Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Review Article

A State of the Art Review on User Behavioral Issues in Online Social Networks

Author(s): Nidhi A. Patel* and Nirali Nanavati

Volume 16, Issue 2, 2023

Published on: 04 October, 2022

Article ID: e130522204779 Pages: 15

DOI: 10.2174/2666255815666220513162448

Price: $65

Abstract

Social networks are aimed at information sharing and friend-making due to the rapid development of Online Social Networks (OSN) and the increasing number of online users. The OSNs are also becoming an ideal platform for merchandise recommendation, opinion expression, information diffusion, and influence generation. Different types of social network services and users select the appropriate social network technology, services, and applications to meet their sociability, entertainment, or information retrieval needs. User behavior involves user interaction, access, and browsing of the OSN. The users have different roles in different groups of social networks. Different identifications of the user may cause the user's intention to change. The user's intention may change as a result of different identifications. In this work, we discuss an introduction to OSN, single and multi-platform user behavior with various prediction models and recommendations.

Keywords: Fake review detection, multi-platform user behavior, online social network, single platform user behavior, spam profile user behavior analysis.

Graphical Abstract

[1]
X. Ye, B. Zhao, T.H. Nguyen, and S. Wang, "Social media and social awareness", In: H. Guo, M.F. Goodchild, A. Annoni, Eds., Manual of Digital Earth., Springer: Singapore, 2020, pp. 425-440.
[http://dx.doi.org/10.1007/978-981-32-9915-3_12]
[2]
G. Appel, L. Grewal, R. Hadi, and A.T. Stephen, "The future of social media in marketing", J. Acad. Mark. Sci., vol. 48, no. 1, pp. 79-95, 2020.
[http://dx.doi.org/10.1007/s11747-019-00695-1] [PMID: 32431463]
[3]
C.K. Mishra, "Digital marketing: Scope opportunities and challenges", In: Promotion and Marketing Communications., IntechOpen, 2020.
[4]
C.F. Hofacker, and D. Belanche, "Eight social media challenges for marketing managers", Spanish J. Market. -.ESIC, vol. 20, no. 2, pp. 73-80, 2016.
[http://dx.doi.org/10.1016/j.sjme.2016.07.003]
[5]
Y.K. Dwivedi, E. Ismagilova, D.L. Hughes, J. Carlson, R. Filieri, J. Jacobson, V. Jain, H. Karjaluoto, H. Kefi, A.S. Krishen, V. Kumar, M.M. Rahman, R. Raman, P.A. Rauschnabel, J. Rowley, J. Salo, G.A. Tran, and Y. Wang, "Setting the future of digital and social media marketing research: Perspectives and research propositions", Int. J. Inf. Manage., pp. 1-37, 2020.
[6]
S. Wu, F. Pantoja, and N. Krey, "Marketing opportunities and challenges in a changing global marketplace", Annual Conference Proceedings of the 2019 Academy of Marketing Science (AMS) Annual Conference, 2020.
[http://dx.doi.org/10.1007/978-3-030-39165-2]
[7]
F. Li, J. Larimo, and L.C. Leonidou, "Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda", J. Acad. Mark. Sci., 2020.
[8]
G. Meiselwitz, "Social Computing and Social Media", Proceedings, Part II, 8th International Conference, SCSM 2016, Held as Part of HCI International 2016, July 17-22, Toronto, ON, Canada, 2016.
[9]
N.P. Rana, Ed., Digital and Social Media Marketing: Emerging Applications and Theoretical Development., Springer International Publishing, 2020.
[http://dx.doi.org/10.1007/978-3-030-24374-6]
[10]
M.K. Hayat, "Towards deep learning prospects: Insights for social media analytics", IEEE Access, vol. 7, pp. 36958-36979, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2905101]
[11]
F. de Oliveira Santini, W.J. Ladeira, D.C. Pinto, M.M. Herter, C.H. Sampaio, and B.J. Babin, "Customer engagement in social media: A framework and meta-analysis", J. Acad. Mark. Sci., vol. 48, no. 6, pp. 1211-1228, 2020.
[http://dx.doi.org/10.1007/s11747-020-00731-5]
[12]
T. Wang, M. Garfield, P. Wisniewski, and X. Page, "Benefits and Challenges for Social Media Users on the Autism Spectrum", Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing Virtual Event, USA, no. Oct, 2020, pp. 419-424.
[13]
Atta-ur-Rahman, S. Dash, A. Kr. Luhach, N. Chilamkurti, S. Baek, and Y. Nam,, "A Neuro-fuzzy approach for user behavior classification and prediction", J. Cloud. Comp., vol. 8, no. 1, p. 17, 2019.
[http://dx.doi.org/10.1186/s13677-019-0144-9]
[14]
S. Jarang, D. Joshi, and V.S. Deshpande, "Behavior analysis using word embedding & machine learning on social media", 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), 2019 Pune, India, 2019.
[15]
M.M. Tadesse, H. Lin, B. Xu, and L. Yang, "Personality predictions based on user behavior on the facebook social media platform", IEEE Access, vol. 6, pp. 61959-61969, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2876502]
[16]
H. Waheed, M. Anjum, M. Rehman, and A. Khawaja, "Investigation of user behavior on social networking sites", PLoS One, vol. 12, no. 2, p. e0169693, 2017.
[http://dx.doi.org/10.1371/journal.pone.0169693] [PMID: 28151963]
[17]
L. Luceri, T. Braun, and S. Giordano, "Analyzing and inferring human real-life behavior through online social networks with social influence deep learning", Appl. Netw. Sci., vol. 4, no. 1, p. 34, 2019.
[http://dx.doi.org/10.1007/s41109-019-0134-3]
[18]
R. Wang, and S. Rho, "Dynamics prediction of large-scale social network based on cooperative behavior", Sustain Cities Soc., vol. 46, p. 101435, 2019.
[http://dx.doi.org/10.1016/j.scs.2019.101435]
[19]
M. Jiang, "Behavior modeling in social networks", In: R. Alhajj, J. Rokne, Eds., Encyclopedia of Social Network Analysis and Mining., Springer: New York, NY, 2017.
[20]
L. Li, "Behavior analysis in social networks", In: R. Alhajj, J. Rokne, Eds., Encyclopedia of Social Network Analysis and Mining., Springer: New York, NY, 2017, pp. 1-10.
[21]
Z. Zhang, R. Sun, X. Wang, and C. Zhao, "A situational analytic method for user behavior pattern in multimedia social networks", IEEE Trans. Big Data, vol. 5, no. 4, pp. 520-528, 2019.
[http://dx.doi.org/10.1109/TBDATA.2017.2657623]
[22]
M. Aung, P. Seluka, J. Fuata, M. Tikoisuva, M. Cabealawa, and R. Nand, "Random forest classifier for detecting credit card fraud based on performance metrics", 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1-6, .2021,
[23]
B. Sharma, R. Nand, M. Naseem, and E. Reddy, "Effectiveness of Online Presence in a Blended Higher Learning Environment in the Pacific", Stud. High. Educ., vol. 45, no. 8, pp. 1-19, 2020.
[http://dx.doi.org/10.1080/03075079.2019.1602756]
[24]
R. Nand, A. Chand, and E. Reddy, "Data mining students’ performance in a higher learning environment", 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)., IEEE, 2022, pp. 241-245.
[25]
R. Buettner, "Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality based product recommender framework", Electron. Mark., vol. 27, no. 3, pp. 247-265, 2017.
[http://dx.doi.org/10.1007/s12525-016-0228-z]
[26]
R. Valecha, R. Chakraborty, H.R. Rao, and S. Upadhyaya, "A prediction model of privacy control for online social networking users", In: S. Chatterjee, K. Dutta, R.P. Sundarraj, Eds., Designing for a Digital and Globalized World., vol. Vol. 10844. Springer International Publishing: Cham, 2018.
[27]
Z. Gharibshah, X. Zhu, A. Hainline, and M. Conway, "Deep learning for user interest and response prediction in online display advertising", Data Sci. Eng., vol. 5, no. 1, pp. 12-26, 2020.
[http://dx.doi.org/10.1007/s41019-019-00115-y]
[28]
A. Elkhelifi, F.B. Kharrat, and R. Faiz, "Recommendation systems based on online user’s action", 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015.
[http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69]
[29]
Y. Shuqin, and F. Jing, "Fake reviews detection based on text feature and behavior feature", 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 2007-2012.
[http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00277]
[30]
Y.J. Lim, A. Osman, S.N. Salahuddin, A.R. Romle, and S. Abdullah, "Factors influencing online shopping behavior: The mediating role of purchase intention", Procedia Econ. Finance, vol. 35, pp. 401-410, 2016.
[http://dx.doi.org/10.1016/S2212-5671(16)00050-2]
[31]
D.T. Nguyen, and J.E. Jung, "Real-time event detection for online behavioral analysis of big social data", Future Gener. Comput. Syst., vol. 66, pp. 137-145, 2017.
[http://dx.doi.org/10.1016/j.future.2016.04.012]
[32]
M. Nakerekanti, and V.B. Narasimha, "Analysis on malware issues in online Social Networking Sites (SNS)", 5th International Conference on Advanced Computing Communication Systems (ICACCS), 2019.
[http://dx.doi.org/10.1109/ICACCS.2019.8728536]
[33]
N. Sun, G. Lin, J. Qiu, and P. Rimba, "Near real-time twitter spam detection with machine learning techniques", Int. J. Comput. Appl., vol. 44, no. 4, pp. 338-348, 2020.
[http://dx.doi.org/10.1080/1206212X.2020.1751387]
[34]
P. Kondeti, L. P. Yerramreddy, A. Pradhan, and G. Swain, Fake Account Detection Using Machine Learning., Springer, pp. 791-802.
[http://dx.doi.org/10.1007/978-981-15-5258-8_73]
[35]
K.S. Adewole, T. Han, W. Wu, H. Song, and A.K. Sangaiah, "Twitter spam account detection based on clustering and classification methods", J. Supercomput., vol. 76, no. 7, pp. 4802-4837, 2018.
[http://dx.doi.org/10.1007/s11227-018-2641-x]
[36]
N. Eshraqi, M. Jalali, and M.H. Moattar, "Detecting spam tweets in Twitter using a data stream clustering algorithm", 2015 International Congress on Technology, Communication and Knowledge (ICTCK) 11-12 Nov , 2015, pp. 347-351.Mashhad, Iran IEEE 2015,
[http://dx.doi.org/10.1109/ICTCK.2015.7582694]
[37]
A. Bifet, G. Holmes, B. Pfahringer, P. kranen, H. Kremer, T. Jansen, and T. Seidl, "MOA: Massive online analysis, a framework for stream classification and clustering", JMLR: Workshop and Conference Proceedings, pp. 44-50, 2010.
[38]
E.I. Setiawan, C.P. Susanto, J. Santoso, S. Sumpeno, and M.H. Purnomo, "Preliminary study of spam profile detection for social media using Markov Clustering: Case study on Javanese people", 2016 International Computer Science and Engineering Conference (ICSEC), pp. 1-4.Chiang Mai, Thailand 2016,
[http://dx.doi.org/10.1109/ICSEC.2016.7859942]
[39]
F. Ahmed, and M. Abulaish, "An MCL- based approach for spam profile detection in online social networks", Proc. 11th IEEE Int. Conf. Trust. Secur. Priv. Comput. Commun. Trust. - 11th IEEE Int. Conf. Ubiquitous Comput. Commun. IUCC-2012, 2012, pp. 602-608.
[http://dx.doi.org/10.1109/TrustCom.2012.83]
[40]
S.J. Soman, and S. Murugappan, "Detecting malicious tweets in trending topics using clustering and classification", 2014 International Conference on Recent Trends in Information Technology, pp. 1-6.
Chennai, India 2014 [http://dx.doi.org/10.1109/ICRTIT.2014.6996188]
[41]
R. Narayan, J. Rout, and S. Jena, "Review spam detection using semi-supervised technique", In: Progress in Intelligent Computing Techniques: Theory. Practice, and Applications, 2018, pp. 281-286.
[42]
X. Wang, X. Zhang, C. Jiang, and H. Liu, "Identification of fake reviews using semantic and behavioral features", 2018 4th International Conference on Information Management (ICIM), 2018, pp. 92-97.
[http://dx.doi.org/10.1109/INFOMAN.2018.8392816]
[43]
N.A. Patel, and R. Patel, "A survey on fake review detection using machine learning techniques", 2018 4th International Conference on Computing Communication and Automation (ICCCA), IEEE, 2018, pp. 1-6.
[http://dx.doi.org/10.1109/CCAA.2018.8777594]
[44]
W. Etaiwi, and G. Naymat, "The impact of applying preprocessing steps on review spam detection", The 8th international conference on emerging ubiquitous system and pervasion networks, vol. 113. 2017, pp. 273-279.
[http://dx.doi.org/10.1016/j.procs.2017.08.368]
[45]
J.K. Rout, S. Singh, S.K. Jena, and S. Bakshi, "Deceptive review detection using labeled and unlabeled data", Multimedia Tools Appl., vol. 76, no. 3, pp. 3187-3211, 2017.
[http://dx.doi.org/10.1007/s11042-016-3819-y]
[46]
S. Banerjee, A. Chua, and J. Kim, "Using supervised learning to classify authentic and fake online reviews", Proceeding of the 9th International Conference on Ubiquitous Information Management and Communication 7 January, 2015, pp. 1-7.
[http://dx.doi.org/10.1145/2701126.2701130]
[47]
L. Azaouzi, D. Rhouma, and L. Ben Romdhane, "An efficient multilevel scheme for coarsening large scale social networks", Appl. Intell., vol. 48, no. 10, pp. 3557-3576, 2018.
[http://dx.doi.org/10.1007/s10489-018-1162-x]
[48]
W. Fan, and K.H. Yeung, "Incorporating profile information in community detection for online social networks", Physica A, vol. 405, pp. 226-234, 2014.
[http://dx.doi.org/10.1016/j.physa.2014.02.075]
[49]
Z. Wang, D. Zhang, X. Zhou, D. Yang, Z. Yu, and Z. Yu, "Discovering and profiling overlapping communities in location based social networks", IEEE Trans. Syst. Man Cybern. Syst., vol. 44, no. 4, pp. 499-509, 2014.
[http://dx.doi.org/10.1109/TSMC.2013.2256890]
[50]
H. Feng, J. Tian, H.J. Wang, and M. Li, "Personalized recommendations based on time-weighted overlapping community detection", Inf. Manage., vol. 52, no. 7, pp. 789-800, 2015.
[http://dx.doi.org/10.1016/j.im.2015.02.004]
[51]
F.S. Gharehchopogh, N. Jabbari, and Z.G. Azar, Evaluation of Fuzzy K-Means And K-Means Clustering Algorithms In Intrusion Detection Systems., pp. 66-71, 2012.
[52]
Available from: http://kdd.ics.uci.edu
[53]
Z. Mingqiang, H. Hui, and W. Qian, "A graph-based clustering algorithm for anomaly intrusion detection (IEEE, 2012)", 7th International Conference on Computer Science & Education (ICCSE 2012), pp. 14-17. Melbourne, Australia, 2012
[54]
F. Adu-Oppong, C.K. Gardiner, A. Kapadia, and P.P. Tsang, "Social circles: Tackling privacy in social networks", Symposium On Usable Privacy and Security (SOUPS) 2008, July 23-25, Pittsburgh, PA, USA, 2008.
[55]
L. Fang, and K. LeFevre, "Privacy wizards for social networking sites", Proceedings of the 19th international conference on World wide web - WWW ’10, pp. 351-360 Raleigh, North Carolina, USA, ACM, 2010.
[http://dx.doi.org/10.1145/1772690.1772727]
[56]
A.K. Pandey, and D.S. Rajpoot, "Resolving cold start problem in recommendation system using demographic approach", 2016 International Conference on Signal Processing and Communication (ICSC), pp. 213-218.
Noida, India 2016 [http://dx.doi.org/10.1109/ICSPCom.2016.7980578]
[57]
P. Nair, M. Moh, and T-S. Moh, "Using social media presence for alleviating cold start problems in privacy protection", 2016 International Conference on Collaboration Technologies and Systems (CTS), Orlando, FL, USA, 2016.
[http://dx.doi.org/10.1109/CTS.2016.0022]
[58]
A. Sang, and S.K. Vishwakarma, "A ranking based recommender system for cold start & data sparsity problem", In 2017 Tenth International Conference on Contemporary Computing (IC3), 10-12 Aug. 2017, Noida, India, IEEE, 2017.
[http://dx.doi.org/10.1109/IC3.2017.8284347]
[59]
A. Bhatia, "Community detection for cold start problem in personalization: Community detection is large social network graphs based on users’ structural similarities and their attribute similarities", 2016 IEEE International Conference on Computer and Information Technology (CIT), Nadi, Fiji, 2016, pp. 167-171.
[http://dx.doi.org/10.1109/CIT.2016.24]
[60]
P.W.L. Fong, "Relationship-based access control: Protection model and policy language", Proceedings of the first ACM conference on Data and application security and privacy - CODASPY ’11, San Antonio, TX, USA, 2011, pp. 191-202.
[http://dx.doi.org/10.1145/1943513.1943539]
[61]
A.C. Squicciarini, F. Paci, and S. Sundareswaran, "PriMa: A comprehensive approach to privacy protection in social network sites", Ann. Telecommun., vol. 69, no. 1-2, pp. 21-36, 2014.
[http://dx.doi.org/10.1007/s12243-013-0371-x]
[62]
V. Sharma, and C. Dyreson, "LINKSOCIAL: Linking User Profiles Across Multiple Social Media Platforms", 2018 IEEE International Conference on Big Knowledge (ICBK), Singapore, 2018.
[http://dx.doi.org/10.1109/ICBK.2018.00042]
[63]
Y. Li, Z. Zhang, Y. Peng, H. Yin, and Q. Xu, "Matching user accounts based on user generated content across social networks", Future Gener. Comput. Syst., vol. 83, pp. 104-115, 2018.
[http://dx.doi.org/10.1016/j.future.2018.01.041]
[64]
Y. Li, Y. Peng, Z. Zhang, H. Yin, and Q. Xu, "Matching user accounts across social networks based on username and display name", World Wide Web (Bussum), vol. 22, no. 3, pp. 1075-1097, 2019.
[http://dx.doi.org/10.1007/s11280-018-0571-4]
[65]
J. Feng, "DPLink: User identity linkage via deep neural network from heterogeneous mobility data", The World Wide Web Conference on - WWW ’19, pp. 459-469.San Francisco, CA, USA 2019,
[http://dx.doi.org/10.1145/3308558.3313424]
[66]
S. Bartunov, A. Korshunov, P. Seung-Taek, W.B. Ryuunov, and H. Lee, "Joint link-attribute user identity resolution in online social networks", The 6th SNA-KDD Workshop ’12 (SNA-KDD’12) August 12, Beijing, China, 2012.
[67]
H. Gao, J. Hu, T. Huang, J. Wang, and Y. Chen, "Security issues in online social networks", IEEE Internet Comput., vol. 15, no. 4, pp. 56-63, 2011.
[http://dx.doi.org/10.1109/MIC.2011.50]
[68]
W. Xu, F. Zhang, and S. Zhu, "Toward worm detection in online social networks", IEEE Internet Comput., vol. 15, no. 4, pp. 56-63, 2011.
[http://dx.doi.org/10.1145/1920261.1920264]
[69]
A. Suhaim, and Jawad Berri, Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities., vol. 9. IEEE Access, 2021, pp. 57440-574633.
[70]
T-H. Lin, C. Gao, and Y. Li, "CROSS: Cross-platform Recommendation for Social E-Commerce", Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 515-524. Paris, France 2019.
[71]
C. Yang, H. Yan, D. Yu, Y. Li, and D.M. Chiu, "Multi-site user behavior modeling and its application in video recommendation", Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku Tokyo Japan, 2017, pp. 175-184.
[http://dx.doi.org/10.1145/3077136.3080769]
[72]
Y. Li, and Z. Su, "A comment on ‘cross-platform identification of anonymous identical users in multiple social media networks’", IEEE Trans. Knowl. Data Eng., vol. 30, no. 7, pp. 1409-1410, 2018.
[http://dx.doi.org/10.1109/TKDE.2018.2828812]
[73]
D. Perito, C. Castelluccia, M.A. Kaafar, and P. Manils, How unique and traceable are usernames?Privacy Enhancing Technologies(PETS’11),, 2011, pp. 1-17.
[http://dx.doi.org/10.1007/978-3-642-22263-4_1]
[74]
J. Liu, F. Zhang, X. Song, Y-I. Song, C-Y. Lin, and H-W. Hon, "What’s in a name?: An unsupervised approach to link users across communities", Proceedings of the sixth ACM international conference on Web search and data mining - WSDM ’13, pp. 495-504.Rome, Italy 2013,
[http://dx.doi.org/10.1145/2433396.2433457]
[75]
O. Goga, H. Lei, S.H.K. Parthasarathi, G. Friedland, R. Sommer, and R. Teixeira, "Exploiting innocuous activity for correlating users across sites", Proceedings of the 22nd international conference on World Wide Web - WWW ’13, Rio de Janeiro, Brazil, pp. 447-458.2013,
[http://dx.doi.org/10.1145/2488388.2488428]
[76]
C. Gao, T-H. Lin, N. Li, D. Jin, and Y. Li, "Cross-platform item recommendation for online social e-commerce", IEEE Trans. Knowl. Data Eng., 2021.
[http://dx.doi.org/10.1109/TKDE.2021.3098702]
[77]
R.K.W. Lee, M.S. Hee, P.K. Prasetyo, and E-P. Lim, "Linky: Visualizing user identity linkage results for multiple online social networks", 2019 IEEE International Conference on Data Mining Workshops (ICDMW), 17-20 Nov. 2018, Singapore, 2018.

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy