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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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The Rhetoric of Psychopathology: An Interdisciplinary Approach to Understanding and Talking About Mental Health
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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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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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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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Generating health evidence from social media ; Extração de informação de saúde através das redes sociais
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Studying depression through big data analytics on Twitter
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In: TDX (Tesis Doctorals en Xarxa) (2021)
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Civility in digital discourse : an experimental approach to the contagion of thoughtful and hurtful responses
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Espraiamento do conservadorismo no Brasil das mídias sociais ; Spread of conservatism in Brazil on social networks
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Paiva, Síria Maria Andrade. - : Universidade Federal do Rio Grande do Norte, 2021. : Brasil, 2021. : UFRN, 2021. : Serviço Social, 2021. : Departamento de Serviço Social, 2021
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Content Selection for Effective Counter-Argument Generation
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Clickbait detection using multimodel fusion and transfer learning ; Détection de clickbait utilisant fusion multimodale et apprentissage par transfert
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In: https://tel.archives-ouvertes.fr/tel-03139880 ; Social and Information Networks [cs.SI]. Institut Polytechnique de Paris, 2020. English. ⟨NNT : 2020IPPAS025⟩ (2020)
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Abstract:
Internet users are likely to be victims to clickbait assuming as legitimate news. The notoriety of clickbait can be partially attributed to misinformation as clickbait use an attractive headline that is deceptive, misleading or sensationalized. A major type of clickbait are in the form of spam and advertisements that are used to redirect users to web sites that sells products or services (often of dubious quality). Another common type of clickbait are designed to appear as news headlines and redirect readers to their online venues intending to make revenue from page views, but these news can be deceptive, sensationalized and misleading. News media often use clickbait to propagate news using a headline which lacks greater context to represent the article. Since news media exchange information by acting as both content providers and content consumers, misinformation that is deliberately created to mislead requires serious attention. Hence, an automated mechanism is required to explore likelihood of a news item being clickbait.Predicting how clickbaity a given news item is difficult as clickbait are very short messages and written in obscured way. The main feature that can identify clickbait is to explore the gap between what is promised in the social media post, news headline and what is delivered by the article linked from it. The recent enhancement to Natural Language Processing (NLP) can be adapted to distinguish linguistic patterns and syntaxes among social media post, news headline and news article.In my Thesis, I propose two innovative approaches to explore clickbait generated by news media in social media. Contributions of my Thesis are two-fold: 1) propose a multimodel fusion-based approach by incorporating deep learning and text mining techniques and 2) adapt Transfer Learning (TL) models to investigate the efficacy of transformers for predicting clickbait contents.In the first contribution, the fusion model is built on using three main features, namely similarity between post and headline, sentiment of the post and headline and topical similarity between news article and post. The fusion model uses three different algorithms to generate output for each feature mentioned above and fuse them at the output to generate the final classifier.In addition to implementing the fusion classifier, we conducted four extended experiments mainly focusing on news media in social media. The first experiment is on exploring content originality of a social media post by amalgamating the features extracted from author's writing style and online circadian rhythm. This originality detection approach is used to identify news dissemination patterns among news media community in Facebook and Twitter by observing news originators and news consumers. For this experiment, dataset is collected with our implemented crawlers from Facebook and Twitter streaming APIs. The next experiment is on exploring flaming events in the news media in Twitter by using an improved sentiment classification model. The final experiment is focused on detecting topics that are discussed in a meeting real-time aiming to generate a brief summary at the end.The second contribution is to adapt TL models for clickbait detection. We evaluate the performance of three TL models (BERT, XLNet and RoBERTa) and delivered a set of architectural changes to optimize these models.We believe that these models are the representatives of most of the other TL models in terms of their architectural properties (Autoregressive model vs Autoencoding model) and training datasets. The experiments are conducted by introducing advanced fine-tuning approaches to each model such as layer pruning, attention pruning, weight pruning, model expansion and generalization. To the best of authors' knowledge, there have been an insignificant number of attempts to use TL models on clickbait detection tasks and no any comparative analysis of multiple TL models focused on this task. ; Presque tous les internautes sont susceptibles d'être victimes de clickbait, supposant à tort qu’il s’agit d’informations légitimes. Un type important de clickbait se présente sous la forme de spam et de publicités qui sont utilisés pour rediriger les utilisateurs vers des sites web. Un autre type de "clickbait" est conçu pour faire la une des journaux et rediriger les lecteurs vers leurs sites en ligne, mais ces nouvelles sensationnelles peuvent être trompeuses. Il est difficile de prédire le degré de click-baity d'une nouvelle donnée car les clickbait sont des messages très courts et écrits de manière souvent obscure. La principale caractéristique qui permet d'identifier les clickbait est d'explorer l'écart entre ce qui est attendu dans un post, le titre de l'information et l’information réellement présente dans l'article qui y est lié. Dans cette thèse, on propose deux approches innovantes pour explorer le clickbait généré par les médias d'information dans les médias sociaux. Les contributions 1) de proposer une approche multimodèle basée sur la fusion en incorporant des techniques d'apprentissage profond et d'exploration de texte et 2) d’adapter les modèles d'apprentissage par transfert (TL) pour étudier l'efficacité des transformateurs permettant de prédire le contenu des clickbaits.
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Keyword:
[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; Analyse des sentiments; Apprentissage par transfert; Apprentissage profond; Clickbait; Deep learning; Médias d'information; Médias sociaux; News media; Sentiment analysis; Social media; Transfer learning
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URL: https://tel.archives-ouvertes.fr/tel-03139880
https://tel.archives-ouvertes.fr/tel-03139880/file/98914_PRABODA_CHATHURANGANI_RAJAPAKSHA_2020_ARCHIVAGE.pdf
https://tel.archives-ouvertes.fr/tel-03139880/document
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An old tradition in a new space : a critical discourse analysis of YouTubers' metalinguistic commentary on Quebec French
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Towards Subjective Multimedia Summarization Framework for Sporting Event in the Context of Digital Twins
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Detection and analysis of drug non-adherence in social media ; Détection et analyse de la non-adhérence médicamenteuse dans les réseaux sociaux
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In: https://tel.archives-ouvertes.fr/tel-02478927 ; Linguistique. Université de Lille, 2019. Français. ⟨NNT : 2019LILUH026⟩ (2019)
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Neologia de empresa: o Facebook como observatório de novas tendências em neologia
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Language, queerly phrased: a sociolinguistic examination of nonbinary gender identity in French
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Computational linguistics using social media to understand immigrant sentiment in the United States
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Bain, James. - : University of Missouri--Columbia, 2019
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