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« “Twitta” “Intellectuelle” “Influenceuse” ? Être enseignante-chercheuse sur twitter »
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In: ISSN: 1763-0061 ; EISSN: 1963-1812 ; Tracés : Revue de Sciences Humaines ; https://hal.archives-ouvertes.fr/hal-03592945 ; Tracés : Revue de Sciences Humaines, ENS Éditions, A paraître (2022)
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Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
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In: Sensory Analysis for the Development of Meat Products ; https://hal-agrosup-dijon.archives-ouvertes.fr/hal-03575175 ; Sensory Analysis for the Development of Meat Products, Elsevier, pp.181-196, 2022, 9780128228326. ⟨10.1016/B978-0-12-822832-6.00013-8⟩ (2022)
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Health TrueInfo: A multilingual Android app and social media approach in tackling COVID-19 vaccine misinformation and hesitancy in Bolivia, India, and Canada
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In: University of Toronto Journal of Public Health; Vol. 3 No. 1 (2022): Special Issue of Abstracts from Conferences ; 2563-1454 (2022)
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Detecting weak and strong Islamophobic hate speech on social media
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Word Frequency Analysis of Community Reaction to Religious Violence on Social Media
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In: School of Computer Science & Engineering Faculty Publications (2022)
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Examining negative online social reaction to police use of force : the George Floyd and Jacob Blake events
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Η επίδραση των κοινωνικών μέσων δικτύωσης στον σχεδιασμό ενός ταξιδιού: Ταξιδιωτική πρόθεση και αντίληψη κινδύνου κατά τη διάρκεια της πανδημίας
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Language modeling for personality prediction
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Abstract:
This dissertation can be divided into two large questions. The first is a supervised learning problem: given text from an individual, how much can be said about their personality? The second is more fundamental: what personality structure is embedded in modern language models? To address the first question, three language models are used to predict many traits from Facebook Statuses. Traits include: gender, religion, politics, Big5 personality, sensational interests, impulsiveness, IQ, fair-mindedness, and self-disclosure. Linguistic Inquiry Word Count (Pennebaker et al., 2015), the dominant model used in psychology, explains close to zero variance on many labels. Bag of Words performs well and the model weights provide valuable insight about why predictions are made. Neural Nets perform the best by a wide margin on personality traits especially when few training samples are available. A pretrained personality model is made available online that can explain 10% of the variance of a trait with as little as 400 samples, within the range of normal psychology studies. This is a good replacement for Linguistic Inquiry Word Count in predictive settings. In psychology, personality structure is defined by dimensionality reduction of word vectors (Goldberg, 1993). To address the second question, factor analysis is performed on embeddings of personality words produced by the language model RoBERTa (Liu et al., 2019). This recovers two factors that look like Digman’s α and β (Digman, 1997) and not the more popular Big Five. The structure is shown to be robust to choice of context around an embedded word, language model, factorization method, word set and English vs Spanish. This is a flexible tool for exploring personality structure that can easily be applied to other languages.
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Keyword:
Computer engineering; Lexical hypothesis; Machine learning; Natural language processing; Personality; Social media
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URL: https://hdl.handle.net/2144/41942
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The Rhetoric of Psychopathology: An Interdisciplinary Approach to Understanding and Talking About Mental Health
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An Approach Utilizing Linguistic Features for Fake News Detection
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In: IFIP Advances in Information and Communication Technology ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) ; https://hal.inria.fr/hal-03287679 ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.646-658, ⟨10.1007/978-3-030-79150-6_51⟩ (2021)
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Emotionally Informed Hate Speech Detection: A Multi-target Perspective
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In: ISSN: 1866-9956 ; EISSN: 1866-9964 ; Cognitive Computation ; https://hal.archives-ouvertes.fr/hal-03275549 ; Cognitive Computation, Springer, 2021, 13 (4), ⟨10.1007/s12559-021-09862-5⟩ ; https://link.springer.com/article/10.1007%2Fs12559-021-09862-5 (2021)
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Multiword Expression Features for Automatic Hate Speech Detection
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In: NLDB 2021 - 26th International Conference on Natural Language & Information Systems ; https://hal.archives-ouvertes.fr/hal-03231047 ; NLDB 2021 - 26th International Conference on Natural Language & Information Systems, Jun 2021, Saarbrücken/Virtual, Germany ; http://nldb2021.sb.dfki.de/ (2021)
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Influencer detection in social media ; Détection des influenceurs dans des médias sociaux
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In: https://tel.archives-ouvertes.fr/tel-03640442 ; Ordinateur et société [cs.CY]. Institut National des Langues et Civilisations Orientales- INALCO PARIS - LANGUES O', 2021. Français. ⟨NNT : 2021INAL0034⟩ (2021)
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Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.
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In: Biological psychiatry. Cognitive neuroscience and neuroimaging, vol 6, iss 9 (2021)
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Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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ARMYPEDIA: A “Special Chronicle” of Multilingual Digital Crowdsourcing ...
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Kittinger, Allison. - : The University of North Carolina at Chapel Hill University Libraries, 2021
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Civility in digital discourse : an experimental approach to the contagion of thoughtful and hurtful responses ...
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Influencer detection in social media ; Détection des influenceurs dans des médias sociaux
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In: https://tel.archives-ouvertes.fr/tel-03640442 ; Ordinateur et société [cs.CY]. Institut National des Langues et Civilisations Orientales- INALCO PARIS - LANGUES O', 2021. Français. ⟨NNT : 2021INAL0034⟩ (2021)
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