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Concepts and Experiments on Psychoanalysis Driven Computing

arXiv 2210.00850 ↗First posted 29 Sep 2022cs.CLcited by 2

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Abstract (arXiv)

This research investigates the effective incorporation of the human factor and user perception in text-based interactive media. In such contexts, the reliability of user texts is often compromised by behavioural and emotional dimensions. To this end, several attempts have been made in the state of the art, to introduce psychological approaches in such systems, including computational psycholinguistics, personality traits and cognitive psychology methods. In contrast, our method is fundamentally different since we employ a psychoanalysis-based approach; in particular, we use the notion of Lacanian discourse types, to capture and deeply understand real (possibly elusive) characteristics, qualities and contents of texts, and evaluate their reliability. As far as we know, this is the first time computational methods are systematically combined with psychoanalysis. We believe such psychoanalytic framework is fundamentally more effective than standard methods, since it addresses deeper, quite primitive elements of human personality, behaviour and expression which usually escape methods functioning at "higher", conscious layers. In fact, this research is a first attempt to form a new paradigm of psychoanalysis-driven interactive technologies, with broader impact and diverse applications. To exemplify this generic approach, we apply it to the case-study of fake news detection; we first demonstrate certain limitations of the well-known Myers-Briggs Type Indicator (MBTI) personality type method, and then propose and evaluate our new method of analysing user texts and detecting fake news based on the Lacanian discourses psychoanalytic approach.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 36 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

2 of 36 references.

References · 2

  1. 2022Emotion-based Modeling of Mental Disorders on Social Mediacited by 4
  2. 2015Computational fact checking from knowledge networkscited by 3

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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.