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Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation ...
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Text mining for online mental health state and personality assessment
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The impact of emotional signals on credibility assessment
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In: J Assoc Inf Sci Technol (2021)
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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In: Natural Language Processing and Information Systems (2020)
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Experimental IR meets multilinguality, multimodality, and interaction: 10th international conference of the CLEF association, CLEF 2019, Lugano, Switzerland, September 9-12, 2019, proceedings
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Opinions in Federated Search: University of Lugano at TREC 2014 Federated Web Search Track
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In: DTIC (2014)
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Concept-based semantic annotation, indexing and retrieval of office-like document units
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A Language Modelling Approach to Linking Criminal Styles with Offender Characteristics
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Evaluation of term-based queries using possibilistic ontologies
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In: Soft Computing for Information Retrieval on the Web ; https://hal.archives-ouvertes.fr/hal-03367400 ; Herrera-Viedma, Enrique; Pasi, Gabriella; Crestani, Fabio. Soft Computing for Information Retrieval on the Web, 197, Springer-Verlag, pp.135-160, 2005, Studies in Fuzziness and Soft Computing book series (STUDFUZZ), ⟨10.1007/3-540-31590-X_8⟩ ; https://link.springer.com/chapter/10.1007/3-540-31590-X_8 (2005)
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Suicide risk assessment on social media: USI-UPF at the CLPsych 2019 shared task
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Abstract:
Comunicació presentada a: The Sixth Workshop on Computational Linguistics and Clinical Psychology, celebrat el 6 de juny de 2019 a Minneapolis, Minnesota, Estats Units d'Amèrica. ; This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych2019). The goal is to assess the degree of suicide risk of social media users given a labelled dataset with their posts. An appropriate suicide risk assessment, with the usage of automated methods, can assist experts on the detection of people at risk and eventually contribute to prevent suicide. We propose a set of machine learning models with features based on lexicons, word embeddings, word level n-grams, and statistics extracted from users’ posts. The results show that the most effective models for the tasks are obtained integrating lexicon-based features, a selected set of n-grams, and statistical measures. ; This work was partially supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
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URL: http://hdl.handle.net/10230/44358 https://doi.org/10.18653/v1/W19-3021
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