Among the many tasks of the authorship field, Authorship Identification aims at uncovering the author of a document, while Author Profiling focuses on the analysis of personal characteristics of the author(s), such as gender, age, etc. Methods devised for such tasks typically focus on the style of the writing, and are expected not to make inferences grounded on the topics that certain authors tend to write about. In this paper, we present a series of experiments evaluating the use of topicagnostic feature sets for Authorship Identification and Author Profiling tasks in Spanish political language. In particular, we propose to employ features based on rhythmic and sycholinguistic patterns, obtained via different approaches of text masking that we use to actively mask the underlying topic. We feed these feature sets to a SVM learner, and show that they lead to results that are comparable to those obtained by a BETO transformer, when the latter is trained on the original text, i.e., potentially learning from topical information. Moreover, we further investigate the results for the different authors, showing that variations in performance are partially explainable in terms of the authors’ political affiliation and communication style

Rhythmic and psycholinguistic features for authorship tasks in the Spanish parliament : evaluation and analysis

Corbara, Silvia;
2022

Abstract

Among the many tasks of the authorship field, Authorship Identification aims at uncovering the author of a document, while Author Profiling focuses on the analysis of personal characteristics of the author(s), such as gender, age, etc. Methods devised for such tasks typically focus on the style of the writing, and are expected not to make inferences grounded on the topics that certain authors tend to write about. In this paper, we present a series of experiments evaluating the use of topicagnostic feature sets for Authorship Identification and Author Profiling tasks in Spanish political language. In particular, we propose to employ features based on rhythmic and sycholinguistic patterns, obtained via different approaches of text masking that we use to actively mask the underlying topic. We feed these feature sets to a SVM learner, and show that they lead to results that are comparable to those obtained by a BETO transformer, when the latter is trained on the original text, i.e., potentially learning from topical information. Moreover, we further investigate the results for the different authors, showing that variations in performance are partially explainable in terms of the authors’ political affiliation and communication style
2022
Settore INF/01 - Informatica
13th International Conference of the CLEF Association
Bologna
5–8 settembre 2022
Experimental IR Meets Multilinguality, Multimodality, and Interaction
978-3-031-13642-9
Authorship Analysis; Text masking; Political speech
   DeepPattern
   Generalitat Valenciana
   PROMETEO/2019/121

   A European Excellence Centre for Media, Society and Democracy
   AI4Media
   European Commission
   Horizon 2020 Framework Programme
   951911
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/143623
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