Abstract
Online Social Networks (OSNs) have recently been the subject of numerous studies that have attempted to develop effective methods for classifying and analyzing big content. Some of the key contributions of these studies to current scientific understanding include the identification of underlying topics within content (posts and messages), determination of each user’s influence and contributions, c) measurement of content quality, and extraction and analysis of users’ motives and preferences. We aimed to develop an integrative solution entailing a combination of these methodological advances within a single framework that could facilitate attribution and differentiate OSN members. Specifically, we examined peer effects within Twitter and assessed the propensity of members to alter their views on commonly discussed matters based on their exposure to alternative views expressed by respected and influential members. We availed of abundant available resources and tracked historical interactions of selected users to create a workable model that captured differences in opinions. The resulting solution enables peer influence within the online environment to be quantified and the level of investment of identified social media users in particular topics to be assessed.
Similar content being viewed by others
References
Abel F, Gao Q, Houben G-J, Tao K (2011) Semantic enrichment of twitter posts for user profile construction on the social web. In: Extended semantic web conference, pp 375–389
Alhamid, Mohammed F., Majdi Rawashdeh, Haiwei Dong, M. Anwar Hossain, and Abdulmotaleb El Saddik. Exploring latent preferences for context-aware personalized recommendation systems. IEEE Transactions on Human-Machine Systems 46(4):615–623
Al-Qurishi M, Aldrees R, AlRubaian M, Al-Rakhami M, Rahman SMM, Alamri A (2015) A new model for classifying social media users according to their behaviors. In: Web applications and networking (WSWAN), 2015 2nd world symposium on, pp 1–5
Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM conference on internet measurement conference, pp 49–62
Besel C, Schlötterer J, Granitzer M (2016) On the quality of semantic interest profiles for onine social network consumers. ACM SIGAPP Appl Comput Rev 16:5–14
Besel C, Schlötterer J, Granitzer M (2016) Inferring semantic interest profiles from twitter followees: does twitter know better than your friends? In: Proceedings of the 31st Annual ACM symposium on applied computing pp 1152–1157
Chaudhary P et al (2016) XSS detection with automatic view isolation on online social network. Consumer electronics, 2016 I.E. 5th global conference on. IEEE
Chaudhary P et al (2017) A novel framework to alleviate dissemination of xss worms in online social network (osn) using view segregation. Neural Netw World 27(1):5
Chen M, Zhang Y, Li Y, Mao S, Leung V (2015) EMC: emotion-aware mobile cloud computing in 5G. IEEE Netw 29(2):32–38
Chen M, Ma Y, Hao Y, Li Y, Wu D, Zhang Y, Song E (2016) CP-robot: cloud-assisted pillow robot for emotion sensing and interaction. Industrialiot 2016, Guangzhou, China
Chu Z, Gianvecchio S, Wang H, Jajodia S (2012) Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans Dependable Secure Comput 9:811–824
Dougnon RY, Fournier-Viger P, Nkambou R (2015) Inferring user profiles in online social networks using a partial social graph. In: Canadian Conference on Artificial Intelligence, pp 84–99
Eason G, Noble B, Sneddon IN (1955) On certain integrals of Lipschitz-Hankel type involving products of Bessel functions. Philos Trans R Soc Lond A247:529–551 references
Fang Q, Xu C, Sang J, Hossain MS, Muhammad G (2015) Word-of-mouth understanding: entity-centric multimodal aspect-opinion mining in social media. IEEE Trans Multimed 17:2281–2296
Fang Q, Sang J, Xu C, Shamim Hossain M (2015) Relational user attribute inference in social media. IEEE Trans Multimed 17(7):1031–1044
Gupta BB, Agrawal DP (2016) Shingo Yamaguchi, "handbook of research on modern cryptographic solutions for computer and cyber security," IGI global. Publisher, USA
Hossain MS, El Saddik A (2008) A biologically-inspired multimedia content repurposing system in heterogeneous network environments. ACM/Springer Multimed Syst J 14(3):135–143
Hossain MS, Muhammad G, Al Hamid MF, Song B (2016) Audio-visual emotion-aware big data recognition towards 5G. Mob Netw Appl 21(5):753–763
Hossain MS, Alhamid MF, Muhammad G (2017) Collaborative analysis model for trending images on social networks. Futur Gener Comput Syst
Java A, Song X, Finin T, Tseng B (2007) Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on web mining and social network analysis, pp 56–65
Jiang J, Wilson C, Wang X, Sha W, Huang P, Dai Y et al (2013) Understanding latent interactions in online social networks. ACM Trans Web (TWEB) 7:18
Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web, pp 591–600
Li J, Yan H et al Location-sharing systems with enhanced privacy in mobile online social networks. IEEE Syst J. https://doi.org/10.1109/JSYST.2015.2415835
Lo SL, Chiong R, Cornforth D (2016) Ranking of high-value social audiences on twitter. Decis Support Syst 85:34–48
Miller JH, Page SE (2004) The standing ovation problem. Complexity 9:8–16
Min W, Bao B-K, Xu C, Hossain MS (2015) Cross-platform multi-modal topic modeling for personalized inter-platform recommendation. IEEE Trans Multimed 17:1787–1801
Peng, Min, Jiajia Huang, Hui Fu, Jiahui Zhu, Li Zhou, Yanxiang He, Fei Li (2013) High quality microblog extraction based on multiple features fusion and time-frequency transformation. In International Conference on Web Information Systems Engineering, pp. 188–201. Springer, Berlin, Heidelberg
Peng M, Gao B, Zhu J, Huang J, Yuan M, Li F (2016) High quality information extraction and query-oriented summarization for automatic query-reply in social network. Expert Systems with Applications 44:92–101
Pennacchiotti M, Popescu A-M (2011) Democrats, republicans and starbucks afficionados: user classification in twitter. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 430–438
Qian S, Zhang T, Xu C, Hossain MS (2015) Social event classification via boosted multimodal supervised latent dirichlet allocation. ACM Trans Multimed Comput Commun Appl (TOMM):11–27
Rawashdeh, Majdi, Mohammad Shorfuzzaman, Abdel Monim Artoli, M. Shamim Hossain, and Ahmed Ghoneim (2017) Mining tag-clouds to improve social media recommendation. Multimed Tools and Appl 76(20) 21157–21170
Riquelme F, González-Cantergiani P (2016) Measuring user influence on Twitter: A survey. Inf Process & Manag 52:949–975
Song J, Zhang Y, Duan K, Hossain MS, Rahman SMM (2016) TOLA: topic-oriented learning assistance based on cyber-physical system and big data. Futur Gener Comput Syst 75(2017):200–205
Tao, Ke, Fabian Abel, Qi Gao, and Geert-Jan Houben. "TUMS: twitter-based user modeling service." In Extended Semantic Web Conference, pp. 269–283. Springer, Berlin, Heidelberg, 2011
Vosecky, Jan, Kenneth Wai-Ting Leung, Wilfred Ng (2012) Searching for Quality Microblog Posts: Filtering and Ranking Based on Content Analysis and Implicit Links. In DASFAA 1:397–413
Yang T, Lee D, Yan S (2013) Steeler nation, 12th man, and boo birds: classifying twitter user interests using time series. In: Advances in social networks analysis and mining (ASONAM), 2013 IEEE/ACM international conference on, pp 684–691
Yang Z, Wilson C, Wang X, Gao T, Zhao BY, Dai Y (2014) Uncovering social network sybils in the wild. ACM Trans Knowl Discov Data (TKDD) 8:2
Yang X et al (2015) Automatic visual concept learning for social event understanding. IEEE Trans Multimed 17(3):346–358
Zhang Z et al (2016) CyVOD: a novel trinity multimedia social network scheme (MTAP-D-16-01532). MTAP. Springer, New York
Zhang Z et al (2016) Social media security and trustworthiness: overview and new direction. Futur Gener Comput Syst 2016
Zuber M (2014) A survey of data mining techniques for social network analysis. Int J Res Comput Eng Electron 3(6):1–8
Acknowledgments
The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Al-Qurishi, M., Alhuzami, S., AlRubaian, M. et al. User profiling for big social media data using standing ovation model. Multimed Tools Appl 77, 11179–11201 (2018). https://doi.org/10.1007/s11042-017-5402-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-5402-6