Graph-based

• {Viswanath10} Bimal Viswanath, Ansley Post, Krishna P. Gummadi, Alan Mislove. An Analysis of Social Network-Based Sybil Defenses. In Proc. of ACM SIGCOMM, 2010. community detection
• {Sirivianos11} Michael Sirivianos, Kyungbaek Kim, and Xiaowei Yang. Introducing Social Trust to Collaborative Spam Mitigation. In Proc. of IEEE INFOCOM, 2011.
• {Cao12} Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. Aiding the Detection of Fake Accounts in Large Scale Social Online Services. Proc. of USENIX/ACM NSDI, 2012. Social graph-based sybil detection, deployment in Tuenti
• {Boshmaf15} Yazan Boshmaf, Dionysios Logothetis, Georgos Siganos, Jorge Lería, José Lorenzo, Matei Ripeanu, Konstantin Beznosov. Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs. Proc. of NDSS, 2015. integrating user-level activities into graph-level structures
• {Yang18} Zhi Yang, Yusi Zhang and Yafei Dai. Defending against Social Network Sybils with Interaction Graph Embedding. Proc. of IEEE Conference on Communications and Network Security (CNS), 2018. model the friend requests of users as a signed interaction graph, and perform Sybil detection by decoupling the graph into independent vectors in a low-dimensional space
• {Wang19} Binghui Wang, Jinyuan Jia, Le Zhang, Neil Zhenqiang Gong. Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation. IEEE Transactions on Network Science and Engineering, 2019, 6(3):523-537.
• {Li19} Ang Li, Tao Li, Yan Zhang, Lili Zhang, and Yanchao Zhang. SocialDistance: how far are you from verified users in online social media? Proc. of IWQoS, 2019. online interactions initiated from verified users towards unverified users can translate into some sort of trustworthiness
• {Li22} Siyu Li, Jin Yang, Gang Liang, Tianrui Li, Kui Zhao. SybilFlyover: Heterogeneous graph-based fake account detection model on social networks. Knowledge-Based Systems, 2022. content-based social network information is injected into the model using a method based on prompt learning to achieve more accurate modeling of the real state of an OSN
• {Amira24} Abdelouahab Amira, Abdelouahid Derhab, Samir Hadjar, Mustapha Merazka, Md. Golam Rabiul Alam, and Mohammad Mehedi Hassan. Detection and Analysis of Fake News Users’ Communities in Social Media. IEEE Transactions on Computational Social Systems, 2024, 11(4):5050-5059. propose a spatial–temporal similarity graph, a novel graph structure that connects social accounts that participate in the early stage of similar fake news campaigns

Supervised ML

• {Yang11} Zhi Yang, Christo Wilson, et al. Uncovering Social Network Sybils in the Wild. Proc. of ACM IMC, 2011. SVM, ground truth from renren, Sybils are not connected
• {Chu12} Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? IEEE Transactions on Dependable and Secure Computing, 9(6):811–824. difference among human, bot and cyborg / entropy - tweeting interval - automation
• {Wang13} G. Wang, T. Konolige, et al. You are How You Click: Clickstream Analysis for Sybil Detection, Usenix Security 2013. Clickstream-based sybil detection
• {Al-Qurishi18} M. Al-Qurishi, M. S. Hossain, M. Alrubaian, S. M. M. Rahman and A. Alamri. Leveraging Analysis of User Behavior to Identify Malicious Activities in Large-Scale Social Networks. IEEE Transactions on Industrial Informatics, 2018, 14(2):799-813.
• {Yuan19} Dong Yuan, Yuanli Miao, Neil Zhenqiang Gong, Zheng Yang, Qi Li, Dawn Song, Qian Wang, and Xiao Liang. Detecting Fake Accounts in Online Social Networks at the Time of Registrations. Proc. of ACM CCS, 2019. Ianus can detect around 400K per million new registered accountseach day and achieve a precision of over 96% on average PDF
• {Shen23} Xingfa Shen, Wentao Lv, Jianhui Qiu, Achhardeep Kaur, Fengjun Xiao, Feng Xia. Trust-Aware Detection of Malicious Users in Dating Social Networks. IEEE Transactions on Computational Social Systems, 2023, vol. 10, no. 5, pp. 2587-2598. develop a user trust model to distinguish between malicious and legitimate users

Semi-supervised

• {Gong14} Neil Zhenqiang Gong, Mario Frank, Prateek Mittal. SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection. IEEE Transactions on Information Forensics and Security, 2014, 9(6):976-987. recast the problem of finding Sybil users as a semi-supervised learning problem, where the goal is to propagate reputations from a small set of known benign and/or Sybil users to other users along the social connections between them
• {Li18} Chaozhuo Li, Senzhang Wang, Lifang He, Philip S. Yu, Yanbo Liang, Zhoujun Li. SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-view Data Fusion. Proc. of IEEE ICDM, 2018. a semi-supervised deep learning model for social spammer detection
• {Sedhai18} Surendra Sedhai and Aixin Sun. Semi-Supervised Spam Detection in Twitter Stream. IEEE Transactions on Computational Social Systems, 2018, 5(1):169-175. adaptively learns patterns of new spam activities and maintain good accuracy for spam detection in a tweet stream

Deep Learning

• {Li19} Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li. Spam Review Detection with Graph Convolutional Networks. Proc. of ACM CIKM, 2019. GCN-based heterogeneous graph spam detection algorithm which works on a bipartite graph with edge attributes at Xianyu
• {Xia19} Zenghua Xia, Chang Liu, Neil Zhenqiang Gong, Qi Li, Yong Cui, Dawn Song. Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study. Proc. of KDD, 2019. We observe that both methods produce similar accuracy, but CNN delivers better runtime performance PDF
• {Xu21} Teng Xu, Gerard Goossen, Huseyin Kerem Cevahir, Sara Khodeir, Yingyezhe Jin, Frank Li, Shawn Shan, Sagar Patel, David Freeman, and Paul Pearce. Deep Entity Classification: Abusive Account Detection for Online Social Networks. Proc. of USENIX Security Symposium, 2021. PDF
• {Ghanem23} Razan Ghanem, Hasan Erbay, Khaled Bakour. Contents‑Based Spam Detection on Social Networks Using RoBERTa Embedding and Stacked BLSTM. SN Computer Science, 2023, 4:380. a RoBERTa-based bi-directional Recurrent Neural Network model for spam detection on social networks
• {Gong23} Qingyuan Gong, Yushan Liu, Jiayun Zhang, Yang Chen, Qi Li, Yu Xiao, Xin Wang, Pan Hui. Detecting Malicious Accounts in Online Developer Communities Using Deep Learning. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10):10633-10649.
• {Sánchez-Corcuera24} Rubén Sánchez-Corcuera, Arkaitz Zubiaga, Aitor Almeida. Early Detection and Prevention of Malicious User Behavior on Twitter Using Deep Learning Techniques. IEEE Transactions on Computational Social Systems, 2024, 11(5):6649-6661. advocate for a forward-thinking methodology, which utilizes user data to foresee and mitigate potential threats before their realization, thereby cultivating more secure, equitable, and unbiased online communities

Deep Learning (App/relevant)

• {Liu18} Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. Heterogeneous Graph Neural Networks for Malicious Account Detection. Proc. of ACM CIKM, 2018. malicious account detection in Alipay; account-device graphs
• {Babaev19} Dmitrii Babaev, Maxim Savchenko, Alexander Tuzhilin, and Dmitrii Umerenkov. E.T.-RNN: Applying Deep Learning to Credit Loan Applications. Proc. of ACM KDD, 2019. used RNNs on fine grained transnational data to compute credit scores for the loan applicants
• {Zhan18} Qing Zhan and Hang Yin. A loan application fraud detection method based on knowledge graph and neural network. Proc. of the 2nd International Conference on Innovation in Artificial Intelligence (ICIAI), 2018.
• {Cheng19} Dawei Cheng, Yiyi Zhang, Fangzhou Yang, Yi Tu, Zhibin Niu, and Liqing Zhang. A Dynamic Default Prediction Framework for Networked-guarantee Loans. Proc. of ACM CIKM 2019. propose a dynamic default prediction framework (DDPF), which preserves temporal network structures and loan behavior sequences in an end-to-end model. In particular, we design a gated recursive and attention mechanism to integrate both the loan behavior and network information.

Others

• {Gao10} Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen and Ben Y. Zhao. Detecting and Characterizing Social Spam Campaigns. In Proc. of ACM IMC, 2010.
• {Makridakis10} Andreas Makridakis, Elias Athanasopoulos, Spiros Antonatos, Demetres Antoniades, Sotiris Ioannidis, and Evangelos P. Markatos. Understanding the behavior of malicious applications in social networks. IEEE Network, 2010, 24(5):14-19.
• {Ghosh12} Saptarshi Ghosh, Bimal Viswanath, et al. Understanding and combating link farming in the twitter social network. Proc. of WWW, 2012. link farming / social capitalists / top link farmers are not spammers but active contributors
• {Lumezanu12} Cristian Lumezanu and Nick Feamster. Observing Common Spam in Tweets and Email. Proc. of ACM IMC, 2012. spam in both email & twitter
• {Gao12} Hongyu Gao, Yan Chen, Kathy Lee, Diana Palsetia and Alok Choudhary, Towards Online Spam Filtering in Social Networks, in the Proc. of 19th Network & Distributed System Security Symposium (NDSS), 2012. message clusters
• {Mondal12} M. Mondal, B. Viswanath, et al. Defending Against Large-scale Crawls in Online Social Networks. Proc. of ACM CoNEXT, 2012. social link-based crawling defense
• {Viswanath14} Bimal Viswanath, M. Ahmad Bashir, et al. Towards Detecting Anomalous User Behavior in Online Social Networks. USENIX Security, 2014. unsupervised anomaly detection techniques over user behavior; PCA; when testing for anomalies, any data point whose L^{2} norm in the residual subspace exceeds the bound is flagged as anomalous.
• {Yang14} Chao Yang, Jialong Zhang, Guofei Gu. A Taste of Tweets: Reverse Engineering Twitter Spammers. Proc. of ACSAC, 2014. how Twitter spammers find their targets
• {Goga15} Oana Goga, Giridhari Venkatadri, and Krishna P Gummadi. The Doppelgänger Bot Attack: Exploring Identity Impersonation in Online Social Networks. Proc. of ACM IMC, 2015. identity impersonation attack: spoof or assume the identity of another real-world user
• {Viswanath15} Bimal Viswanath, Muhammad Ahmad Bashir, Muhammad Bilal Zafar, Simon Bouget, Saikat Guha, Krishna P. Gummadi, Aniket Kate and Alan Mislove. Strength in Numbers: Robust Tamper Detection in Crowd Computations. Proc. of ACM COSN, 2015.
• {KC16} Santosh KC, Arjun Mukherjee. On the Temporal Dynamics of Opinion Spamming: Case Studies on Yelp. Proc. of WWW, 2016. discover various temporal patterns and their relationships with the rate at which fake reviews are posted; explore the effect of filtered reviews on (long-term and imminent) future rating and popularity prediction of entities
• {Yao17} Yuanshun Yao, Bimal Viswanath, Jenna Cryan, Haitao Zheng and Ben Y. Zhao. Automated Crowdturfing Attacks and Defenses in Online Review Systems. Proc. of ACM CCS, 2017.
• {Kumar17} S. Kumar, J. Cheng, J. Leskovec, V.S. Subrahmanian. An Army of Me: Sockpuppets in Online Discussion Communities. Proc. of WWW, 2017. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users
• {Zheng18} Haizhong Zheng, Minhui Xue, Hao Lu, Shuang Hao, Haojin Zhu, Xiaohui Liang, Keith Ross. Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks. Proc. of NDSS, 2018.
• {Xu18} Fengli Xu, Guozhen Zhang, Zhilong Chen, Jiaxin Huang, Yong Li, Diyi Yang, Ben Y. Zhao, and Fanchao Meng. Understanding Motivations behind Inaccurate Check-ins. Proc. ACM Hum.-Comput. Interact., 2018, 2(CSCW):Article 188 (November 2018), 22 pages. conduct a data-driven analysis using an empirical check-in data trace of more than 276,000 users from WeChat Moments, with matching traces of their physical mobility; there are surprisingly high amount of inaccurate check-ins even in the absence of rewards: 43% of total check-ins are inaccurate and 61% of survey participants report they have misrepresented their check-ins
• {Liang21} Xiao Liang, Zheng Yang, Binghui Wang, Shaofeng Hu, Zijie Yang, Dong Yuan, Neil Zhenqiang Gong, Qi Li, and Fang He. Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach. Proc. of ACM KDD, 2021.
• {Kolomeets24} Maxim Kolomeets, Han Wu, Lei Shi, Aad van Moorsel. The Face of Deception: The Impact of AI-Generated Photos on Malicious Social Bots. To appear: IEEE Transactions on Computational Social Systems.