In this study, we present a new method for profiling the author of an anonymous English text. The aim of author profiling is to determine demographic (age, gender, region, education level) and psychological (personality, mental health) properties of the authors of a text, especially authors of user generated content in social media. To obtain the best classification, authors resort to machine l...

In this paper we describe and evaluate the corpora submitted to the PAN 2015 shared task on plagiarism detection for text alignment. We received monoand cross-language corpora in the following languages (pairs): English, Persian, Chinese, and Urdu-English, English-Persian. We present an independent section for each submitted corpus including statistics, discussion of the obfuscation techniques ...

This paper describes our approach for the Author Profiling Shared Task at PAN 2017. The goal was to classify the gender and language variety of a Twitter user solely by their tweets. Author Profiling can be applied in various fields like marketing, security and forensics. Twitter already uses similar techniques to deliver personalized advertisement for their users. PAN 2017 provided a corpus fo...

We present the results of gender and language variety identification performed on the tweet corpus prepared for the PAN 2017 Author profiling shared task. Our approach consists of tweet preprocessing, feature construction, feature weighting and classification model construction. We propose a Logistic regression classifier, where the main features are different types of character and word n-gram...

This overview presents the framework and the results for the Author Profiling task at PAN 2014. Objective of this year is the analysis of the adaptability of the detection approaches when given different genres. For this purpose a corpus with four different parts (subcorpora) has been compiled: social media, Twitter, blogs, and hotel reviews. The construction of the Twitter subcorpus happened i...

In this paper we present an approach for the task of author profiling. We propose a modular framework, extracting two main group of features, combined with appropriate preprocessing, implementing Support Vector Machines for classification. The two main groups we used were stylometric and discriminative, featuring trigrams on one hand and complementary-weighted Second Order Attributes on the oth...

To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both singleand cross-genre conditions. We show that under certain settings the neural network-based features outperform t...

This paper presents our approach to the Author Profiling (AP) task at PAN 2016. The task aims at identifying the author’s age and gender under crossgenre AP conditions in three languages: English, Spanish, and Dutch. Our preprocessing stage includes reducing non-textual features to their corresponding semantic classes. We exploit typed character n-grams, lexical features, and nontextual feature...

This paper describes two methodologies applied to the author profiling task submitted to the PAN 2013 competition of the CLEF 2013 conference. The first methodology was applied only to the English language, whereas the second one was executed only over the corpus written in Spanish language. The aim was to evaluate the performance of both methodologies in the above mentioned task. The obtained ...

Predicting an author’s age, gender and personality traits by analyzing his/her documents is important in forensics, marketing and resolving authorship disputes. Our system combines different styles, lexicons, topics, familial tokens and different categories of character n-grams as features to build a logistic regression model for four different languages: English, Spanish, Italian and Dutch. Wi...