250, 2011.
  156. Jibing Gong, Jie Tang and A. C. M. Fong, “ACTPred: Activity Prediction in Mobile Social Networks,” Tsinghua Science and Technology, vol. 19, no. 3, pp. 265-274, 2014.
  157. Sofia Angeletou, Matthew Rowe and Harith Alani, “Modelling and analysis of User behaviourin online community,” International Semantic Web Conference (ISWC), pp. 35-50, Springer Berlin Heidelberg, 2011.
  158. Ahmed Youssef and Ahmed Emam, “Network Intrusion Detection Using Data Mining and Network Behavior Analysis,” International Journal of Computer Science and Information Technology (IJCSIT), vol. 3, no. 6, 2011.
  159. Fortunato Bianconi, Valerio Brunori, Paolo Valigi, Francesco La Rosa and FabrizioStracci, “Information Technology as Tools for Cancer Registry and Regional Cancer Network Integration,” IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans, vol. 42, no. 6, 2012.
  160. Paek, Hye-Jin and Thomas Hove, “Determinants of vertical and horizontal online health information behavior,” IEEE 47th Hawaii International Conference on System Sciences, pp. 2597-2606, 2014.
  161. Gokcen, Yasemin, Vahid Aghaei Foroushani and A. Nur Zincir Heywood, “Can we identify NAT behavior by analyzing Traffic Flows?,” IEEE Security and Privacy Workshops (SPW), pp. 132-139, 2014.
  162. Liben-Nowell, David and Jon Kleinberg, “The link-prediction problem for social networks,” Journal of American Society for Information Science and Technology, vol. 58, no. 7, pp. 1019-1031, 2007.
  163. Ratnapala I P, R G Ragel and S Deegalla, “Students behavioural analysis in an online learning environment using data mining,” IEEE 7th International Conference on Information and Automation for Sustainability, pp. 1-7, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Behavior Analysis Content Analysis Face Detection Online Social Networks User Profiling

Powered by PhDFocusTM