Topic · Spam and Phishing Detection
Most cited papers in Spam and Phishing Detection
What are the most cited papers in Spam and Phishing Detection?
The most cited paper in Spam and Phishing Detection is "The spread of true and false news online" (Soroush Vosoughi, Deb Kumar Roy, Sinan Aral, 2018), with 8,825 citations. Next come "Support vector machines" (7,152) and "A systematic analysis of performance measures for classification tasks" (7,026).
Showing 50 of 100 papers. Every paper with more than 732 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | The spread of true and false news online Soroush Vosoughi, Deb Kumar Roy, Sinan Aral · Science | 8 Mar 2018 | 8,825 |
| 2 | Support vector machines Marti A. Hearst, Susan Dumais, E. Osuna et al. · IEEE Intelligent Systems and their Applications | Jul 1998 | 7,152 |
| 3 | A systematic analysis of performance measures for classification tasks Marina V. Sokolova, Guy Lapalme · Information Processing & Management | 9 May 2009 | 7,026 |
| 4 | VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text Cecelia Hutto, Éric Gilbert · Proceedings of the International AAAI Conference on Web and Social Media | 16 May 2014 | 6,006 |
| 5 | A survey on concept drift adaptation João Manuel Portela da Gama, Indrė Žliobaitė, Albert Bifet et al. · ACM Computing Surveys | Mar 2014 | 3,634 |
| 6 | ML-KNN: A lazy learning approach to multi-label learning Min-Ling Zhang, Zhi‐Hua Zhou · Pattern Recognition | 14 Jan 2007 | 3,607 |
| 7 | A Review on Multi-Label Learning Algorithms Min-Ling Zhang, Zhi‐Hua Zhou · IEEE Transactions on Knowledge and Data Engineering | 31 May 2013 | 3,407 |
| 8 | Fake News Detection on Social Media Kai Shu, Amy L. Sliva, Suhang Wang et al. · ACM SIGKDD Explorations Newsletter | Sep 2017 | 3,290 |
| 9 | Measurement and analysis of online social networks Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi et al. | 24 Oct 2007 | 3,100 |
| 10 | Social information filtering Upendra Shardanand, Pattie Maes | 1995 | 2,821 |
| 11 | CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON Saif M. Mohammad, Peter D. Turney · Computational Intelligence | 4 Sep 2012 | 2,682 |
| 12 | A re-examination of text categorization methods Yiming Yang, Xin Liu | Aug 1999 | 2,667 |
| 13 | Automated Hate Speech Detection and the Problem of Offensive Language Thomas Davidson, Dana Warmsley, Michael Walton Macy et al. · Proceedings of the International AAAI Conference on Web and Social Media | 3 May 2017 | 2,554 |
| 14 | Information credibility on twitter Carlos Cortes Castillo, Marcelo Mendoza, Bárbara Poblete | 28 Mar 2011 | 2,514 |
| 15 | Classifier chains for multi-label classification Jesse Read, Bernhard Pfahringer, Geoffrey Holmes et al. · Machine Learning | 29 Jun 2011 | 2,324 |
| 16 | Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook Alessandro Acquisti, Ralph Gross · Lecture notes in computer science | 2006 | 1,896 |
| 17 | Rank aggregation methods for the Web Cynthia Dwork, Ravi Kumar, Moni Naor et al. | Apr 2001 | 1,833 |
| 18 | Crowds Michael K. Reiter, Aviel D. Rubin · ACM Transactions on Information and System Security | Nov 1998 | 1,789 |
| 19 | Scalable influence maximization for prevalent viral marketing in large-scale social networks Wei Chen, Chi Wang, Yajun Wang | 25 Jul 2010 | 1,774 |
| 20 | The rise of social bots Emilio Ferrara, Onur Varol, Clayton Davis et al. · Communications of the ACM | 24 Jun 2016 | 1,620 |
| 21 | I tube, you tube, everybody tubes Meeyoung Cha, Haewoon Kwak, Pablo Rodríguez et al. | 24 Oct 2007 | 1,603 |
| 22 | Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach Xavier Glorot, Antoine Bordes, Yoshua Bengio | 2011 | 1,560 |
| 23 | A survey on sentiment analysis methods, applications, and challenges Mayur Wankhade, Annavarapu Chandra Sekhara Rao, Chaitanya Kulkarni · Artificial Intelligence Review | 7 Feb 2022 | 1,509 |
| 24 | Support vector machines for spam categorization Harris Drucker, Donghui Wu, Vladimir N. Vapnik · IEEE Transactions on Neural Networks | 1999 | 1,479 |
| 25 | CAPTCHA: Using Hard AI Problems for Security Luis von Ahn, Manuel Blum, Nicholas Hopper et al. · Lecture notes in computer science | 2003 | 1,465 |
| 26 | A Survey on Hate Speech Detection using Natural Language Processing Anna Grau Schmidt, Michael Wiegand | 2017 | 1,426 |
| 27 | Mining Multi-label Data Grigorios Tsoumakas, Ioannis Katakis, Ioannis P. Vlahavas | 2009 | 1,415 |
| 28 | Toward developing a systematic approach to generate benchmark datasets for intrusion detection Ali Shiravi, Hadi Shiravi, Mahbod Tavallaee et al. · Computers & Security | 5 Jan 2012 | 1,354 |
| 29 | A Survey of Opinion Mining and Sentiment Analysis Bing Liu, Lei Zhang | 2012 | 1,337 |
| 30 | Why phishing works Rachna Dhamija, J. D. Tygar, Marti A. Hearst | 22 Apr 2006 | 1,320 |
| 31 | Twitter Sentiment Analysis: The Good the Bad and the OMG! Efthymios Kouloumpis, Theresa Wilson, Johanna D. Moore · Proceedings of the International AAAI Conference on Web and Social Media | 3 Aug 2021 | 1,245 |
| 32 | A Survey on Automatic Detection of Hate Speech in Text Paula Fortuna, Sérgio Nunes · ACM Computing Surveys | 31 Jul 2018 | 1,229 |
| 33 | Affective Computing and Sentiment Analysis Erik Cambria · IEEE Intelligent Systems | Mar 2016 | 1,208 |
| 34 | Labeled LDA Daniel Ramage, David Hall, Ramesh M. Nallapati et al. | 2009 | 1,208 |
| 35 | User Awareness of Security Countermeasures and Its Impact on Information Systems Misuse: A Deterrence Approach John D’Arcy, Anat Hovav, Dennis F. Galletta · Information Systems Research | 20 Jun 2008 | 1,178 |
| 36 | Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification Duyu Tang, Furu Wei, Nan Yang et al. | 2014 | 1,176 |
| 37 | EANN Yaqing Wang, Fenglong Ma, Zhiwei Jin et al. | 19 Jul 2018 | 1,175 |
| 38 | The Enron Corpus: A New Dataset for Email Classification Research Bryan Klimt, Yiming Yang · Lecture notes in computer science | 2004 | 1,166 |
| 39 | A Survey of Fake News Xinyi Zhou, Reza Zafarani · ACM Computing Surveys | 7 Jul 2020 | 1,163 |
| 40 | A data mining framework for building intrusion detection models Wenke Lee, Salvatore J. Stolfo, Kui W. Mok | 20 Jan 2003 | 1,157 |
| 41 | FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media Kai Shu, Deepak Mahudeswaran, Suhang Wang et al. · Big Data | Jun 2020 | 1,156 |
| 42 | Abusive Language Detection in Online User Content Chikashi Nobata, Joel Tetreault, Achint Oommen Thomas et al. | 11 Apr 2016 | 1,156 |
| 43 | A Bayesian Approach to Filtering Junk E-Mail Mehran Sahami, Susan Dumais, David E. Heckerman et al. | Jul 1998 | 1,152 |
| 44 | Applying Support Vector Machines to Imbalanced Datasets Rehan Akbani, Stephen S. Kwek, Nathalie Japkowicz · Lecture notes in computer science | 2004 | 1,150 |
| 45 | Shifting attention to accuracy can reduce misinformation online Gordon Pennycook, Ziv Epstein, Mohsen Mosleh et al. · Nature | 17 Mar 2021 | 1,144 |
| 46 | Social phishing Tom N. Jagatic, Nathaniel A. Johnson, Markus Jakobsson et al. · Communications of the ACM | 26 Sep 2007 | 1,126 |
| 47 | Winnowing Saul Schleimer, Daniel Shawcross Wilkerson, Alex Aiken | 9 Jun 2003 | 1,119 |
| 48 | Data mining methods for detection of new malicious executables Matthew G. Schultz, Eleazar Eskin, F. Zadok et al. | 13 Nov 2002 | 1,081 |
| 49 | The spread of low-credibility content by social bots Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol et al. · Nature Communications | 9 Nov 2018 | 1,067 |
| 50 | Combating Web Spam with TrustRank Zoltán Gyöngyi, Héctor García-Molina, Jan Ole Pedersen · Elsevier eBooks | 2004 | 1,030 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.