A generic statistical approach for spam detection in Online Social Networks
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The established works benchmarked in Section 4 largely failed to detect the new wave of social spambots, with the notable exception of the unsupervised approach proposed in [2].
“As shown in Table 7, the established works benchmarked in Section 4 largely failed to detect the new wave of social spambots, one notable exception being the unsupervised approach proposed in [2].”
From The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017) · cites this paper as [2]
The approach of Ahmed et al. [2] failed to identify 2 distinct clusters, since accounts of both test sets were assigned to a single cluster.
“However, the approach failed to identify 2 distinct clusters, since accounts of both our test-sets were assigned to a single cluster.”
From The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017) · cites this paper as [2]
Cites
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593 · see citing works
Proceedings of the 19th international conference on World wide web
(2010) · doi:10.1145/1772690 · Cited by 907 · see citing works
Characterizing user behavior in online social networks
Benevenuto, F., Rodrigues, T., Cha, M., & Almeida, V. A. F. (2009) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1644893.1644900 · Cited by 795 · see citing works
Detecting spammers on social networks
Stringhini, G., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 26th Annual Computer Security Applications Conference · doi:10.1145/1920261.1920263 · Cited by 791 · see citing works
Alfredo Cuzzocrea, J. A., Cuzzocrea, A., Allan, J., Paton, N. W., Srivastava, D., Agrawal, R., Broder, A., Zaki, M. J., Selçuk Candan, K., Labrinidis, A., et al. (2006) · doi:10.1145/1183614 · Cited by 756 · see citing works
Graph Clustering Via a Discrete Uncoupling Process
van Dongen, S. (2008) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/040608635 · Cited by 726 · see citing works
Uncovering social spammers: social honeypots + machine learning
Lee, K., Caverlee, J., & Webb, S. (2010) · Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1835449.1835522 · Cited by 619 · see citing works
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866311 · Cited by 558 · see citing works
All your contacts are belong to us
Bilge, L., Strufe, T., Balzarotti, D., & Kirda, E. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526784 · Cited by 551 · see citing works
Design and Evaluation of a Real-Time URL Spam Filtering Service
Thomas, K., Grier, C., Ma, J. H., Paxson, V., & Song, D. X. (2011) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2011.25 · Cited by 470 · see citing works
DON’T FOLLOW ME - Spam Detection in Twitter
Wang, A. H. (2010) · International Conference on Security and Cryptography · doi:10.5220/0002996201420151 · Cited by 469 · see citing works
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter
Lee, K., Eoff, B. D., & Caverlee, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14106 · Cited by 448 · see citing works
Suspended accounts in retrospect: an analysis of twitter spam
Thomas, K., Grier, C., Song, D., & Paxson, V. (2011) · Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference · doi:10.1145/2068816.2068840 · Cited by 439 · see citing works
Detecting and characterizing social spam campaigns
Gao, H., Hu, J. S., Wilson, C., Li, Z., Chen, Y., & Zhao, B. Y. (2010) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1879141.1879147 · Cited by 420 · see citing works
Proceedings of the 18th ACM conference on Computer and communications security
Chen, Y., Danezis, G., & Shmatikov, V. (2011) · doi:10.1145/2046707 · Cited by 339 · see citing works
Spam Detection on Twitter Using Traditional Classifiers
McCord, M., & Chuah, M. C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23496-5_13 · Cited by 269 · see citing works
Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers
Yang, C., Harkreader, R. C., & Gu, G. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-23644-0_17 · Cited by 235 · see citing works
Understanding latent interactions in online social networks
Jiang, J., Wilson, C., Wang, X., Huang, P., Sha, W., Dai, Y., & Zhao, B. Y. (2010) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1879141.1879190 · Cited by 205 · see citing works
Measurement-calibrated graph models for social network experiments
Sala, A., Cao, L., Wilson, C., Zablit, R., Zheng, H.-T., & Zhao, B. Y. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772778 · Cited by 174 · see citing works
Proceedings of the 26th Annual Computer Security Applications Conference
(2010) · doi:10.1145/1920261 · Cited by 166 · see citing works
Uncovering social network sybils in the wild
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2011) · Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference · doi:10.1145/2068816.2068841 · Cited by 164 · see citing works
Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Allman, M. (2010) · doi:10.1145/1879141 · Cited by 154 · see citing works
The Koobface botnet and the rise of social malware
Thomas, K., & Nicol, D. M. (2010) · International Conference on Malicious and Unwanted Software · doi:10.1109/malware.2010.5665793 · Cited by 122 · see citing works
Jin, X. Y., Lin, C. X., Luo, J., & Han, J. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/3402755.3402795 · Cited by 94 · see citing works
Stegobot: A Covert Social Network Botnet
Nagaraja, S., Houmansadr, A., Piyawongwisal, P., Singh, V., Agarwal, P., & Borisov, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24178-9_21 · Cited by 92 · see citing works
Social Network-Based Botnet Command-and-Control: Emerging Threats and Countermeasures
Kartaltepe, E. J., Morales, J. A., Xu, S., & Sandhu, R. S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13708-2_30 · Cited by 89 · see citing works
An MCL-Based Approach for Spam Profile Detection in Online Social Networks
Ahmed, F., & Abulaish, M. (2012) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2012.83 · Cited by 81 · see citing works
Self-citationContent-driven detection of campaigns in social media
Lee, K., Caverlee, J., Cheng, Z., & Sui, D. Z. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063658 · Cited by 35 · see citing works
Uncovering Social Network Sybils in the WildPreprint
Yang, Z. C., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1106.5321 · Cited by 16 · see citing works
Proceedings of the 13th international conference on Information hiding
Filler, T., Pevný, T., Craver, S. A., & Ker, A. D. (2011) · Cited by 12 · see citing works
Flagged first, then by citations
2 of the 33 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 461 · see citing works
“Academia has mostly focused its attention on spammers, those accounts actively putting their efforts in spreading malware, sending spam, and advertising activities of doubtful legality [1, 2, 3, 4].”
BackgroundArming the public with artificial intelligence to counter social bots
Yang, K., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2019) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.115 · Cited by 419 · see citing works
“Early attempts combined multiple features via Euclidean distance and then applied clustering or network community algorithms (Ahmed and Abulaish, 2013; Miller et al., 2014).”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381 · see citing works
Social network security: Issues, challenges, threats, and solutions
Rathore, S., Sharma, P. K., Loia, V., Jeong, Y.-S., & Park, J. H. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.063 · Cited by 256 · see citing works
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · doi:10.48550/arxiv.1701.03017 · Cited by 237 · see citing works
Detection of Bots in Social Media: A Systematic Review
Orabi, M., Mouheb, D., Aghbari, Z. A., & Kamel, I. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102250 · Cited by 232 · see citing works
Bots and Misinformation Spread on Social Media: Implications for COVID-19
Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M. M., Ungar, L., Schwartz, H. A., Epstein, D. H., Leggio, L., & Curtis, B. L. (2021) · Journal of Medical Internet Research · doi:10.2196/26933 · Cited by 238 · see citing works
(2020) · Sabanci University · Cited by 197 · see citing works
DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.29 · Cited by 194 · see citing works
“However, although achieving an overall good performance for the considered spambots, the approach in [9] might lack reusability across other groups of spambots with different behaviors, such as those perpetrating a follower fraud [5],[11].”
BackgroundMethodInfluentialA new direction in social network analysis: Online social network analysis problems and applications
Can, Ü., & Alataş, B. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122372 · Cited by 193 · see citing works
Twitter spam detection: Survey of new approaches and comparative study
Wu, T., Wen, S., Xiang, Y., & Zhou, W. (2017) · Computers & Security · doi:10.1016/j.cose.2017.11.013 · Cited by 174 · see citing works
Twitter Bot Detection Using Bidirectional Long Short-Term Memory Neural Networks and Word Embeddings
Wei, F., & Nguyen, U. T. (2019) · 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa48467.2019.00021 · Cited by 168 · see citing works
Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2681672 · Cited by 168 · see citing works
Malicious accounts: Dark of the social networks
Adewole, K. S., Anuar, N. B., Kamsin, A., Varathan, K. D., & Razak, S. A. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.11.030 · Cited by 163 · see citing works
Detection of Novel Social Bots by Ensembles of Specialized Classifiers
Sayyadiharikandeh, M., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3412698 · Cited by 159 · see citing works
“They are especially suitable for finding co-ordination among bots [1, 29, 32].”
BackgroundUncovering Coordinated Networks on Social Media: Methods and Case Studies
Pacheco, D., Hui, P.-M., Torres-Lugo, C., Truong, B. T., Flammini, A., & Menczer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18075 · Cited by 140 · see citing works
A Hybrid Approach for Detecting Automated Spammers in Twitter
Fazil, M., & Abulaish, M. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2825958 · Cited by 137 · see citing works
Self-citationA one-class classification approach for bot detection on Twitter
Rodríguez‐Ruiz, J., Mata-Sánchez, J. I., Monroy, R., Loyola‐González, O., & López‐Cuevas, A. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101715 · Cited by 121 · see citing works
Detecting Social Bots on Twitter: A Literature Review
Alothali, E., Zaki, N., Mohamed, E. A., & Alashwal, H. (2018) · 2018 International Conference on Innovations in Information Technology (IIT) · doi:10.1109/innovations.2018.8605995 · Cited by 102 · see citing works
Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection
Heidari, M., & Jones, J. H. (2020) · 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298158 · Cited by 102 · see citing works
Heidari, M., Jones, J. H., & Uzuner, Ö. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00071 · Cited by 99 · see citing works
Twitter spam account detection based on clustering and classification methods
Adewole, K. S., Han, T., Wu, W., Song, H. H., & Sangaiah, A. K. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-018-2641-x · Cited by 95 · see citing works
Detection of malicious social bots: A survey and a refined taxonomy
Latah, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113383 · Cited by 94 · see citing works
Feng, S., Wan, H., Wang, N., Li, J., & Luo, M. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3481949 · Cited by 96 · see citing works
Contrast Pattern-Based Classification for Bot Detection on Twitter
Loyola‐González, O., Monroy, R., Rodríguez‐Ruiz, J., López‐Cuevas, A., & Mata-Sánchez, J. I. (2019) · IEEE Access · doi:10.1109/access.2019.2904220 · Cited by 80 · see citing works
An efficient hybrid system for anomaly detection in social networks
Rahman, M. S., Halder, S., Uddin, M. A., & Acharjee, U. K. (2021) · Cybersecurity · doi:10.1186/s42400-021-00074-w · Cited by 80 · see citing works
DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot Detection
Fazil, M., Sah, A. K., & Abulaish, M. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102498 · Cited by 86 · see citing works
Self-citationEgo network structure in online social networks and its impact on information diffusion
Arnaboldi, V., Conti, M., Gala, M. L., Passarella, A., & Pezzoni, F. (2015) · Computer Communications · doi:10.1016/j.comcom.2015.09.028 · Cited by 80 · see citing works
Kaur, R., Singh, S., & Kumar, H. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.03.015 · Cited by 76 · see citing works
A Feature Selection Approach to Detect Spam in the Facebook Social Network
Sohrabi, M. K., & Karimi, F. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-017-2855-x · Cited by 72 · see citing works
Rathore, S., Loia, V., & Park, J. H. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.032 · Cited by 73 · see citing works
A Social Bots Detection Model Based on Deep Learning Algorithm
Ping, H., & Qin, S. (2018) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2018.8600029 · Cited by 64 · see citing works
Barushka, A., & Hájek, P. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04331-5 · Cited by 65 · see citing works
Social media bot detection with deep learning methods: a systematic review
Hayawi, K., Saha, S., Masud, M. M., Mathew, S. S., & Kaosar, M. G. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08352-z · Cited by 61 · see citing works
Political Bots and the Swedish General Election
Fernquist, J., Kaati, L., & Schroeder, R. (2018) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2018.8587347 · Cited by 57 · see citing works
Discovering social spammers from multiple views
Shen, H., Ma, F., Zhang, X., Zong, L., Liu, X., & Liang, W. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.11.013 · Cited by 55 · see citing works
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2019) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3292522.3326030 · Cited by 52 · see citing works
Behavioral analysis and classification of spammers distributing pornographic content in social media
Singh, M., Bansal, D., & Sofat, S. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0350-0 · Cited by 47 · see citing works
Singh, M., Bansal, D., & Sofat, S. (2018) · Cybernetics & Systems · doi:10.1080/01969722.2017.1412866 · Cited by 44 · see citing works
Segregating Spammers and Unsolicited Bloggers from Genuine Experts on Twitter
Khan, M. U. S., Ali, M., Abbas, A., Khan, S. U., & Zomaya, A. Y. (2016) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2616879 · Cited by 42 · see citing works
Twitter Content-Based Spam Filtering
Santos, I., Miñambres-Marcos, I., Laorden, C., Galán-García, P., Santamaría-Ibirika, A., & Bringas, P. G. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-01854-6_46 · Cited by 42 · see citing works
Detecting spam tweets in Twitter using a data stream clustering algorithm
Eshraqi, N., Jalali, M., & Moattar, M. H. (2015) · International Congress on Technology, Communication and Knowledge · doi:10.1109/ictck.2015.7582694 · Cited by 43 · see citing works
Dutta, S., Ghatak, S., Dey, R., Das, A. K., & Ghosh, S. (2018) · Social Network Analysis and Mining · doi:10.1007/s13278-017-0484-8 · Cited by 38 · see citing works
Profile Similarity Recognition in Online Social Network using Machine Learning Approach
Soumya, T. R., Manohar, S. S., Ganapathy, N. B. S., Nelson, L., Mohan, A., & Pandian, M. T. (2022) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca54612.2022.9985683 · Cited by 36 · see citing works
Fake Account Identification in Social Networks
Caruccio, L., Desiato, D., & Polese, G. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622011 · Cited by 35 · see citing works
Review of Machine Learning and Data Mining Methods to Predict Different Cyberattacks
Chayal, N. M., & Patel, N. P. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-4474-3_5 · Cited by 30 · see citing works
Twitter Spam Detection Using Naïve Bayes Classifier
Santoshi, K., Bhavya, S., Sri, Y., & Venkateswarlu, B. S. S. (2021) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict50816.2021.9358579 · Cited by 30 · see citing works
Uncovering Coordinated Networks on Social Media.
Pacheco, D. F., Hui, P.-M., Torres-Lugo, C., Truong, B. T., Flammini, A., & Menczer, F. (2020) · Cited by 29 · see citing works
Detecting Malicious Users in Twitter using Classifiers
Singh, M., Bansal, D., & Sofat, S. (2014) · International Conference on Security of Information and Networks (SIN) · doi:10.1145/2659651.2659736 · Cited by 28 · see citing works
Sumathi, S., & Pugalendhi, G. K. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02087-8 · Cited by 28 · see citing works
RetractedChoi, J., & Jeon, C. (2021) · IEEE Access · doi:10.1109/access.2021.3098799 · Cited by 28 · see citing works
VASSL: A Visual Analytics Toolkit for Social Spambot Labeling
Khayat, M., Karimzadeh, M., Zhao, J. L., & Ebert, D. S. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934266 · Cited by 27 · see citing works
Rostami, R. R. (2020) · Webology · doi:10.14704/web/v17i1/a204 · Cited by 26 · see citing works
Twitter User Profiling: Bot and Gender Identification
Kosmajac, D., & Kešelj, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58219-7_13 · Cited by 26 · see citing works
Elakkiya, E., Selvakumar, S., & Velusamy, R. L. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02640-5 · Cited by 25 · see citing works
Detection of Malicious Profiles and Protecting Users in Online Social Networks
Senthil Raja, M., & Arun Raj, L. (2022) · Wireless Personal Communications · doi:10.1007/s11277-021-08095-x · Cited by 25 · see citing works
Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks
Wang, C., Zhu, H., & Yang, B. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3092007 · Cited by 27 · see citing works
Machine learning algorithm-based spam detection in social networks
Sumathi, M., & Raja, S. P. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01108-6 · Cited by 29 · see citing works
Fake news detection on social networks using Machine learning techniques
Raja, M. S., & Raj, L. A. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.03.351 · Cited by 24 · see citing works
An Empirical Study for Detecting Fake Facebook Profiles Using Supervised Mining Techniques
Albayati, M. B., & Altamimi, A. M. (2019) · Informatica · doi:10.31449/inf.v43i1.2319 · Cited by 26 · see citing works
Twitter Bot Detection with Reduced Feature Set
Abreu, J. V. F., Ralha, C. G., & Gondim, J. J. C. (2020) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi49825.2020.9280525 · Cited by 23 · see citing works
Spammer Detection Approaches in Online Social Network (OSNs): A Survey
Sahoo, S. R., Gupta, B. B., Peraković, D., García‐Peñalvo, F. J., & Cvitić, I. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-90462-3_11 · Cited by 22 · see citing works
Ruíz-Núñez, C., Segado-Fernández, S., Jiménez-Gómez, B., Hidalgo, P. J. J., Magdalena, C. S. R., Pollo, M. D. C. Á., García, A. S., & Herrera‐Peco, I. (2022) · Vaccines · doi:10.3390/vaccines10081240 · Cited by 22 · see citing works
Chetan, A., Joshi, B., Dutta, H. S., & Chakraborty, T. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291010 · Cited by 22 · see citing works
Identifying Fake Facebook Profiles Using Data Mining Techniques
Albayati, M. B., & Altamimi, A. M. (2019) · Journal of ICT Research and Applications · doi:10.5614/itbj.ict.res.appl.2019.13.2.2 · Cited by 21 · see citing works
Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content
Choudhary, M., Chouhan, S. S., & Rathore, S. S. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3673236 · Cited by 20 · see citing works
Friendship Preference: Scalable and Robust Category of Features for Social Bot Detection
Moghaddam, S. H., & Abbaspour, M. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3159007 · Cited by 21 · see citing works
A supernetwork-based online post informative quality evaluation model
Chi, Y., Tang, X., Lian, Y., Dong, X., & Liu, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.12.027 · Cited by 20 · see citing works
SDHM: A hybrid model for spammer detection in Weibo
Liu, Y., Wu, B., Wang, B., & Li, G. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921699 · Cited by 20 · see citing works
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series
Mannocci, L., Cresci, S., Monreale, A., Vakali, A. I., & Tesconi, M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020363 · Cited by 19 · see citing works
A Graph-Theoretic Embedding-Based Approach for Rumor Detection in Twitter
Abulaish, M., Kumari, N., Fazil, M., & Singh, B. (2019) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3350546.3352569 · Cited by 19 · see citing works
Self-citationA sneak into the Devil's Colony - Fake Profiles in Online Social NetworksPreprint
Wani, M. A., & Jabin, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09929 · Cited by 19 · see citing works
Comparing the Impact of Social Media Regulations on News Consumption
Etta, G., Cinelli, M., Galeazzi, A., Valensise, C. M., Quattrociocchi, W., & Conti, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3171391 · Cited by 18 · see citing works
Towards Measuring the Role of Phone Numbers in Twitter-Advertised Spam
Gupta, P., Roberto Perdisci, A., & Ahamad, M. (2018) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3196494.3196516 · Cited by 18 · see citing works
Shevtsov, A., Tzagkarakis, C., Antonakaki, D., & Ioannidis, S. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19349 · Cited by 18 · see citing works
BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning
Wu, J., Ye, X., & Man, Y. (2023) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs58141.2023.10131839 · Cited by 17 · see citing works
Multimodal Detection of Bots on X (Twitter) Using Transformers
Ilias, L., Kazelidis, I. M., & Askounis, D. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3435138 · Cited by 17 · see citing works
INFOSHIELD: Generalizable Information-Theoretic Human-Trafficking Detection
Lee, M.-C., Vajiac, C., Kulshrestha, A., Lévy, S., Park, N., Jones, C., Reihaneh Rabbany, K., & Faloutsos, C. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00101 · Cited by 17 · see citing works
An Evolutionary Computation Approach for Twitter Bot Detection
Rovito, L., Bonin, L., Manzoni, L., & Lorenzo, A. D. (2022) · Applied Sciences · doi:10.3390/app12125915 · Cited by 18 · see citing works
Identifying active, reactive, and inactive targets of socialbots in Twitter
Fazil, M., & Abulaish, M. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3106483 · Cited by 17 · see citing works
Self-citationKrithiga, R., & Ilavarasan, E. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103451 · Cited by 17 · see citing works
RetractedTwitter Bot Detection Using Neural Networks and Linguistic Embeddings
Feng, W. Q., & Nguyen, U. T. (2023) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2023.3302286 · Cited by 17 · see citing works
SDHM: a hybrid model for spammer detection in weibo
Liu, Y., Wu, B., Wang, B., & Li, G. (2014) · Advances in Social Networks Analysis and Mining · doi:10.5555/3191835.3192020 · Cited by 16 · see citing works
An Internet Water Army Detection Supernetwork Model
Lian, Y., Dong, X., Chi, Y., Tang, X., & Liu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2913005 · Cited by 16 · see citing works
Fake accounts detection system based on bidirectional gated recurrent unit neural network
Benabbou, F., Boukhouima, H., & Sael, N. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i3.pp3129-3137 · Cited by 16 · see citing works
Twitter Bot Detection using Diversity Measures
Kosmajac, D., & Kešelj, V. (2019) · Cited by 15 · see citing works
Are We Secure from Bots? Investigating Vulnerabilities of Botometer
Torusdağ, M. B., Kutlu, M., & Selçuk, A. A. (2020) · doi:10.1109/ubmk50275.2020.9219433 · Cited by 15 · see citing works
It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency
Cardaioli, M., Conti, M., Sorbo, A. D., Fabrizio, E., Laudanna, S., & Visaggio, C. A. (2021) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn52240.2021.9522339 · Cited by 15 · see citing works
Followers or Fradulents? An Analysis and Classification of Twitter Followers Market Merchants
Singh, M., Bansal, D., & Sofat, S. (2016) · Cybernetics & Systems · doi:10.1080/01969722.2016.1237227 · Cited by 15 · see citing works
A socialbots analysis-driven graph-based approach for identifying coordinated campaigns in twitter
Fazil, M., & Abulaish, M. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-182895 · Cited by 14 · see citing works
Self-citationChSO-DNFNet: Spam detection in Twitter using feature fusion and optimized Deep Neuro Fuzzy Network
Thomas, M., & Meshram, B. B. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103333 · Cited by 14 · see citing works
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot DetectionPreprint
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05132 · Cited by 17 · see citing works
Detecting spam web pages using content and link-based techniques
Roul, R. K., Asthana, S. R., SHAH, M., & Parikh, D. (2016) · Sadhana · doi:10.1007/s12046-015-0460-9 · Cited by 13 · see citing works
A novel framework for internet of knowledge protection in social networking services
Rathore, S., Sangaiah, A. K., & Park, J. H. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.12.010 · Cited by 13 · see citing works
Characterizing Social Spambots by their Human Traits
Giorgi, S., Ungar, L., & Schwartz, H. A. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.457 · Cited by 13 · see citing works
Classification of social media users with generalized functional data analysis
Weishampel, A., Staicu, A., & Rand, W. M. (2022) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2022.107647 · Cited by 13 · see citing works
Spam detection in social networks: A review
Eshraqi, N., Jalali, M., & Moattar, M. H. (2015) · International Congress on Technology, Communication and Knowledge · doi:10.1109/ictck.2015.7582661 · Cited by 12 · see citing works
Elakkiya, E., & Selvakumar, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08334-1 · Cited by 13 · see citing works
Towards the future of bot detection: A comprehensive taxonomical review and challenges on Twitter/X
Javed, D., Jhanjhi, N. Z., Khan, N. A., Ray, S. K., Mazroa, A. A., Ashfaq, F., & Das, S. R. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110808 · Cited by 13 · see citing works
Cyber Security Concerns in Social Networking Service
Hamid, A., Alam, M., Sheherin, H., & Pathan, A. K. (2022) · International Journal of Communication Networks and Information Security (IJCNIS) · doi:10.17762/ijcnis.v12i2.4634 · Cited by 12 · see citing works
- Authors
- Ahmed, F., & Abulaish, M.
- Affiliations
- King Saud University (SA)
- Corresponding author
- Abulaish, Muhammad
- Venue
- Computer Communications
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 36
- Issue
- 10-11
- Pages
- 1120-1129
- ISSN
- 0140-3664 (ISSN-L)
- OpenAlex
- W2090182207
- Cited by
- 199
- References
- 33
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 9 · 2023: 16 · 2022: 27 · 2021: 20 · 2020: 30 · 2019: 30 · 2018: 21 · 2017: 18 · 2016: 10 · 2015: 5 · 2014: 4 · 2013: 1
- FWCI
- 19.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- spam detection, social networking sites, statistical features, Facebook, Twitter, naive Bayes, J48 decision tree, JRip, spam campaigns
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
