• Task: Given a document, your task is to determine its author's age and gender.
  • Input: [data]
  • Submission: [submit]


Authorship analysis deals with the classification of texts into classes based on the stylistic choices of their authors. Beyond the author identification and author verification tasks where the style of individual authors is examined, author profiling distinguishes between classes of authors studying their sociolect aspect, that is, how language is shared by people. This helps in identifying profiling aspects such as gender, age, native language, or personality type. Author profiling is a problem of growing importance in applications in forensics, security, and marketing. E.g., from a forensic linguistics perspective one would like being able to know the linguistic profile of the author of a harassing text message (language used by a certain type of people) and identify certain characteristics (language as evidence). Similarly, from a marketing viewpoint, companies may be interested in knowing, on the basis of the analysis of blogs and online product reviews, the demographics of people that like or dislike their products. The focus is on author profiling in social media since we are mainly interested in everyday language and how it reflects basic social and personality processes.


Note. Besides, at RepLab 2013 author profiling will be approached from the online reputation monitoring perspective. Given a large number of Twitter profiles with 600 associated tweets each, participants will be asked to classify the author of a set of tweets as journalist, politician, activist, professional, client, company, authority or citizen, since the fact of belonging to a certain category could determine the importance of the user's opinions. The dataset will contain English and Spanish tweets related to the banking and automotive domains.


We are happy to announce the following overall winner of the 1st International Competition on Author Profiling who will be awarded 300,- Euro sponsored by the Forensic Lab of the Universitat Pompeu Fabra Barcelona.

  • Manuel Montes-y-Gómez, Luis Villaseñor-Pineda, Hugo Jair Escalante, and Adrián Pastor López-Monrroy from INAOE, Mexico.



To develop your software, we provide you with a training data set that consists of documents written in both English and Spanish. With regard to age, we will consider posts of three classes: 10s (13-17), 20s (23-27), and 30s (33-47). Moreover, documents from authors who pretend to be minors will be included (e.g., documents composed of chat lines of sexual predators will be also considered). Learn more »


Your software must take as input the absolute path to an unpacked dataset, and has to output for each document of the dataset a corresponding XML file that looks like this:


The naming of the output files is up to you, we recommend to use the author-id as filename and "xml" as extension. The output files have to be written either directly to the working directory (to "..) or to a subfolder. The author-id has to be extracted from each document's filename which follows the pattern <authorid>_<lang>_<age>_<gender>.xml. Note that in the test corpus the age and gender information are replaced by "xxx".


The performance of your author profiling solution will be ranked by accuracy.


The following table lists the performances achieved by the participating teams:

English author profiling performance
Accuracy Team
0.3894 Michał Meina, Karolina Brodzínska, Bartosz Celmer, Maja Czoków, Martyna Patera, Jakub Pezacki, and Mateusz Wilk
Nicolaus Copernicus University, Poland
0.3813 A. Pastor López-Monroy°, Manuel Montes-y-Gómez°, Hugo Jair Escalante°, Luis Villaseñor-Pineda°, and Esaú Villatoro-Tello*
°Instituto Nacional de Astrofísica, Óptica y Electrónica and *Universidad Autónoma Metropolitana-Cuajimalpa, Mexico
0.3677 Seifeddine Mechti, Maher Jaoua, and Lamia Hadrich Belguith
University of Sfax, Tunisia
0.3508 K Santosh, Romil Bansal, Mihir Shekhar, and Vasudeva Varma
International Institute of Information Technology, India
0.3488 Wee-Yong Lim, Jonathan Goh, and Vrizlynn L. L. Thing
Institute for Infocomm Research, Singapore
0.3420 Susana Ladra°, Francisco Claude*, and Roberto Konow^
°University of A Coruña, Spain, *University of Waterloo, Canada, and ^University of Chile, Chile
0.3292 Yuridiana Aleman, Nahun Loya, Darnes Vilariño, and David Pinto
Benem´erita Universidad Aut´onoma de Puebla, Mexico
0.3268 Lee Gillam
University of Surrey, UK
0.3115 Roman Kern
Know-Center GmbH, Autria
0.3113 Fermín L. Cruz°, Rafa Haro R.*, and F. Javier Ortega°
University of Seville and Zaizi, Spain
0.2843 Aditya Pavan, Aditya Mogadala, and Vasudeva Varma
International Institute of Information Technology, India
0.2840 Andrés Alfonso Caurcel Díaz° and José María Gómez Hidalgo*
Universidad Politécnica de Madrid and Optenet, Spain
0.2816 Delia-Irazú Hernández°, Rafael Guzmán-Cabrera*, Antonio Reyes^, and Martha-Alicia Rocha°'
°Universidad Politécnica de Valencia, Spain, and *Universidad de Guanajuato, ^Instituto Superior de Intérpretes y Traductores, and 'Instituto Tecnológico de León, Mexico
0.2813 Magdalena Jankowska, Vlado Kešelj, and Evangelos Milios
Dalhousie University, Canada
0.2785 Lucie Flekovayz and Iryna Gurevych
Technische Universität Darmstadt and German Institute for Educational Research and Educational Information, Germany
0.2564 Edson R. D. Weren, Viviane P. Moreira, and José P. M. de Oliveira
UFRGS, Brazil
0.2471 Upendra Sapkota°, Thamar Solorio°, Manuel Montes-y-Gómez*, and Gabriela Ramírez-de-la-Rosa°
°University of Alabama at Birmingham, USA, and *Instituto Nacional de Astrofísica, Óptica y Electrónica, Mexico
0.2450 Maria De-Arteaga, Sergio Jimenez, George Dueñas, Sergio Mancera and Julia Baquero
Universidad Nacional de Colombia, Colombia
0.2395 Erwan Moreau and Carl Vogel
Trinity College Dublin, Ireland
0.1650 Baseline
0.1574 Braja Gopal Patra°, Somnath Banerjee°, Dipankar Das*, Tanik Saikh°, Sivaji Bandyopadhyay°
°Jadavpur University and NIT Meghalaya, India
0.0741 Leticia Cagnina, Darío Funez, and Marcelo Errecalde
Universidad Nacional de San Luis, Argentina

A more detailed analysis of the detection performances can be found in the overview paper accompanying this task.

Task Committee