Abstract
Authorship Identification is the branch of authorship analysis concerned with uncovering the author of a written document. Methods devised for Authorship Identification typically employ stylometry (the analysis of unconscious traits that authors exhibit while writing), and are expected not to make inferences grounded on the topics the authors usually write about (as reflected in their past production). In this paper, we present a series of experiments evaluating the use of feature sets based on rhythmic and psycholinguistic patterns for Authorship Verification and Attribution in Spanish political language, via different approaches of text distortion used 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 the BETO transformer when the latter is trained on the original text, i.e., when potentially learning from topical information.
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Notes
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We employ the Spanish version of the dictionary, which is based on LIWC2007.
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We use following categories for each macro-categoy: (i) Yo, Nosotro, TuUtd, ElElla, VosUtds, Ellos, Pasado, Present, Futuro, Subjuntiv, Negacio, Cuantif, Numeros, verbYO, verbTU, verbNOS, verbVos, verbosEL, verbELLOS, formal, informal; (ii) MecCog, Insight, Causa, Discrep, Tentat, Certeza, Inhib, Incl, Excl, Percept, Ver, Oir, Sentir, NoFluen, Relleno, Ingerir, Relativ, Movim; (iii) Maldec, Afect, EmoPos, EmoNeg, Ansiedad, Enfado, Triste, Asentir, Placer. We avoid employing categories that would repeat information already captured by the POS tags, or topic-related categories such as Dinero or Familia.
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We also performed preliminary experiments with other learners: SVM showed a remarkably better performance than Random Forest, while no significant differences were noticed between SVM and Logistic Regression.
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https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased. This model obtained better results than the ‘uncased’ version in preliminary experiments.
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Acknowledgment
The work by Silvia Corbara has been carried out during her visit at the Universitat Politècnica de València and has been supported by the AI4Media project, funded by the European Commission (Grant 951911) under the H2020 Programme ICT-48-2020.
The research work by Paolo Rosso was partially funded by the Generalitat Valenciana under DeepPattern (PROMETEO/2019/121).
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Corbara, S., Chulvi Ferriols, B., Rosso, P., Moreo, A. (2022). Investigating Topic-Agnostic Features for Authorship Tasks in Spanish Political Speeches. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham. https://doi.org/10.1007/978-3-031-08473-7_36
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