Détection de clickbait utilisant fusion multimodale et apprentissage par transfert

Clickbait detection using multimodel fusion and transfer learning

• Auteur / Autrice : Rajapaksha Waththe Vidanelage Praboda Chathurangani Rajapaksha
• Direction : Noël Crespi, Reza Farahbakhsh
• Type : Thèse de doctorat
• Discipline(s) : Informatique
• Date : Soutenance le 27/11/2020
• Etablissement(s) : Institut polytechnique de Paris
• Ecole(s) doctorale(s) : École doctorale de l'Institut polytechnique de Paris
• Partenaire(s) de recherche : Laboratoire : Télécom SudParis (France) - Institut Polytechnique de Paris / IP Paris - Département Réseaux et Services Multimédia Mobiles / RS2M - Réseaux, Systèmes, Services, Sécurité / R3S-SAMOVAR
• Jury : Président / Présidente : Gareth Tyson
• Jury : Examinateurs / Examinatrices : Xiaoming Fu, Christophe Cerisara, Bruce Macdowell Maggs, Cécile Bothorel
• Jury : Rapporteurs / Rapporteuses : Xiaoming Fu, Christophe Cerisara

Mots clés

Mots clés contrôlés

Médias

Réseaux sociaux (Internet)

Exploration de données

Modélisation des données (informatique)

Apprentissage profond

Mots clés libres

Clickbait

Apprentissage par transfert

Apprentissage profond

Médias sociaux

Médias d'information

Analyse des sentiments

Clickbait

Transfer learning

Deep learning

Social media

News media

Sentiment analysis

Résumé

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.

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.