648
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Malicious accounts detection from online social networks: a systematic review of literature

ORCID Icon & ORCID Icon
Pages 741-814 | Received 22 Dec 2020, Accepted 31 Jul 2021, Published online: 21 Sep 2021

References

  • Adewole, K. S., N. B. Anuar, A. Kamsin, K. D. Varathan, and S. A. Razak. 2017. “Malicious Accounts: Dark of the Social Networks.” Journal of Network and Computer Applications 79: 41–67.
  • Ahmed, F., and M. Abulaish. 2013. “A Generic Statistical Approach for Spam Detection in Online Social Networks.” Computer Communications 36: 1120–1129. http://www.sciencedirect.com/science/article/pii/S0140366413001047.
  • Al-Dayil, R. A., and M. H. Dahshan. 2016. “Detecting Social Media Mobile Botnets Using User Activity Correlation and Artificial Immune System.” In Proceedings of the 7th International Conference on Information and Communication Systems, 109–114. Irbid, Jordan.
  • Al-Qurishi, M., M. Alrubaian, S. M. M. Rahman, A. Alamri, and M. M. Hassan. 2018. “A Prediction System of Sybil Attack in Social Network Using Deep-Regression Model.” Future Generation Computer Systems 87: 743–753. http://www.sciencedirect.com/science/article/pii/S0167739X17300821.
  • Al-Zoubi, A. M., J. Alqatawna, and H. Faris. April 2017. “Spam Profile Detection in Social Networks Based on Public Features.” In Proceedings of the 8th International Conference on Information and Communication Systems, 130–135. Irbid, Jordan.
  • Alarifi, A., M. Alsaleh, and A. Al-Salman. 2016. “Twitter Turing Test: Identifying Social Machines.” Information Sciences 372: 332–346. http://www.sciencedirect.com/science/article/pii/S0020025516306077.
  • Albadi, N., M. Kurdi, and S. Mishra. 2019. “Hateful People Or Hateful Bots?: Detection and Characterization of Bots Spreading Religious Hatred in Arabic Social Media.” Proceedings of the ACM on Human-Computer Interaction 3: 61:1–61:25. http://doi.acm.org/https://doi.org/10.1145/3359163.
  • Almaatouq, A., E. Shmueli, M. Nouh, A. Alabdulkareem, V. K. Singh, M. Alsaleh, A. Alarifi, A. Alfaris, and A. S. Pentland. 2016. “If it Looks Like a Spammer and Behaves Like a Spammer, it Must Be a Spammer: Analysis and Detection of Microblogging Spam Accounts.” International Journal of Information Security 15: 475–491. http://dx.doi.org/https://doi.org/10.1007/s10207-016-0321-5.
  • Alom, Z., B. Carminati, and E. Ferrari. August 2018. “Detecting Spam Accounts on Twitter.” In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 1191–1198. Barcelona, Spain.
  • Alothali, E., N. Zaki, E. A. Mohamed, and H. Alashwal. November 2018. “Detecting Social Bots on Twitter: A Literature Review.” In Proceeding of the 2018 International Conference on Innovations in Information Technology (IIT), 175–180. Al Ain, United Arab Emirates.
  • Alsaleh, M., A. Alarifi, A. Al-Salman, M. Alfayez, and A. Almuhaysin. December 2014. “TSD: Detecting Sybil Accounts in Twitter.” In Proceedings of the 2014 13th International Conference on Machine Learning and Applications, 463–469. Washington, DC, USA.
  • Alvari, H., E. Shaabani, and P. Shakarian. 2018. “Early Identification of Pathogenic Social Media Accounts.” In Proceedings of the IEEE International Conference on Intelligence and Security Informatics, 169–174. Miami, FL, USA. https://doi.org/https://doi.org/10.1109/ISI.2018.8587339.
  • Amleshwaram, A. A., N. Reddy, S. Yadav, G. Gu, and C. Yang. January 2013. “CATS: Characterizing Automation of Twitter Spammers.” In Proceedings of the Fifth International Conference on Communication Systems and Networks, 1–10. Bangalore, India.
  • Andriotis, P., and A. Takasu. December 2018. “Emotional Bots: Content-Based Spammer Detection on Social Media.” In Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security, 1–8. Hong Kong.
  • Appling, S., and E. J. Briscoe. 2017. “The Perception of Social Bots by Human and Machine.” In Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, 20–25. Marco Island, Florida, US.
  • Awan, I., and B. Blakemore. 2012. Policing Cyber Hate, Cyber Threats and Cyber Terrorism. London: Ashgate Publishing Company.
  • Balakrishnan, V., S. Khan, T. Fernandez, and H. R. Arabnia. 2019. “Cyberbullying Detection on Twitter Using Big Five and Dark Triad Features.” Personality and Individual Differences 141: 252–257. http://www.sciencedirect.com/science/article/pii/S0191886919300364.
  • Balestrucci, A., R. De Nicola, O. Inverso, and C. Trubiani. 2019. “Identification of Credulous Users on Twitter.” In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC '19, 2096–2103. Limassol, Cyprus: ACM. http://doi.acm.org/https://doi.org/10.1145/3297280.3297486.
  • Bara, I. A., C. J. Fung, and T. Dinh. May 2015. “Enhancing Twitter Spam Accounts Discovery Using Cross-Account Pattern Mining.” In Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, 491–496. Ottawa, Canada.
  • Barbon Jr., S., G. F. C. Campos, G. M. Tavares, R. A. Igawa, M. L. Proença Jr., and R. C. Guido. 2018. “Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets.” ACM Transactions on Multimedia Computing, Communications, and Applications 14: 26:1–26:17. http://doi.acm.org/https://doi.org/10.1145/3183506.
  • Bellman, R. 1961. Adaptive Control Processes. Princeton, NJ: Princeton University Press.
  • Benigni, M. C., K. Joseph, and K. M. Carley. 2017. “Online Extremism and the Communities that Sustain it: Detecting the Isis Supporting Community on Twitter.” PLoS ONE 2: 1–23. https://doi.org/https://doi.org/10.1371/journal.pone.0181405.
  • Beskow, D. M., and K. M. Carley. 2018a. “Bot Conversations are Different: Leveraging Network Metrics for Bot Detection in Twitter.” In Proceedings of the IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, 825–832. Barcelona, Spain. https://doi.org/https://doi.org/10.1109/ASONAM.2018.8508322.
  • Beskow, D. M., and K. M. Carley. 2018b. “Bot-Hunter: A Tiered Approach to Detecting & Characterizing Automated Activity on Twitter.” In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS '18, 1–8. Washington, DC, USA.
  • Beskow, D. M., and K. M. Carley. 2019. “Its All in a Name: Detecting and Labeling Bots by Their Name.” Computational and Mathematical Organization Theory 25: 24–35. https://doi.org/https://doi.org/10.1007/s10588-018-09290-1.
  • Bhat, S. Y., and M. Abulaish. 2013. “Community-Based Features for Identifying Spammers in Online Social Networks.” In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '13, 100–107. Niagara, Ontario, Canada: ACM. http://doi.acm.org/https://doi.org/10.1145/2492517.2492567.
  • Boreggah, B., A. Alrazooq, M. Al-Razgan, and H. AlShabib. April 2018. “Analysis of Arabic Bot Behaviors.” In Proceedings of the 2018 21st Saudi Computer Society National Computer Conference, NCC '18, 1–6. Ar Riyad, Saudi Arabia.
  • Cai, C., L. Li, D. Zeng, and H. Ma. July 2019. “Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling.” In Proceedings of the 2019 International Joint Conference on Neural Networks, IJCNN '19, 1–8. Budapest, Hungary.
  • Cao, N., C. Shi, S. Lin, J. Lu, Y. R. Lin, and C. Y. Lin. 2016. “Targetvue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems.” IEEE Transactions on Visualization and Computer Graphics 22: 280–289.
  • Castellini, J., V. Poggioni, and G. Sorbi. 2017. “Fake Twitter Followers Detection by Denoising Autoencoder.” In Proceedings of the International Conference on Web Intelligence, WI '17, 195–202. Leipzig, Germany: ACM. http://doi.acm.org/https://doi.org/10.1145/3106426.3106489.
  • Cerón-Guzmán, J. A., and E. León. 2015. “Detecting Social Spammers in Colombia 2014 Presidential Election.” In Proceedings of the 14th Mexican International Conference on Artificial Intelligence, MICAI '15, 121–141. Cuernavaca, Morelos, Mexico: Springer International Publishing.
  • Chavoshi, N., H. Hamooni, and A. Mueen. December 2016. “DeBot: Twitter Bot Detection via Warped Correlation.” In Proceedings of the 2016 IEEE 16th International Conference on Data Mining, ICDM '16, 817–822. Barcelona, Spain.
  • Chavoshi, N., and A. Mueen. 2018. “Model Bots, Not Humans on Social Media.” In Proceedings of the IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM '18, 178–185. Barcelona, Spain. https://doi.org/https://doi.org/10.1109/ASONAM.2018.8508279.
  • Chen, H., J. Liu, Y. Lv, M. H. Li, M. Liu, and Q. Zheng. 2018. “Semi-Supervised Clue Fusion for Spammer Detection in Sina Weibo.” Information Fusion 44: 22–32. http://www.sciencedirect.com/science/article/pii/S1566253517300714.
  • Chen, Z., R. S. Tanash, R. Stoll, and D. Subramanian. 2017. “Hunting Malicious Bots on Twitter: An Unsupervised Approach.” In Proceedings of the International Conference on Social Informatics, SocInfo '17, 501–510. Oxford, United Kingdom: Springer International Publishing.
  • Chetty, N., and S. Alathur. 2018. “Hate Speech Review in the Context of Online Social Networks.” Aggression and Violent Behavior 40: 108–118.
  • Choi, H., K. Lee, and S. Webb. 2016. “Detecting Malicious Campaigns in Crowdsourcing Platforms.” In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '16, 197–202. Piscataway, NJ, USA: IEEE Press. http://dl.acm.org/citation.cfm?id=3192424.3192461.
  • Chu, Z., S. Gianvecchio, H. Wang, and S. Jajodia. 2010. “Who is Tweeting on Twitter: Human, Bot, or Cyborg?” In Proceedings of the 26th Annual Computer Security Applications Conference, ACSAC '10, 21–30. Austin, Texas, USA: ACM. http://doi.acm.org/https://doi.org/10.1145/1920261.1920265.
  • Chu, Z., S. Gianvecchio, H. Wang, and S. Jajodia. 2012. “Detecting Automation of Twitter Accounts: Are You a Human, Bot, Or Cyborg?.” IEEE Transactions on Dependable and Secure Computing 9: 811–824.
  • Chu, Z., I. Widjaja, and H. Wang. 2012. “Detecting Social Spam Campaigns on Twitter.” In Proceedings of the International Conference on Applied Cryptography and Network Security, ACNS '12, 455–472. Singapore: Springer Berlin Heidelberg.
  • Clark, E. M., W. J. Ryland, C. A. Jones, R. A. Galbraith, and D. C. M. P.S. Dodds. 2016. “Sifting Robotic From Organic Text: A Natural Language Approach for Detecting Automation on Twitter.” Journal of Computational Science 16: 1–7. https://doi.org/https://doi.org/10.1016/j.jocs.2015.11.002.
  • Concone, F., G. Lo Re, M. Morana, and C. Ruocco. June 2019. “Assisted Labeling for Spam Account Detection on Twitter.” In Proceedings of the 2019 IEEE International Conference on Smart Computing, 359–366. Bologna, Italy.
  • Costa, A. F., Y. Yamaguchi, A. J. M. Traina, C. Traina Jr., and C. Faloutsos. 2017. “Modeling Temporal Activity to Detect Anomalous Behavior in Social Media.” ACM Transactions on Knowledge Discovery From Data 11: 49:1–49:23. http://doi.acm.org/https://doi.org/10.1145/3064884.
  • Cresci, S. 2020. “A Decade of Social Bot Detection.” Communications of the ACM 63: 72–83. https://doi.org/https://doi.org/10.1145/3409116.
  • Cresci, S., R. Di Pietro, M. Petrocchi, S. Angelo, and M. Tesconi. 2020. “Emergent Properties, Models, and Laws of Behavioral Similarities Within Groups of Twitter Users.” Computer Communications 150: 47–61. http://www.sciencedirect.com/science/article/pii/S014036641930283X.
  • Cresci, S., R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi. 2015. “Fame for Sale: Efficient Detection of Fake Twitter Followers.” Decision Support Systems 80: 56–71. http://www.sciencedirect.com/science/article/pii/S0167923615001803.
  • Cresci, S., R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi. 2016. “Dna-inspired Online Behavioral Modeling and Its Application to Spambot Detection.” IEEE Intelligent Systems 31: 58–64.
  • Cresci, S., R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi. 2017. “The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race.” In Proceedings of the 26th International Conference on World Wide Web Companion, WWW '17 Companion, 963–972. Perth, Australia. International World Wide Web Conferences Steering Committee. https://doi.org/https://doi.org/10.1145/3041021.3055135.
  • Cresci, S., R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi. 2018. “Social Fingerprinting: Detection of Spambot Groups Through Dna-inspired Behavioral Modeling.” IEEE Transactions on Dependable and Secure Computing 15: 561–576.
  • Danezis, G., and P. Mittal. 2009. “SybilInfer: Detecting Sybil Nodes using Social Networks.” In Proceedings of the Network and Distributed System Security Symposium, NDSS '09, 1–15. San Diego, California, USA. https://www.ndss-symposium.org/ndss2009/sybillnfer-detecting-sybil-nodes-using-social-networks/.
  • David, I., O. S. Siordia, and D. Moctezuma. November 2016. “Features Combination for the Detection of Malicious Twitter Accounts.” In Proceedings of the 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, 1–6. Ixtapa, Mexico.
  • Denning, D. E. 2000. Cyberterrorism. Technical Report, Global Dialogue.
  • Dickerson, J. P., V. Kagan, and V. S. Subrahmanian. 2014. “Using Sentiment to Detect Bots on Twitter: Are Humans More Opinionated Than Bots?” In Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '14, 620–627. Beijing, China: IEEE Press. http://dl.acm.org/citation.cfm?id=3191835.3191957.
  • Do, T. N., P. Lenca, S. Lallich, and N. K. Pham. 2010. “Classifying Very-High-Dimensional Data with Random Forests of Oblique Decision Trees.” In Advances in Knowledge Discovery and Management, 39–55. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Domgue, F. G., N. Tsopze, and R. Ndoundam. 2020. “Community Structure Extraction in Directed Network Using Triads.” International Journal of General Systems 49: 819–842. https://doi.org/https://doi.org/10.1080/03081079.2020.1786379.
  • Dorri, A., M. Abadi, and M. Dadfarnia. August 2018. “SocialBotHunter: Botnet Detection in Twitter-Like Social Networking Services Using Semi-Supervised Collective Classification.” In Proceedings of the 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, DASC/PiCom/DataCom/CyberSciTech '18, 496–503. Athens, Greece.
  • Dugué, N., and A. Perez. 2014. “Social Capitalists on Twitter: Detection, Evolution and Behavioral Analysis.” Social Network Analysis and Mining 4: 178. https://doi.org/https://doi.org/10.1007/s13278-014-0178-4.
  • Dutta, H. S., A. Chetan, B. Joshi, and T. Chakraborty. August 2018. “Retweet Us, We will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services.” In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 242–249. Barcelona, Spain.
  • Egele, M., G. Stringhini, C. Kruegel, and G. Vigna. 2017. “Towards Detecting Compromised Accounts on Social Networks.” IEEE Transactions on Dependable and Secure Computing 14: 447–460.
  • Egele, M., G. Stringhini, C. Krügel, and G. Vigna. 2013. “COMPA: Detecting Compromised Accounts on Social Networks.” In Proceedings of the 20th Annual Network & Distributed System Security Symposium, 1–17. San Diego, CA United States: The Internet Society.
  • Fazil, M., and M. Abulaish. 2018. “A Hybrid Approach for Detecting Automated Spammers in Twitter.” IEEE Transactions on Information Forensics and Security 13: 2707–2719.
  • Feng, Y., J. Li, L. Jiao, and X. Wu. June 2019. “BotFlowMon: Learning-based, Content-Agnostic Identification of Social Bot Traffic Flows.” In Proceedings of the Conference on Communications and Network Security, 169–177. Washington, DC, USA.
  • Fernandes, M., P. Patel, and T. Marwala. 2015. “Automated Detection of Human Users in Twitter.” Procedia Computer Science 53: 224–231. Proceedings of the INNIS Conference on Big Data. http://www.sciencedirect.com/science/article/pii/S1877050915018013.
  • Fernquist, J., L. Kaati, and R. Schroeder. November 2018. “Political Bots and the Swedish General Election.” In 2018 IEEE International Conference on Intelligence and Security Informatics, 124–129. Miami, Florida, USA.
  • Ferrara, E. 2018. “Measuring Social Spam and the Effect of Bots on Information Diffusion in Social Media.” In Complex Spreading Phenomena in Social Systems: Influence and Contagion in Real-World Social Networks, 229–255. Cham: Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-319-77332-2_13.
  • Ferrara, E., O. Varol, C. Davis, F. Menczer, and A. Flammini. 2016. “The Rise of Social Bots.” Communications of the ACM 59: 96–104. http://doi.acm.org/https://doi.org/10.1145/2818717.
  • Ferraz Costa, A., Y. Yamaguchi, A. Juci Machado Traina, C. Traina Jr., and C. Faloutsos. 2015. “RSC: Mining and Modeling Temporal Activity in Social Media.” In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, 269–278. Sydney, NSW, Australia: ACM. http://doi.acm.org/https://doi.org/10.1145/2783258.2783294.
  • Fornacciari, P., M. Mordonini, A. Poggi, L. Sani, and M. Tomaiuolo. 2018. “A Holistic System for Troll Detection on Twitter.” Computers in Human Behavior 89: 258–268. http://www.sciencedirect.com/science/article/pii/S0747563218303832.
  • Fu, Q., B. Feng, D. Guo, and Q. Li. 2018. “Combating the Evolving Spammers in Online Social Networks.” Computers & Security 72: 60–73. https://doi.org/https://doi.org/10.1016/j.cose.2017.08.014.
  • Gamallo, P., and S. Almatarneh. 2019. “Naive-Bayesian Classification for Bot Detection in Twitter – Notebook for PAN at CLEF 2019.” In Proceedings of the Working Notes of CLEF 2019 – Conference and Labs of the Evaluation Forum, CLEF '19, 1–9. Lugano, Switzerland.
  • Gilani, Z., R. Farahbakhsh, G. Tyson, and J. Crowcroft. 2019. “A Large-Scale Behavioural Analysis of Bots and Humans on Twitter.” ACM Transactions on the Web 13: 7:1–7:23. http://doi.acm.org/https://doi.org/10.1145/3298789.
  • Gilani, Z., R. Farahbakhsh, G. Tyson, L. Wang, and J. Crowcroft. 2017. “Of Bots and Humans (on Twitter).” In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM '17, 349–354. Sydney, Australia: ACM. http://doi.acm.org/https://doi.org/10.1145/3110025.3110090.
  • Gong, N. Z., M. Frank, and P. Mittal. 2014. “Sybilbelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection.” IEEE Transactions on Information Forensics and Security 9: 976–987.
  • Guo, D., and C. Chen. 2014. “Detecting Non-Personal and Spam Users on Geo-Tagged Twitter Network.” Transactions in GIS 18: 370–384. https://doi.org/https://doi.org/10.1111/tgis.12101.
  • Gupta, A., and R. Kaushal. January 2017. “Towards Detecting Fake User Accounts in Facebook.” In Proceedings of the 2017 ISEA Asia Security and Privacy, ISEASP '17, 1–6. Surat, India.
  • Hall, M. A., and L. A. Smith. 1999. “Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper.” In Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, 235–239. Orlando, Florida, USA: AAAI Press.
  • Hall, A., L. Terveen, and A. Halfaker. 2018. “Bot Detection in Wikidata Using Behavioral and Other Informal Cues.” Proceedings of the ACM on Human-Computer Interaction 2: 64:1–64:18. http://doi.acm.org/https://doi.org/10.1145/3274333.
  • Hotho, A., D. Benz, R. Jäschke, and B. Krause, eds. 2008. “ECML PKDD Discovery Challenge 2008 (RSDC'08).” In Proceedings of the Workshop at 18th European Conference on Machine Learning/11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Antwerp, Belgium. http://www.kde.cs.uni-kassel.de/ws/rsdc08/pdf/all_rsdc_v2.pdf.
  • Hu, X., J. Tang, H. Gao, and H. Liu. December 2014. “Social Spammer Detection with Sentiment Information.” In 2014 IEEE International Conference on Data Mining, ICDM '14, 180–189. Shenzhen, China.
  • Hu, X., J. Tang, and H. Liu. 2014. “Online Social Spammer Detection.” In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI' 14, 59–65. Québec, Canada: AAAI Press. http://dl.acm.org/citation.cfm?id=2893873.2893884.
  • Hu, X., J. Tang, Y. Zhang, and H. Liu. 2013. “Social Spammer Detection in Microblogging.” In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, 2633–2639. Beijing, China: AAAI Press. http://dl.acm.org/citation.cfm?id=2540128.2540508.
  • Ibrahim, M., O. Abdillah, A. F. Wicaksono, and M. Adriani. 2015. “Buzzer Detection and Sentiment Analysis for Predicting Presidential Election Results in a Twitter Nation.” In Proceedings of the 2015 IEEE International Conference on Data Mining Workshop, ICDMW '15, 1348–1353. Washington, DC, USA: IEEE Computer Society. https://doi.org/http://dx.doi.org/10.1109/ICDMW.2015.113.
  • Igawa, R. A., S. Barbon Jr., K. C. S. Paulo, G. S. Kido, R. C. Guido, M. L. Proença Júnior, and I. N. da Silva. 2016. “Account Classification in Online Social Networks with LBCA and Wavelets.” Information Sciences 332: 72–83. http://www.sciencedirect.com/science/article/pii/S0020025515007732.
  • Inuwa-Dutse, I., M. Liptrott, and I. Korkontzelos. 2018. “Detection of Spam-Posting Accounts on Twitter.” Neurocomputing 315: 496–511. http://www.sciencedirect.com/science/article/pii/S0925231218308798.
  • Jang, B., S. Jeong, and C.k. Kim. 2019. “Distance-Based Customer Detection in Fake Follower Markets.” Information Systems 81: 104–116. http://www.sciencedirect.com/science/article/pii/S030643791830214X.
  • Jeong, S., G. Noh, H. Oh, and C.k. Kim. 2016. “Follow Spam Detection Based on Cascaded Social Information.” Information Sciences 369: 481–499. http://www.sciencedirect.com/science/article/pii/S0020025516305138.
  • Jia, J., B. Wang, and N. Z. Gong. June 2017. “Random Walk Based Fake Account Detection in Online Social Networks.” In Proceedings of the 47th IEEE/IFIP International Conference on Dependable Systems and Networks, 273–284. Denver, CO, USA.
  • Ji, Y., H. Yukun, X. Jiang, J. Cao, and Q. Li. 2016. “Combating the Evasion Mechanisms of Social Bots.” Computers & Security 58: 230–249. http://www.sciencedirect.com/science/article/pii/S0167404816300025.
  • Kaur, R., S. Singh, and H. Kumar. 2018. “Rise of Spam and Compromised Accounts in Online Social Networks: A State-of-the-Art Review of Different Combating Approaches.” Journal of Network and Computer Applications 112: 53–88.
  • Keele, S. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Ebse Technical Report, ebse-2007-01, version 2.3, Software Engineering Group, School of Computer Science and Mathematics, Keele University.
  • Khaled, S., N. El-Tazi, and H. M. O. Mokhtar. 2018. “Detecting Fake Accounts on Social Media.” In Proceedings of the IEEE International Conference on Big Data, 3672–3681. Seattle, WA, USA. https://doi.org/https://doi.org/10.1109/BigData.2018.8621913.
  • Khalil, A., H. Hajjdiab, and N. Al-Qirim. 2017. “Detecting Fake Followers in Twitter: A Machine Learning Approach.” International Journal of Machine Learning and Computing 7: 198–202.
  • Kitchenham, B. 2004. Procedures for Performing Systematic Reviews. Technical Report tr/se-0401, Department of Computer Science, Keele University, UK.
  • Kosmajac, D., and V. Keselj. 2019. “Twitter User Profiling: Bot and Gender Identification – Notebook for PAN at CLEF 2019.” In Proceedings of the Working Notes of CLEF 2019 – Conference and Labs of the Evaluation Forum, CLEF '19, 1–11. Lugano, Switzerland.
  • Kudugunta, S., and E. Ferrara. 2018. “Deep Neural Networks for Bot Detection.” Information Sciences 467: 312–322. http://www.sciencedirect.com/science/article/pii/S0020025518306248.
  • Latah, M. 2020. “Detection of Malicious Social Bots: A Survey and a Refined Taxonomy.” Expert Systems with Applications 151: Article ID: 113383. http://www.sciencedirect.com/science/article/pii/S0957417420302074.
  • Lee, K., J. Caverlee, and S. Webb. 2010. “Uncovering Social Spammers: Social Honeypots + Machine Learning.” In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '10, 435–442. Geneva, Switzerland: ACM. http://doi.acm.org/https://doi.org/10.1145/1835449.1835522.
  • Lee, K., B. D. Eoff, and J. Caverlee. 2011. “Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter.” In Proceedings of the Fifth International Conference on Weblogs and Social Media, 185–192. Barcelona, Catalonia, Spain. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2780.
  • Lee, S., and J. Kim. 2014. “Early Filtering of Ephemeral Malicious Accounts on Twitter.” Computer Communications 54: 48–57. http://www.sciencedirect.com/science/article/pii/S0140366414002850.
  • Leskovec, J., and A. Krevl. 2014. “SNAP Datasets: Stanford Large Network Dataset Collection.” http://snap.stanford.edu/data.
  • Li, C., S. Wang, L. He, P. S. Yu, Y. Liang, and Z. Li. November 2018. “SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-View Data Fusion.” In Proceedings of the 2018 IEEE International Conference on Data Mining, ICDM '18, 247–256. Singapore.
  • Lingam, G., R. R. Rout, and D. Somayajulu. December 2018. “Detection of Social Botnet using a Trust Model based on Spam Content in Twitter Network.” In Proceedings of the 2018 IEEE 13th International Conference on Industrial and Information Systems, ICIIS '18, 280–285. Rupnagar, India.
  • Loyola-González, O., R. Monroy, J. Rodríguez, A. López-Cuevas, and J. I. Mata-Sánchez. 2019. “Contrast Pattern-Based Classification for Bot Detection on Twitter.” IEEE Access 7: 45800–45817.
  • Main, W., and N. Shekokhar. 2015. “Twitterati Identification System.” Procedia Computer Science 45: 32–41. Proceedings of the International Conference on Advanced Computing Technologies and Applications. http://www.sciencedirect.com/science/article/pii/S1877050915003129.
  • Mateen, M., M. A. Iqbal, M. Aleem, and M. A. Islam. January 2017. “A Hybrid Approach for Spam Detection for Twitter.” In Proceedings of the 2017 14th International Bhurban Conference on Applied Sciences and Technology, IBCAST '17, 466–471. Islamabad, Pakistan.
  • Mazza, M., S. Cresci, M. Avvenuti, W. Quattrociocchi, and M. Tesconi. 2019. “RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter.” In Proceedings of the 10th ACM Conference on Web Science, WebSci '19, 183–192. Boston, Massachusetts, USA: ACM. http://doi.acm.org/https://doi.org/10.1145/3292522.3326015.
  • Mehrotra, A., M. Sarreddy, and S. Singh. December 2016. “Detection of Fake Twitter Followers Using Graph Centrality Measures.” In 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I '16, 499–504. Noida, India.
  • Miller, Z., B. Dickinson, W. Deitrick, W. Hu, and A. H. Wang. 2014. “Twitter Spammer Detection Using Data Stream Clustering.” Information Sciences 260: 64–73. http://www.sciencedirect.com/science/article/pii/S0020025513008037.
  • Minnich, A. J., N. Chavoshi, D. Koutra, and A. Mueen. 2017. “BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks.” In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '17, 467–474. Sydney, Australia. https://doi.org/https://doi.org/10.1145/3110025.3110163.
  • Morstatter, F., L. Wu, T. H. Nazer, K. M. Carley, and H. Liu. August 2016. “A New Approach to Bot Detection: Striking the Balance Between Precision and Recall.” In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '16, 533–540. Piscataway, NJ, USA.
  • Mulamba, D., I. Ray, and I. Ray. 2016. “SybilRadar: A Graph-Structure Based Framework for Sybil Detection in On-line Social Networks.” In ICT Systems Security and Privacy Protection, 179–193. Ghent, Belgium: Springer International Publishing.
  • Mulamba, D., I. Ray, and I. Ray. August 2018. “On Sybil Classification in Online Social Networks Using Only Structural Features.” In Proceedings of the 16th Conference on Privacy, Security and Trust, 1–10. Belfast, Northern Ireland, UK.
  • Mutlu, B., M. Mutlu, K. Oztoprak, and E. Dogdu. December 2016. “Identifying Trolls and Determining Terror Awareness Level in Social Networks Using a Scalable Framework.” In Proceedings of the IEEE International Conference on Big Data, 1792–1798. Washington, DC.
  • Nilizadeh, S., F. Labrèche, A. Sedighian, A. Zand, J. Fernandez, C. Kruegel, G. Stringhini, and G. Vigna. 2017. “POISED: Spotting Twitter Spam Off the Beaten Paths.” In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS '17, 1159–1174. Dallas, Texas, USA: ACM. http://doi.acm.org/https://doi.org/10.1145/3133956.3134055.
  • Nooralahzadeh, F., V. Arunachalam, and C. G. Chiru. May 2013. “2012 Presidential Elections on Twitter – An Analysis of How the US and French Election were Reflected in Tweets.” In Proceedings of the 19th International Conference on Control Systems and Computer Science, CSCS '13, 240–246. Bucharest, Romania.
  • Oentaryo, R. J., A. Murdopo, P. K. Prasetyo, and E. P. Lim. 2016. “On Profiling Bots in Social Media.” In Proceedings of the 8th International Conference on Social informatics, SocInfo '16, 92–109. Bellevue, WA, USA.
  • Ott, M., C. Cardie, and J. T. Hancock. 2013. “Negative Deceptive Opinion Spam.” In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 497–501. Atlanta, Georgia: Association for Computational Linguistics. https://www.aclweb.org/anthology/N13-1053.
  • Ott, M., Y. Choi, C. Cardie, and J. T. Hancock. 2011. “Finding Deceptive Opinion Spam by Any Stretch of the Imagination.” In Proceedings of the 49th Meeting of the Association for Computational Linguistics: Human Language Technologies – Volume 1, HLT '11, 309–319. USA: Association for Computational Linguistics.
  • Perdana, R. S., T. H. Muliawati, and R. Alexandro. 2015. “Bot Spammer Detection in Twitter Using Tweet Similarity and Time Interval Entropy.” Journal of Computer Science and Information 8: 19–25.
  • Perez, C., M. Lemercier, and B. Birregah. June 2013. “A Dynamic Approach to Detecting Suspicious Profiles on Social Platforms.” In Proceedings of the IEEE International Conference on Communications Workshops, ICC '13, 174–178. Budapest, Hungary.
  • Perna, D., and A. Tagarelli. 2018. “Learning to Rank Social Bots.” In Proceedings of the 29th on Hypertext and Social Media, HT '18, 183–191. Baltimore, MD, USA: ACM. http://doi.acm.org/https://doi.org/10.1145/3209542.3209563.
  • Ping, H., and S. Qin. October 2018. “A Social Bots Detection Model Based on Deep Learning Algorithm.” In Proceedings of the 2018 IEEE 18th International Conference on Communication Technology, ICCT '18, 1435–1439. Chongqing, China.
  • Pizarro, J. 2019. “Using N-Grams to detect Bots on Twitter – Notebook for PAN at CLEF 2019.” In Proceedings of the Working Notes of CLEF 2019 – Conference and Labs of the Evaluation Forum, CLEF '19, 1–11. Lugano, Switzerland.
  • Przybyla, P. 2019. “Detecting Bot Accounts on Twitter by Measuring Message Predictability – Notebook for PAN at CLEF 2019.” In Proceedings of the Working Notes of CLEF 2019 – Conference and Labs of the Evaluation Forum, CLEF '19, 1–10. Lugano, Switzerland.
  • Rathore, S., V. Loia, and J. H. Park. 2018. “Spamspotter: An Efficient Spammer Detection Framework Based on Intelligent Decision Support System on Facebook.” Applied Soft Computing 67: 920–932. http://www.sciencedirect.com/science/article/pii/S1568494617305719.
  • Rowe, N. C. 2008. “The Ethics of Deception in Cyberspace.” https://calhoun.nps.edu/handle/10945/36581.
  • Sadiq, S., Y. Yan, A. Taylor, M. L. Shyu, S. C. Chen, and D. Feaster. August 2017. “AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter.” In Proceedings of the 2017 IEEE International Conference on Information Reuse and Integration, IRI '17, 356–365. San Diego, CA, USA.
  • Saeed, U., and F. Shirazi. 2019. “Bots and Gender Classification on Twitter – Notebook for PAN at CLEF 2019.” In Proceedings of the Working Notes of CLEF 2019 – Conference and Labs of the Evaluation Forum, CLEF '19, 1–8. Lugano, Switzerland.
  • Sahoo, S. R., and B. Gupta. 2019. “Hybrid Approach for Detection of Malicious Profiles in Twitter.” Computers & Electrical Engineering 76: 65–81. http://www.sciencedirect.com/science/article/pii/S0045790618322766.
  • Santia, G. C., M. I. Mujib, and J. R. Williams. 2019. “Detecting Social Bots on Facebook in an Information Veracity Context.” In Proceedings of the Thirteenth International Conference on Web and Social Media, ICWSM '19, 463–472. Munich, Germany. https://aaai.org/ojs/index.php/ICWSM/article/view/3244.
  • Saquete, E., D. Tomás, M. Paloma, M. B. Patricio, and M. Palomar. 2020. “Fighting Post-Truth Using Natural Language Processing: A Review and Open Challenges.” Expert Systems with Applications 141: Article ID: 112943.
  • Schnebly, J., and S. Sengupta. January 2019. “Random Forest Twitter Bot Classifier.” In Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC '19, 0506–0512. Las Vegas, NV, USA.
  • Sedhai, S., and A. Sun. 2018. “Semi-Supervised Spam Detection in Twitter Stream.” IEEE Transactions on Computational Social Systems 5: 169–175.
  • Shaabani, E., R. Guo, and P. Shakarian. April 2018. “Detecting Pathogenic Social Media Accounts without Content or Network Structure.” In Proceedings of the 2018 1st International Conference on Data Intelligence and Security, ICDIS '18, 57–64. South Padre Island, TX, USA.
  • Shen, H., F. Ma, Z. Xianchao, L. Zong, X. Liu, and W. Liang. 2017. “Discovering Social Spammers From Multiple Views.” Neurocomputing 225: 49–57. http://www.sciencedirect.com/science/article/pii/S092523121631342X.
  • Shi, P., Z. Zhang, and K. K. R. Choo. 2019. “Detecting Malicious Social Bots Based on Clickstream Sequences.” IEEE Access 7: 28855–28862.
  • Siddiqui, M. H. F., I. Ameer, A. Gelbukh, and G. Sidorov. 2019. “Bots and Gender Profiling on Twitter – Notebook for PAN at CLEF 2019.” In Proceedings of the Working Notes of CLEF 2019 – Conference and Labs of the Evaluation Forum, 1–8. Lugano, Switzerland.
  • Singh, M., D. Bansal, and S. Sofat. 2014. “Detecting Malicious Users in Twitter Using Classifiers.” In Proceedings of the 7th International Conference on Security of Information and Networks, 247:247–247:253. Glasgow, Scotland, UK: ACM. http://doi.acm.org/https://doi.org/10.1145/2659651.2659736.
  • Singh, A., and S. Batra. 2018. “Ensemble Based Spam Detection in Social IOT Using Probabilistic Data Structures.” Future Generation Computer Systems 81: 359–371. http://www.sciencedirect.com/science/article/pii/S0167739X17313134.
  • Souravlas, S., A. Sifaleras, M. Tsintogianni, and S. Katsavounis. 2021. “A Classification of Community Detection Methods in Social Networks: a Survey.” International Journal of General Systems 50: 63–91. https://doi.org/https://doi.org/10.1080/03081079.2020.1863394.
  • Stella, M., E. Ferrara, and M. De Domenico. 2018. “Bots Increase Exposure to Negative and Inflammatory Content in Online Social Systems.” Proceedings of the National Academy of Sciences 115: 12435–12440. https://doi.org/https://doi.org/10.1073/pnas.1803470115.
  • Stringhini, G., C. Kruegel, and G. Vigna. 2010. “Detecting Spammers on Social Networks.” In Proceedings of the 26th Annual Computer Security Applications Conference, ACSAC '10, 1–9. Austin, Texas, USA: ACM. http://doi.acm.org/https://doi.org/10.1145/1920261.1920263.
  • Stukal, D., S. Sanovich, R. Bonneau, and J. A. Tucker. 2017. “Detecting Bots on Russian Political Twitter.” Big Data 5: 310–324.
  • Subrahmanian, V. S., A. Azaria, S. Durst, V. Kagan, A. Galstyan, K. Lerman, L. Zhu, E. Ferrara, A. Flammini, and F. Menczer. 2016. “The Darpa Twitter Bot Challenge.” Computer 49: 38–46. https://doi.org/https://doi.org/10.1109/MC.2016.183.
  • Teljstedt, C., M. Rosell, and F. Johansson. September 2015. “A Semi-Automatic Approach for Labeling Large Amounts of Automated and Non-automated Social Media User Accounts.” In Proceedings of the 2015 Second European Network Intelligence Conference, 155–159. Karlskrona, Sweden.
  • Thelwall, M. 2009. “Chapter 2 Social Network Sites: Users and Uses.” In Social Networking and The Web, 19–73. Advances in Computers Vol. 76. Elsevier. https://www.sciencedirect.com/science/article/pii/S006524580901002X.
  • Tsikerdekis, M., T. Morse, C. Dean, and J. Ruffin. 2019. “A Taxonomy of Features for Preventing Identity Deception in Online Communities and Their Estimated Efficacy.” Journal of Information Security and Applications 47: 363–370. http://www.sciencedirect.com/science/article/pii/S2214212619300778.
  • Van Der Walt, E., and J. Eloff. 2018. “Using Machine Learning to Detect Fake Identities: Bots Vs Humans.” IEEE Access 6: 6540–6549.
  • Van der Walt, E., J. Eloff, and J. Grobler. 2018. “Cyber-Security: Identity Deception Detection on Social Media Platforms.” Computers & Security 78: 76–89. http://www.sciencedirect.com/science/article/pii/S0167404818306503.
  • Varol, O., E. Ferrara, C. A. Davis, F. Menczer, and A. Flammini. 2017. “Online Human-Bot Interactions: Detection, Estimation, and Characterization.” In Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM '17, 280–289. Montréal, Québec, Canada: AAAI Press. https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587.
  • Viswanath, B., M. A. Bashir, M. Crovella, S. Guha, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. 2014. “Towards Detecting Anomalous User Behavior in Online Social Networks.” In Proceedings of the 23rd USENIX Conference on Security Symposium, SEC '14, 223–238. Berkeley, CA, USA: USENIX Association. http://dl.acm.org/citation.cfm?id=2671225.2671240.
  • Volkova, S., and E. Bell. 2017. “Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter Across Languages.” In Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM '17, 290–298. Montréal, Québec, Canada: AAAI Press. https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15591.
  • Volkova, S., and E. Bell. June 2016. “Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis.” In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, NAACL-HLT '16, 1–6. San Diego, California: Association for Computational Linguistics. https://www.aclweb.org/anthology/W16-0801.
  • Voorhees, E. M., ed. 2013. Proceedings of The Twenty-Second Text REtrieval Conference, TREC 2013, Gaithersburg, Maryland, USA, November 19–22, 2013, Vol. Special Publication 500-302. National Institute of Standards and Technology (NIST). http://trec.nist.gov/pubs/trec22/trec2013.html.
  • Wagner, C., S. Mitter, M. Strohmaier, and C. Körner. 2012. “When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks.” In Proceedings of the 2nd WWW'12 Workshop on ‘Making Sense of Microposts’, MSM '12, 41–48. Lyon, France.
  • Wang, A. H. 2010a. “Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach.” In Proceedings of the IFIP Annual Conference on Data and Applications Security and Privacy, DBSec '10, 335–342. Rome, Italy: Springer Berlin Heidelberg.
  • Wang, A. H. July 2010b. “Don't Follow Me: Spam Detection in Twitter.” In Proceedings of the 2010 International Conference on Security and Cryptography, SECRYPT '10, 1–10. Athens, Greece.
  • Wang, T., l.C. Chen, and Y. Genc. 2018. “An N-Gram-Based Approach for Detecting Social Media Spambots.” In Proceedings of the 2018 Symposium on Decision Analytics Connecting People, Data & Things, 1–16. San Francisco, California. https://aisel.aisnet.org/sigdsa2018/10.
  • Wang, B., J. Jia, L. Zhang, and N. Z. Gong. 2019. “Structure-Based Sybil Detection in Social Networks Via Local Rule-Based Propagation.” IEEE Transactions on Network Science and Engineering 6: 523–537.
  • Wang, G., M. Mohanlal, C. Wilson, X. Wang, M. J. Metzger, H. Zheng, and B. Y. Zhao. 2013. “Social Turing Tests: Crowdsourcing Sybil Detection.” In Proceedings of the 20th Network and Distributed System Security Symposium, 1–16. San Diego, California, USA.
  • Wang, G., C. Wilson, X. Zhao, Y. Zhu, M. Mohanlal, H. Zheng, and B. Y. Zhao. 2012. “Serf and Turf: Crowdturfing for Fun and Profit.” In Proceedings of the 21st International Conference on World Wide Web, WWW '12, 679–688. New York, NY, USA: Association for Computing Machinery. https://doi.org/https://doi.org/10.1145/2187836.2187928.
  • Wang, Y., C. Wu, K. Zheng, and X. Wang. 2018. “Social Bot Detection Using Tweets Similarity.” In Proceedings of the International Conference on Security and Privacy in Communication Systems, 63–78. Singapore: Springer International Publishing.
  • Wang, B., A. Zubiaga, M. Liakata, and R. Procter. 2015. “Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter.” In In Proceedings of the the 5th Workshop on Making Sense of Microposts, Microposts '15, 10–16. Florence, Italy.
  • Washha, M., A. Qaroush, M. Mezghani, and F. Sedes. 2017. “A Topic-Based Hidden Markov Model for Real-Time Spam Tweets Filtering.” Procedia Computer Science 112: 833–843. Proceedings of the 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. http://www.sciencedirect.com/science/article/pii/S1877050917314199.
  • Washha, M., A. Qaroush, M. Mezghani, and F. Sedes. 2019. “Unsupervised Collective-Based Framework for Dynamic Retraining of Supervised Real-Time Spam Tweets Detection Model.” Expert Systems with Applications 135: 129–152. http://www.sciencedirect.com/science/article/pii/S0957417419303872.
  • Wu, X., Z. Feng, W. Fan, J. Gao, and Y. Yu. 2013. “Detecting Marionette Microblog Users for Improved Information Credibility.” In Proceedings of the 2013th European Conference on Machine Learning and Knowledge Discovery in Databases – Volume Part III, ECMLPKDD'13, 483–498. Prague, Czech Republic: Springer-Verlag.
  • Wu, F., J. Shu, Y. Huang, and Z. Yuan. 2016. “Co-Detecting Social Spammers and Spam Messages in Microblogging Via Exploiting Social Contexts.” Neurocomputing 201: 51–65. https://doi.org/http://dx.doi.org/10.1016/j.neucom.2016.03.036.
  • Xiao, C., D. M. Freeman, and T. Hwa. 2015. “Detecting Clusters of Fake Accounts in Online Social Networks.” In Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, AISec '15, 91–101. Denver, Colorado, USA: ACM. http://doi.acm.org/https://doi.org/10.1145/2808769.2808779.
  • Yang, C., R. C. Harkreader, and G. Gu. 2011. “Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers.” In Proceedings of the International Workshop on Recent Advances in Intrusion Detection, RAID' 11, 318–337. Menlo Park, CA, USA: Springer Berlin Heidelberg.
  • Yang, C., R. Harkreader, and G. Gu. 2013. “Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers.” IEEE Transactions on Information Forensics and Security 8: 1280–1293.
  • Yang, K. C., O. Varol, C. A. Davis, E. Ferrara, A. Flammini, and F. Menczer. 2019. “Arming the Public with Artificial Intelligence to Counter Social Bots.” Human Behavior and Emerging Technologies 1: 48–61. https://doi.org/https://doi.org/10.1002/hbe2.115.
  • Ye, S., J. Lang, and F. Wu. 2010. “Crawling Online Social Graphs.” In Proceedings of the 2010 12th International Asia-Pacific Web Conference, APWEB '10, 236–242. USA: IEEE Computer Society. https://doi.org/https://doi.org/10.1109/APWeb.2010.10.
  • Yu, D., N. Chen, F. Jiang, B. Fu, and A. Qin. 2017. “Constrained NMF-Based Semi-Supervised Learning for Social Media Spammer Detection.” Knowledge-Based Systems 125: 64–73. http://www.sciencedirect.com/science/article/pii/S0950705117301533.
  • Zangerle, E., and G. Specht. 2014. ““Sorry, I Was Hacked”: A Classification of Compromised Twitter Accounts.” In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC '14, 587–593. Gyeongju, Republic of Korea: ACM. http://doi.acm.org/https://doi.org/10.1145/2554850.2554894.
  • Zeng, Z., X. Zheng, G. Chen, and Y. Yu. December 2014. “Spammer Detection on Weibo Social Network.” In Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 881–886. Singapore.
  • Zhang, X., and A. A. Ghorbani. 2020. “An Overview of Online Fake News: Characterization, Detection, and Discussion.” Information Processing & Management 57: Article ID: 102025.
  • Zhang, C. M., and V. Paxson. 2011. “Detecting and Analyzing Automated Activity on Twitter.” In Proceedings of the International Conference on Passive and Active Network Measurement, PAM' 11, 102–111. Atlanta, GA, USA: Springer Berlin Heidelberg.
  • Zheng, X., Z. Zeng, Z. Chen, Y. Yu, and C. Rong. 2015. “Detecting Spammers on Social Networks.” Neurocomputing 159: 27–34. http://www.sciencedirect.com/science/article/pii/S0925231215002106.
  • Zhou, X., and R. Zafarani. 2020. “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities.” ACM Computing Surveys 53: 1–40. https://doi.org/https://doi.org/10.1145/3395046.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.