Abstract
Nowadays, technologies cover all human life areas and expand communication platforms with suitable and low-cost space. Advertising and profiteering organizations use this large space of audience and low-cost platform to send their desired information and goals in the form of spam. In addition to creating problems for users, it causes time and bandwidth consumption. They will also be a threat to the productivity, reliability, and security of the network. Various approaches have been proposed to combat spam. The most dynamic and best methods of spam filtering are machine learning and deep learning, which perform high-speed filtering and classification of spam. In this paper, we present a new way to discover spam on various social networks by scaling up a Support Vector Machine (SVM) based on a combination of the Genetic Algorithm (GA) and Gravitational Emulation Local Search Algorithm (GELS) to select the most effective features of spam. The experiments' results show that the accuracy of the proposed method will be more optimal compared to other algorithms, and the algorithm has been able to compete with the compared algorithms.
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References
Abas AR (2011) Using incremental general regression neural network for learning mixture models from incomplete data. Egypt Inf J 12(3):185–196
Bamakan SMH, Wang H, Ravasan AZ (2016a) Parameters optimization for nonparallel support vector machine by particle swarm optimization. Procedia Comput Sci 91:482–491
Bamakan SMH, Wang H, Yingjie T, Shi Y (2016b) An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102
Barbier G, Liu H (2011) Data mining in social media, social network data analytics. Springer Science Business Media, pp 327–352
Bilecen B, Gamper M, Lubbers MJ (2018) The missing link: social network analysis in migration and transnationalism. Soc Netw 53:1–3
Bobba G (2017) Social media populism: features and ‘likeability of Lega Nord communication on Facebook. European Consortium for Political Research, pp 1–13
Bucur D, Iacca G, (2016) Influence maximization in social networks with genetic algorithms, European Conference on the Applications of Evolutionary Computation, 379–392
Cassidy W, Brown K, Jackson M (2012) Making kind cool: parents’ suggestions for preventing cyber bullying and fostering cyber kindness. J Educ Comput Res 46(4):415–436
Cranor LF, LaMacchia BA (1998) Spam! Commun ACM 41(8):74–83
Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874
Hajeer M. H, Singh A, Dasgupta D, Sanyal S, (2013) Clustering online social network communities using genetic algorithms, Social and Information Networks, 1–7
Hana L, Koenig-Archibugi M, Opsahl T (2018) The social network of international health aid. Soc Sci Med 206:67–74
He J, Shao B (2018) Examining the dynamic effects of social network advertising: a semiotic perspective. Telemat Inf 35(2):504–516
Hunter E (2012) Class list [not equal to] friend list. Educ Horiz 90(2):21–22
Jain G, Sharma M, Agarwal B (2019) Spam detection in social media using convolutional and long short term memory neural network. Ann Math Artif Intell 85:21–44
Kashikolaei SMG, Hosseinabadi AR, Saemi B, Shareh MB, Sangaiah AK, Bian G (2020) An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm. J Supercomput 76(8):6302–6329
Khodadoust J, Medina-Pérez MA, Monroy R, Khodadoust AM, Mirkamali SS (2021) A multibiometric system based on the fusion of fingerprint, finger-vein, and finger-knuckle-print. Expert Syst Appl 176:1–12
Kim J, Hastak M (2018) Social network analysis: characteristics of online social networks after a disaster. Int J Inf Manage 38(1):86–96
Kotzias D, Denil M, De Freitas N, Smyth P (2015) From group to individual labels using deep features, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 597–606
Kuang L, Zhang H, Shi R, Liao Z, Yang X (2020) A spam worker detection approach based on heterogeneous network embedding in crowdsourcing platforms. Comput Netw 183:1075–1087
Kumar N, Sonowal S (2020) Email spam detection using machine learning algorithms, In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), 108–113
Luo Q, Liu B, Yan J, He Z (2011) Design and implement a rule-based spam filtering system using neural network, International Conference on Computational and Information Sciences,.398–401
Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), 3818–3824
Mandala SR, Kumara SRT, Rao CR, Albert R (2013) Clustering social networks using ant colony Optimization. Oper Res Int J 13:47–65
Mccord M, Chuah M, (2011) Spam detection on twitter using traditional classifiers, International Conference on Autonomic and Trusted Computing, 175–186
Mekonnen DA, Gerber N, Matz JA (2018) Gendered social networks, agricultural innovations, and farm productivity in Ethiopia. World Dev 105:321–335
Noekhah S, binti SalimZakaria NNH (2020) Opinion spam detection: using multi-iterative graph-based model. Inf Process Manage 57(1):1–18
Noveiri E, Naderan M, Alavi SE (2015) Community Detection in Social Networks using Ant Colony Algorithm and Fuzzy Clustering, International Conference on Computer and Knowledge Engineering (ICCKE), 73–79
Ott M, Choi Y, Cardie C, Hancock J. T, (2011) Finding deceptive opinion spam by any stretch of the imagination, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 309–319
Ott M, Cardie C, Hancock J. T, (2013) Negative deceptive opinion spam, Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 497–501
Pandey AC, Rajpoot DS (2019) Spam review detection using spiral cuckoo search clustering method”. Evol Intel 12:147–164
Peng Z, Rastgari M, Dorostkar Navaei Y, Daraei R, Jamili Oskouei R, Pirozmand P, Mirkamali SS (2021) TCDABCF: a trust-based community detection using artificial bee colony by feature fusion. Math Probl Eng 2021:1–19
Sanadhya S, Singh S (2015) Trust calculation with ant colony optimization in online social networks. Procedia Comput Sci 54:186–195
Sangaiah AK, Hosseinabadi AR, Bozorgi SM, Rad SY, Zolfagharian A, Chilamkurti N (2020) IoT resource allocation and optimization based on heuristic algorithm. Sensors 20(2):1–26
Sattari P, Fragouli AC, Gjoka M (2013) A network coding approach to loss tomography. IEEE Trans Inf Theory 59(3):1532–1562
Shareh MB, Bargh SH, Hosseinabadi AR, Slowik A (2021) An improved bat optimization algorithm to solve the tasks scheduling problem in open shop. Neural Comput Appl 33(5):1559–1573
Shin-ike K, (2010) A two-phase method for determining the number of neurons in the hidden layer of a 3-layer neural network, Proceedings of SICE Annual Conference, 238–242
Silva R. M, Almeida T. A, Yamakami A, (2012) Artificial neural networks for content-based web spam detection, Proceedings on the International Conference on Artificial Intelligence (ICAI), 1–8
Sohrabi MK, Karimi F (2018) A feature selection approach to detect spam in the facebook social network. Arab J Sci Eng 43:949–958
Tian Y, Mirzabagheri M, Bamakan SMH, Wang H, Qu Q (2018) Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems. Neurocomputing 310:223–235
Tiana Y, Mirzabagheri M, Tirandazi P, Bamakan SMH (2020) A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM, Information Processing & Managemen, 1–13
Tsai CF, Chiou Y (2009) Earnings management prediction: a pilot study of combining neural networks and decision trees. Expert Syst Appl 36(3):7183–7191
Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge, pp 1–825
Xu J, Yang R, Wilson A, Reblin M, Clayton M, Ellington L (2018) Using social network analysis to investigate positive EOL communication. J Pain Symptom Manage 56:273–280
Zamudio E, Berdn LS, Amandi AA (2016) Social networks and genetic algorithms to choose committees with independent members. Expert Syst Appl 43:261–270
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Pirozmand, P., Sadeghilalimi, M., Hosseinabadi, A.A.R. et al. A feature selection approach for spam detection in social networks using gravitational force-based heuristic algorithm. J Ambient Intell Human Comput 14, 1633–1646 (2023). https://doi.org/10.1007/s12652-021-03385-5
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DOI: https://doi.org/10.1007/s12652-021-03385-5