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 topic-agnostic feature sets for Authorship Identification and Author Profiling tasks in Spanish political language. In particular, we propose to employ features based on rhythmic and psycholinguistic 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.
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Notes
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Precisely, the PAN2021 event presented a particular case of AV where the dataset contained pairs of documents, and the aim was to infer whether the two documents shared the same author; we call this task Same-Authorship Verification (SAV).
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Regionalist parties aim for more political power for regional entities.
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Note that we use the decade of birth as representation of age group. We assign the closest decade label to each author’s birth; for example, an author born in 1984 is assigned the label ‘1980’, while an author born in 1987 is assigned the label ‘1990’.
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We employ the Spanish version of the dictionary, which is based on LIWC2007.
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We use the following categories: (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, Asentir, Tentat, Certeza, Inhib, Incl, Excl, Percept, Ver, Oir, Sentir, NoFluen, Relleno, Ingerir, Relativ, Movim; (iii) Maldec, Afect, EmoPos, EmoNeg, Ansiedad, Enfado, Triste, Placer. We avoid employing categories that would repeat information already captured by the POS tags, or topic-related categories (e.g., Dinero, Familia).
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Formally, LIWC can be seen as a map \(m : w \rightarrow C\), where w is a word token and \(C\subset \mathcal {C}\) is a subset of the psycholinguistic categories \(\mathcal {C}\). Given a macro-category \(M\subset \mathcal {C}\), we replace each word w in a document by the categories \(m(w)\cap M\). If \(|m(w)\cap M|>1\), then a new token is created which consists of a concatenation of the category names (following a consistent ordering). If \(m(w)\cap M=\emptyset \), then w is replaced with the ‘w’ symbol. (Note that some entries in LIWC have the suffix truncated and replaced with an asterisk ‘*’, e.g., president*; the asterisk is treated as a wildcard in the mapping function, and in case more than one match is possible, the match with the longest common prefix is returned).
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We also carried out preliminary experiments with Random Forest (RF) and Logistic Regression (LR). SVM showed a remarkably better performance than RF, while no significant differences were noticed between SVM and LR.
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Indeed, LIWC_GRAM, LIWC_COG and LIWC_FEELS create the highest number of features in our experiments, ranging from 3000 to more than 20000.
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The selection is always carried out in the training set. During the 5-fold cross-validation optimization phase, feature selection is carried out in the corresponding \(80\%\) of the training set used as training.
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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 research work by Silvia Corbara was carried out during her visit at the Universitat Politècnica de València and was supported by the AI4Media project, funded by the EU Commission (Grant 951911, 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, B., Rosso, P., Moreo, A. (2022). Rhythmic and Psycholinguistic Features for Authorship Tasks in the Spanish Parliament: Evaluation and Analysis. In: Barrón-Cedeño, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham. https://doi.org/10.1007/978-3-031-13643-6_6
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