Most cited papers in Authorship Attribution and Profiling
What are the most cited papers in Authorship Attribution and Profiling?
The most cited paper in Authorship Attribution and Profiling is "Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination" (Marianne Bertrand, Sendhil Mullainathan, 2004), with 4,548 citations. Next come "The mathematics of statistical machine translation: parameter estimation" (4,107) and "Word association norms, mutual information, and lexicography" (3,666).
Showing 50 of 100 papers. Every paper with more than 313 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination Marianne Bertrand, Sendhil Mullainathan · American Economic Review | Sep 2004 | 4,548 |
| 2 | The mathematics of statistical machine translation: parameter estimation Peter F. Brown, Vincent J. Della Pietra, Stephen A. Della Pietra et al. | Jun 1993 | 4,107 |
| 3 | Word association norms, mutual information, and lexicography Kenneth Church, Patrick Hanks | Mar 1990 | 3,666 |
| 4 | Quantitative Analysis of Culture Using Millions of Digitized Books Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden et al. · Science | 16 Dec 2010 | 3,145 |
| 5 | Semantics derived automatically from language corpora contain human-like biases Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan · Science | 13 Apr 2017 | 2,956 |
| 6 | Class-based n -gram models of natural language Peter F. Brown, P.V. deSouza, Robert L. Mercer et al. | Dec 1992 | 2,883 |
| 7 | A survey of named entity recognition and classification David R. Nadeau, Satoshi Sekine · Lingvisticae Investigationes | 10 Aug 2007 | 2,517 |
| 8 | Linguistic Inquiry and Word Count (LIWC) Cindy K. Chung, James W. Pennebaker · IGI Global eBooks | 2012 | 2,214 |
| 9 | Norms of valence, arousal, and dominance for 13,915 English lemmas Amy Beth Warriner, Victor Kuperman, Marc Brysbaert · Behavior Research Methods | 12 Feb 2013 | 2,024 |
| 10 | Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach H. Andrew Schwartz, johannes Christopher Eichstaedt, Margaret L. Kern et al. · PLoS ONE | 25 Sep 2013 | 1,760 |
| 11 | Lying Words: Predicting Deception from Linguistic Styles Matthew L. Newman, James W. Pennebaker, Diane S. Berry et al. · Personality and Social Psychology Bulletin | May 2003 | 1,470 |
| 12 | Syntactic clustering of the Web Andrei Broder, S. Glassman, Mark S. Manasse et al. · Computer Networks and ISDN Systems | Sep 1997 | 1,360 |
| 13 | Subtlex-UK: A New and Improved Word Frequency Database for British English Walter J. B. van Heuven, Paweł Mandera, Emmanuel Keuleers et al. · Quarterly Journal of Experimental Psychology | 4 Oct 2013 | 1,338 |
| 14 | The author-topic model for authors and documents Michal Rosen‐Zvi, Thomas Morgan Griffiths, Mark Steyvers et al. · Uncertainty in Artificial Intelligence | 7 Jul 2004 | 1,310 |
| 15 | The Mathematics of Sentence Structure Joachim Lambek · American Mathematical Monthly | Mar 1958 | 1,296 |
| 16 | The WaCky wide web: a collection of very large linguistically processed web-crawled corpora Marco Baroni, Silvia Bernardini, Adriano Ferraresi et al. · Language Resources and Evaluation | 9 Feb 2009 | 1,220 |
| 17 | A survey of modern authorship attribution methods Efstathios Stamatatos · Journal of the American Society for Information Science and Technology | 16 Dec 2008 | 1,168 |
| 18 | reCAPTCHA: Human-Based Character Recognition via Web Security Measures Luis von Ahn, Benjamin Maurer, Colin P. McMillen et al. · Science | 15 Aug 2008 | 1,134 |
| 19 | Dependency-Based Word Embeddings Omer Levy, Yoav Goldberg | 2014 | 1,071 |
| 20 | Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text François Mairesse, Marilyn Walker, Matthias R. Mehl et al. · Journal of Artificial Intelligence Research | 28 Nov 2007 | 1,035 |
| 21 | A program for aligning sentences in bilingual corpora William A. Gale, Kenneth Church | Mar 1993 | 955 |
| 22 | Zipf’s word frequency law in natural language: A critical review and future directions Steven T. Piantadosi · Psychonomic Bulletin & Review | 24 Mar 2014 | 908 |
| 23 | Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations Jennifer Hill, W. Randolph Ford, Ingrid G. Farreras · Computers in Human Behavior | 20 Mar 2015 | 854 |
| 24 | Response Latencies in Naming Objects Richard Charles Oldfield, Arthur Wingfield · Quarterly Journal of Experimental Psychology | Dec 1965 | 819 |
| 25 | SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles Qing Cai, Marc Brysbaert · PLoS ONE | 2 Jun 2010 | 800 |
| 26 | Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints Jieyu Zhao, Tianlu Wang, Mark Yatskar et al. | 2017 | 798 |
| 27 | SemEval-2018 Task 1: Affect in Tweets Saif M. Mohammad, Felipe Bravo-Márquez, Mohammad Yahya Bani Salameh et al. | 2018 | 765 |
| 28 | From key words to key semantic domains Paul Rayson · International Journal of Corpus Linguistics | 8 Dec 2008 | 752 |
| 29 | Three generative, lexicalised models for statistical parsing Michael Collins | 1997 | 750 |
| 30 | Wizard of Oz studies Nils Dahlbäck, Arne Jönsson, Lars Ahrenberg | 1993 | 742 |
| 31 | Word lengths are optimized for efficient communication Steven T. Piantadosi, Harry J. Tily, Edward Leigh Gibson · Proceedings of the National Academy of Sciences | 28 Jan 2011 | 720 |
| 32 | A Stylometric Inquiry into Hyperpartisan and Fake News Martin Potthast, Johannes Kiesel, Kevin Reinartz et al. | 2018 | 704 |
| 33 | Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods Jieyu Zhao, Tianlu Wang, Mark Yatskar et al. | 2018 | 692 |
| 34 | Deep Learning-Based Document Modeling for Personality Detection from Text Navonil Majumder, Soujanya Poria, Alexander F. Gelbukh et al. · IEEE Intelligent Systems | Mar 2017 | 686 |
| 35 | Finding Deceptive Opinion Spam by Any Stretch of the Imagination Myle Ott, Yejin Choi, Claire Cardie et al. · arXiv (Cornell University) | 22 Jul 2011 | 686 |
| 36 | Classifying latent user attributes in twitter Delip Rao, David Yarowsky, Abhishek Shreevats et al. | 30 Oct 2010 | 653 |
| 37 | Specification curve analysis Uri Simonsohn, Joseph P. Simmons, Leif D. Nelson · Nature Human Behaviour | 27 Jul 2020 | 640 |
| 38 | Three new probabilistic models for dependency parsing Jason M. Eisner | 1996 | 634 |
| 39 | Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers Tal Yarkoni · Journal of Research in Personality | 12 Apr 2010 | 631 |
| 40 | Resolving pronoun references Jerry R. Hobbs · Lingua | Apr 1978 | 625 |
| 41 | SemEval-2007 task 14 Carlo Strapparava, Rada F. Mihalcea | 2007 | 607 |
| 42 | The communicative function of ambiguity in language Steven T. Piantadosi, Harry J. Tily, Edward A. F. Gibson · Cognition | 20 Dec 2011 | 602 |
| 43 | What’s in a Name? Joseph Bonneau, Mike Just, Greg Dean Matthews · Lecture notes in computer science | 2010 | 583 |
| 44 | Discriminating Gender on Twitter John D. Burger, John C. Henderson, George Kim et al. | 27 Jul 2011 | 566 |
| 45 | A Machine Learning Approach to Twitter User Classification Marco Pennacchiotti, Ana-Maria Popescu · Proceedings of the International AAAI Conference on Web and Social Media | 3 Aug 2021 | 558 |
| 46 | Effects of Age and Gender on Blogging Jonathan Schler, Moshe Koppel, Shlomo Argamon et al. · National Conference on Artificial Intelligence | 2005 | 554 |
| 47 | Crime data mining: a general framework and some examples Hao Chen, Wingyan Chung, Jennifer Xu et al. · Computer | Apr 2004 | 546 |
| 48 | Noun classification from predicate-argument structures Donald Hindle | 1990 | 545 |
| 49 | The Web as a Parallel Corpus Philip Resnik, Noah A. Smith · Computational Linguistics | Sep 2003 | 528 |
| 50 | A framework for authorship identification of online messages: Writing‐style features and classification techniques Rong Jian Zheng, Jiexun Li, Hsinchun Chen et al. · Journal of the American Society for Information Science and Technology | 21 Dec 2005 | 519 |
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.