Author Profiling 2015

Synopsis

  • Task: Given a document, what're its author's traits?
  • Input: [data]
  • Twitter Downloader: [code]
  • Submission: [submit]

Introduction

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.

Task

This task is about predicting an author's demographics from her writing. Participants will be provided with Twitter tweets in English and Spanish to predict age, gender and personality traits. Moreover, they will be provided also with tweets in Italian and Dutch and asked to predict the gender and personality.

Award

We are happy to announce that the best performing team at the 3rd International Competition on Author Profiling will be awarded 300,- Euro sponsored by MeaningCloud.

  • Miguel Ángel Álvarez Carmona, Adrián Pastor López Monroy, Manuel Montes y Gómez and Luis Villaseñor Pineda from INAOE, Mexico

Congratulations!

Input

To develop your software, we provide you with a training data set that consists of Twitter tweets in English, Spanish, Italian and Dutch. With regard to age, we will consider the following classes: 18-24, 25-34, 35-49, 50-xx. With regard to personality traits, for each trait we will provide scores (between -0.5 and 0.5).

Output

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:

  <author id="{author-id}"
	  type="twitter"
	  lang="en|es|it|nl"
	  age_group="18-24|25-34|35-49|50-xx"
	  gender="male|female"
	  extroverted="-0.5 to +0.5"
	  stable="-0.5 to +0.5"
	  agreeable="-0.5 to +0.5"
	  conscientious="-0.5 to +0.5"
	  open="-0.5 to +0.5"
  />

The naming of the output files is up to you, we recommend to use the author-id as filename and "xml" as extension.

Evaluation

The performance of your author profiling solution for age and gender will be ranked by accuracy.

For personality identification the average Root Mean Squared Error (RMSE) will be used.

For obtaining a global ranking, we apply the following formula: global_ranking = ((1-RMSE) + joint_accuracy)/ 2

Results

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

Author profiling performance
Avg. Accuracy Team
0.8404 Miguel Ángel Álvarez Carmona, Adrián Pastor López Monroy, Manuel Montes y Gómez, Luis Villaseñor Pineda and Hugo Jair Escalante. INAOE Mexico.
0.8346 Carlos E. González Gallardo, Azucena Montes Redón, Gerardo Eugenio Sierra Martínez, José Antonio Nuñez Juárez, Adolfo Jonathan Salinas López and Juan Rodrigo Ek Catzin. UNAM Mexico.
0.8078 Andreas Grivas, Anastasia Krithara and George Giannakopoulos. NCSR Demokritos, Greece.
0.7875 Mirco Kocher. University of Neuchâtel, Switzerland.
0.7755 Octavia Maria Sulea and Daniel Dichiu. Bitdefender and University of Bucharest, Romania.
0.7584 Lesly Miculicich. University of Necuhatel, Switzerland.
0.7338 Scot Nowson, Julien Perez, Caroline Brun, Shachar Mirkin and Claude Roux. Xerox Research Centre Europe, France.
0.7223 Edson Roberto Duarte Weren. Brazil.
0.7130 Adam Poulston, Mark Stevenson and Kalina Bontcheva. University of Sheffield, United Kingdom.
0.7061 Suraj Maharjan and Thamar Solorio. University of Houston. United States.
0.6960 Caitlin McCollister, Bo Lou and Shu Huang. University of Kansas, United States.
0.6875 Mounica Arroju, Aftab Hassan and Golnoosh Farnadi. University of Washington Tacoma, United States.
0.6857 Mayte Gimenez, Delia Irazú Hernández and Ferran Plá. Universitat Politècnica de València, Spain.
0.6809 Alberto Bartoli, Andrea De Lorenzo, Alessandra Laderchi, Eric Medvet and Fabiano Tarlao. University of Trieste, Italy.
0.6685 Ifrah Pervaz, Iqra Ameer, Abdul Sittar, Rao Muhammad Adeel Nawab. COMSATS Institute of Information Technology, Pakistan.
0.6495 Fahad Najib, Waqas Arshad Cheema and Rao Muhammad Adeel Nawab. Comsats Lahore, Pakistan.
0.6401 Piotr Przybyla and Pawel Teisseyre. Polish Academy of Sciences, Poland.
0.6204 Alonso Palomino Garibay, Adolfo T. Camacho González, Ricardo A. Fierro Villaneda, Irazú Hernández Farias, Davide Buscaldi and Ivan Vladimir Meza Ruiz. UNAM, Mexico.
0.6178 Roy Bayot, Teresa Gonçalves and Paolo Quaresma. Universidade de Évora, Portugal.
* Hafiz Rizwan Iqbal, Muhammad Adnan Ashraf and Rao Muhammad Adeel Nawab. Pakistan.
* Yasen Kiprov, Momchil Hardalov, Preslav Nakov and Ivan Koychev. Sofia University "St. Kliment Ohridski", Bulgaria.
* Juan Pablo Posadas Durán, Ilia Markov, Helena Gómez Adorno, Grigori Sidorov, Ildar Batyrshin, Alexander Gelbukh and Obdulia Pichardo Lagunas. National Polytechnic Institute, Mexico.

* Results have been omitted for these teams since they participated in some languages only.

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

Task Committee