Detecting spammers on social networks

Cited by (783)

Items 201–300 of 783

1. A Unsupervised Learning Method of Anomaly Detection Using GRU
   Zhaowei Qu, Lun Su, Xiaoru Wang, Shuqiang Zheng, Xiaomin Song, Xiaohui Song · 2018 · IEEE International Conference on Big Data and Smart Computing (BigComp)
2. Fake Account Identification in Social Networks
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3. Detecting Social Bots on Facebook in an Information Veracity Context
   Giovanni C. Santia, Munif Ishad Mujib, Jake Ryland Williams · 2019 · Proceedings of the International AAAI Conference on Web and Social Media
4. Spammer Classification Using Ensemble Methods over Structural Social Network Features
   Sajid Yousuf Bhat, Muhammad Abulaish, Abdulrahman Abdulkarim Mirza · 2014 · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
5. Toward Detecting Malicious Links in Online Social Networks through User Behavior
   Bandar M. Alghamdi, Jason Watson, Yue Xu · 2016 · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
6. A study on real-time low-quality content detection on Twitter from the users’ perspective
   Weiling Chen, Chai Kiat Yeo, Chiew Tong Lau, Bu‐Sung Lee · 2017 · PLoS ONE
7. A survey on detecting spam accounts on Twitter network
   Oğuzhan Çıtlak, Murat Dörterler, İbrahim Alper Doğru · 2019 · Social Network Analysis and Mining
8. Extracting medical entities from social media
   Sanja Šćepanović, Enrique Martín-López, Daniele Quercia, Khan Baykaner · 2020 · ACM Conference on Health, Inference, and Learning (CHIL)
9. TrollMagnifier: Detecting State-Sponsored Troll Accounts on Reddit
   Mohammad Hammas Saeed, Shiza Ali, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, Gianluca Stringhini · 2022 · IEEE Symposium on Security and Privacy (S&P)
10. Discover millions of fake followers in Weibo
    Yi Zhang, Jianguo Lü · 2016 · Social Network Analysis and Mining
11. Social Spammer and Spam Message Co-Detection in Microblogging with Social Context Regularization
    Fangzhao Wu, Jinyun Shu, Yongfeng Huang, Zhigang Yuan · 2015 · ACM International Conference on Information and Knowledge Management (CIKM)
12. Examination of data, rule generation and detection of phishing URLs using online logistic regression
    Mohammed Nazim Feroz, Susan A. Mengel · 2014 · IEEE International Conference on Big Data (BigData)
13. Artificial Neural Network for Websites Classification with Phishing Characteristics
    Ricardo Pinto Ferreira, Andréa Martiniano, Domingos Márcio Rodrigues Napolitano, Márcio Romero, Dacyr Dante de Oliveira Gatto, Edquel Bueno Prado Farias, Renato José Sassi · 2018 · Social Networking
14. Emotional Bots: Content-based Spammer Detection on Social Media
    Panagiotis Andriotis, Atsuhiro Takasu · 2018 · IEEE International Workshop on Information Forensics and Security
15. Multi-granularity Textual Adversarial Attack with Behavior Cloning
    Yangyi Chen, Jin Su, Wei Wei · 2021 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
16. Towards automatic real time identification of malicious posts on Facebook
    Prateek Dewan, Ponnurangam Kumaraguru · 2015 · Conference on Privacy, Security and Trust
17. Twitter games
    Vasumathi Sridharan, Vaibhav Shankar, Minaxi Gupta · 2012 · Annual Computer Security Applications Conference
18. Twitter
    Abdullah Almaatouq, Ahmad Alabdulkareem, Mariam Nouh, Erez Shmueli, Mansour Alsaleh, Vivek Kumar Singh, Abdulrahman Alarifi, Anas Alfaris, Alex Pentland · 2014 · ACM Web Science Conference (WebSci)
19. The Many Faces of Link Fraud
    Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos · 2017 · Proceedings
20. Analysis and detection of fake profile over social network
    Vijay Kumar Tiwari · 2017 · International Conference on Computing, Communication and Automation
21. Security techniques for intelligent spam sensing and anomaly detection in online social platforms
    Monther Aldwairi, Lo’ai Tawalbeh · 2019 · International Journal of Electrical and Computer Engineering (IJECE)
22. Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection
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23. Detecting spamming activities in twitter based on deep‐learning technique
    Tingmin Wu, Sheng Wen, Shigang Liu, Jun Zhang, Yang Xiang, Majed Alrubaian, Mohammad Mehedi Hassan · 2017 · Concurrency and Computation Practice and Experience
24. Examining the Demand for Spam
    Elissa M. Redmiles, Neha Chachra, Brian Waismeyer · 2018 · CHI Conference on Human Factors in Computing Systems
25. A Model for Identifying Misinformation in Online Social Networks
    Sotirios Antoniadis, Iouliana Litou, Vana Kalogeraki · 2015 · Lecture notes in computer science
26. LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy Protocols
    Kai Huang, Gaoya Ouyang, Qingqing Ye, Haibo Hu, Bolong Zheng, Xi Zhao, Ruiyuan Zhang, Xiaofang Zhou · 2024 · IEEE Transactions on Knowledge and Data Engineering
27. Cyber security risks for minors: A taxonomy and a software architecture
    Andreas Tsirtsis, Nicolas Tsapatsoulis, Makis Stamatelatos, Kostantinos Papadamou, Michael Sirivianos · 2016 · International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)
28. Social Networks Fake Profiles Detection Using Machine Learning Algorithms
    Yasyn Elyusufi, Zakaria Elyusufi, M. Aït Kbir · 2020 · Lecture notes in intelligent transportation and infrastructure
29. Bot-Detective
    Maria Kouvela, Ilias Dimitriadis, Athena I. Vakali · 2020 · International Conference on Management of Emergent Digital EcoSystems (MEDES)
30. Robust Spammer Detection in Microblogs
    Hao Fu, Xing Xie, Yong Rui, Neil Zhenqiang Gong, Guangzhong Sun, Enhong Chen · 2017 · ACM Transactions on Intelligent Systems and Technology
31. UbCadet: detection of compromised accounts in twitter based on user behavioural profiling
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32. Controlling astroturfing on the internet: a survey on detection techniques and research challenges
    Mahbub, Syed, Pardede, Eric, Kayes, A.S.M., Rahayu, Wenny · 2019 · International Journal of Web and Grid Services
33. On the hardness of evading combinations of linear classifiers
    David Stevens, Daniel Lowd · 2013 · ACM Workshop on Artificial Intelligence and Security (AISec)
34. Machine learning algorithm-based spam detection in social networks
    Muruganandam Sumathi, S. P. Raja · 2023 · Social Network Analysis and Mining
35. 10 Bits of Surprise
    Reza Zafarani, Huan Liu · 2015 · ACM International Conference on Information and Knowledge Management (CIKM)
36. Detecting Arabic spammers and content polluters on Twitter
    Nour El-Mawass, Saad Saleh Alaboodi · 2016 · International Conference on Digital Information Processing and Communications
37. Unsupervised collective-based framework for dynamic retraining of supervised real-time spam tweets detection model
    Mahdi Washha, Aziz M. Qaroush, Manel Mezghani, Florence Sèdes · 2019 · Expert Systems with Applications
38. Organic Products in Mexico and South Korea on Twitter
    Xanat Vargas Meza, Han Woo Park · 2015 · Journal of Business Ethics
39. Twitter opinion mining for adverse drug reactions
    Liang Wu, Teng-Sheng Moh, Natalia Khuri · 2015 · IEEE International Conference on Big Data (BigData)
40. Detecting Malicious Users in Twitter using Classifiers
    Monika Singh, Divya Bansal, Sanjeev Sofat · 2014 · International Conference on Security of Information and Networks (SIN)
41. SybilRadar: A Graph-Structure Based Framework for Sybil Detection in On-line Social Networks
    Dieudonné Mulamba, Indrajit Ray, Indrakshi Ray · 2016 · IFIP advances in information and communication technology
42. Cost-Based Heterogeneous Learning Framework for Real-Time Spam Detection in Social Networks With Expert Decisions
    Jaeun Choi, Chunmi Jeon · 2021 · IEEE Access
43. Thwarting Fake OSN Accounts by Predicting their Victims
    Yazan Boshmaf, Matei Ripeanu, Konstantin Beznosov, Elizeu Santos‐Neto · 2015 · ACM Workshop on Artificial Intelligence and Security (AISec)
44. Fluxing botnet command and control channels with URL shortening services
    Sangho Lee, Jong Kim · 2012 · Computer Communications
45. Finding Users we Trust: Scaling up Verified Twitter Users Using their Communication Patterns
    Martin Hentschel, Omar Alonso, Scott Counts, Vasileios Kandylas · 2014 · Proceedings of the International AAAI Conference on Web and Social Media
46. Measuring, Characterizing, and Detecting Facebook Like Farms
    Ikram, Muhammad, Onwuzurike, Lucky, Farooqi, Shehroze, Cristofaro, Emiliano De, Friedman, Arik, Jourjon, Guillaume, Kaafar, Mohammed Ali, Shafiq, M. Zubair · 2017 · ACM Transactions on Privacy and Security
47. "You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks
    Enrico Mariconti, Guillermo Suárez‐Tangil, Jeremy Blackburn, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis A, Jordi Luque Serrano, Gianluca Stringhini · 2018 · OpenBU (Boston University)
48. Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks
    Cheng Wang, Hangyu Zhu, Bo Yang · 2021 · IEEE Transactions on Computational Social Systems
49. Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets
    Mst. Nilufa Yeasmin, Nosin Ibna Mahbub, Mrinal Kanti Baowaly, Bikash Chandra Singh, Zulfikar Alom, Zeyar Aung, Mohammad Abdul Azim · 2022 · Big Data and Cognitive Computing
50. Efficient e-mail spam filtering approach combining Logistic Regression model and Orthogonal Atomic Orbital Search algorithm
    Ghaith Manita, Amit Chhabra, Ouajdi Korbaa · 2023 · Applied Soft Computing
51. Spam ain't as diverse as it seems
    Hongyu Gao, Yi Yang, Kai Bu, Yan Chen, Doug Downey, Kathy Lee, Alok Choudhary · 2014 · Annual Computer Security Applications Conference
52. Facebook Inspector (FbI): Towards automatic real-time detection of malicious content on Facebook
    Prateek Dewan, Ponnurangam Kumaraguru · 2017 · Social Network Analysis and Mining
53. VASSL: A Visual Analytics Toolkit for Social Spambot Labeling
    Mosab Khayat, Morteza Karimzadeh, J. L. Zhao, David S. Ebert · 2019 · IEEE Transactions on Visualization and Computer Graphics
54. Mining Influencers Using Information Flows in Social Streams
    Karthik Subbian, Charų C. Aggarwal, Jaideep Srivastava · 2016 · ACM Transactions on Knowledge Discovery from Data
55. Detecting Spam Tweets using Character N-gram Features
    Mokhtar Ashour, Cherif R. Salama, Mohamed Watheq El-Kharashi · 2018 · International Conference on Computer Engineering & Systems (ICCES)
56. A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data
    Deniz Kılınç · 2019 · Software Practice and Experience
57. Co-Detection of crowdturfing microblogs and spammers in online social networks
    Bo Liu, Xiangguo Sun, Zeyang Ni, Junzhou Luo, Junzhou Luo, Benyuan Liu, Xinwen Fu · 2019 · World Wide Web
58. Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries
    Syed Mahbub, Eric Pardede, A. S. M. Kayes · 2022 · IEEE Access
59. SybilBlind: Detecting Fake Users in Online Social Networks Without Manual Labels
    Binghui Wang, Le Zhang, Neil Zhenqiang Gong · 2018 · Lecture notes in computer science
60. Understanding eWhoring
    Alice Hutchings, Sergio Pastrana · 2019 · IEEE European Symposium on Security and Privacy
61. A taxonomy-based model of security and privacy in online social networks
    Luca Caviglione, Mauro Coccoli, Alessio Merlo · 2014 · International Journal of Computational Science and Engineering
62. Real-time Classification of Malicious URLs on Twitter using Machine Activity Data
    Pete Burnap, Amir Javed, OMER F. RANA, Malik Shahzad Kaleem Awan · 2015 · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
63. Prediction of drive-by download attacks on Twitter
    Amir Javed, Pete Burnap, OMER F. RANA · 2018 · Information Processing & Management
64. Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter
    Eiman Alothali, Kadhim Hayawi, Hany Alashwal · 2021 · Social Network Analysis and Mining
65. Automatic Detection of Cyber Security Related Accounts on Online Social Networks
    Cagri Burak Aslan, Rahime Belen Sağlam, Shujun Li · 2018 · International Conference on Social Media and Society (SMSociety)
66. Full Cycle Analysis of a Large-Scale Botnet Attack on Twitter
    Christoph Besel, Juan Echeverría, Shi Zhou · 2018 · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
67. Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying
    Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Athena I. Vakali · 2017 · arXiv (Cornell University)
68. Robustness and stability of enterprise intranet social networks: The impact of moderators
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69. Deep Entity Classification: Abusive Account Detection for Online Social Networks
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70. A comprehensive overview of fake news detection on social networks
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71. Exploiting latent content based features for the detection of static SMS spams
    Amir Karami, Lina Zhou · 2014 · Proceedings of the American Society for Information Science and Technology
72. Organic or Organized?
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73. Analysis and detection of low quality information in social networks
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74. What Message Characteristics Make Social Engineering Successful on Facebook: The Role of Central Route, Peripheral Route, and Perceived Risk
    Abdullah Algarni · 2019 · Information
75. Social networks fake profiles detection based on account setting and activity
    Yasyn Elyusufi, Zakaria Elyusufi, M. Aït Kbir · 2019 · International Conference on Smart City Applications (SCA)
76. Leveraging Careful Microblog Users for Spammer Detection
    Hao Fu, Xing Xie, Yong Rui · 2015 · International World Wide Web Conference (WWW)
77. Intelligent Twitter Spam Detection: A Hybrid Approach
    Varad Vishwarupe, Mangesh V. Bedekar, Milind Pande, Anil S Hiwale · 2017 · Lecture notes in networks and systems
78. Twitter Bot Detection with Reduced Feature Set
    Jefferson Viana Fonseca Abreu, Célia Ghedini Ralha, João José Costa Gondim · 2020 · IEEE International Conference on Intelligence and Security Informatics (ISI)
79. Learning Fast and Slow: Propedeutica for Real-Time Malware Detection
    Ruimin Sun, Xiaoyong Yuan, Pan Li He, Qile Zhu, Aokun Chen, Andre Ricardo Abed Gregio, Daniela Seabra Oliveira, Xiaolin Li · 2021 · IEEE Transactions on Neural Networks and Learning Systems
80. A postmortem of suspended Twitter accounts in the 2016 U.S. presidential election
    Huyen Le, George Robert Boynton, Zubair Shafiq, Padmini Srinivasan · 2019 · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
81. An Unsupervised Approach for Identifying Spammers in Social Networks
    Mohamed Bouguessa · 2011 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
82. Behavioral detection of spam URL sharing: Posting patterns versus click patterns
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83. Malicious Account Detection on Twitter Based on Tweet Account Features using Machine Learning
    Farhan Nurdiatama Pakaya, Muhammad Okky Ibrohim, Indra Darmawan Budi · 2019 · International Conference on Informatics and Computing (ICIC)
84. Friendship Preference: Scalable and Robust Category of Features for Social Bot Detection
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85. Beyond personalization and anonymity
    Shang Shang, Yuk Hui, Pan Hui, Paul Cuff, Sanjeev R. Kulkarni · 2014 · ACM Symposium on Applied Computing
86. Research on Botnet Mechanisms and Defenses
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87. A Topic-Based Hidden Markov Model for Real-Time Spam Tweets Filtering
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88. A Criticism to Society (As Seen by Twitter Analytics)
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89. Managing and Deploying Distributed and Deep Neural Models Through Kafka-ML in the Cloud-to-Things Continuum
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90. SpADe: Multi-Stage Spam Account Detection for Online Social Networks
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91. A taste of tweets
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92. Crowdsourcing software evaluation
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93. BOT SPAMMER DETECTION IN TWITTER USING TWEET SIMILARITY AND TIME INTERVAL ENTROPY
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94. Leveraging time for spammers detection on Twitter
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95. Using Contextual Features for Online Recruitment Fraud Detection.
    Syed Mahbub, Eric Pardede · 2018 · Journal of the Association for Information Systems
96. Leveraging multi-level dependency of relational sequences for social spammer detection
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97. Detection of Fake Reviews Using Group Model
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98. Understanding compromised accounts on Twitter
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99. Measuring and mitigating oauth access token abuse by collusion networks
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100. Detecting Malicious Facebook Applications
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