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Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores
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Multi-National Topics Maps for Parliamentary Debate Analysis
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Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University
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In: Lee, Hyowon orcid:0000-0003-4395-7702 , Scriney, Michael orcid:0000-0001-6813-2630 , Dey-Plissonneau, Aparajita and Smeaton, Alan orcid:0000-0003-1028-8389 (2021) Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University. In: Virtual Exchange in Higher Education: Charting the Irish Experience, 17 Sept 2021, Online vs MS Teams. (2021)
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Contextualization of Web contents through semantic enrichment from linked open data ; Contextualisation des contenus Web par l'enrichissement sémantique à partir de données
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In: https://tel.archives-ouvertes.fr/tel-03561788 ; Databases [cs.DB]. Normandie Université, 2021. English. ⟨NNT : 2021NORMC243⟩ (2021)
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Usage of Visual Analytics to Support Immigration-Related, Personalised Language Training Scenarios ...
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Usage of Visual Analytics to Support Immigration-Related, Personalised Language Training Scenarios ...
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Toward a Better Understanding of Academic Programs Educational Objectives: A Data Analytics-Based Approach
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In: Applied Sciences ; Volume 11 ; Issue 20 (2021)
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LLOD-driven Bilingual Word Embeddings Rivaling Cross-lingual Transformers in Quality of Life Concept Detection from French Online Health Communities ...
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LLOD-driven Bilingual Word Embeddings Rivaling Cross-lingual Transformers in Quality of Life Concept Detection from French Online Health Communities ...
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LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2 ...
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LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2 ...
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LADDER. Learners' digital communication: a corpus for pragmatic competences in Italian L1/L2 ...
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English Machine Reading Comprehension Datasets: A Survey ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
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Neural Machine Translation for Conditional Generation of Novel Procedures
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Inferring the Relationship between Anxiety and Extraversion from Tweets during COVID19 – A Linguistic Analytics Approach
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Unsupervised Deep Learning for Fake Content Detection in Social Media
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Abstract:
Fake content is ever increasing in the online environment, driven by various motivations such as gain-ing commercial and political advantages. The interactive and collaborative nature of social media further fuels the growth of fake content by exerting fast and widespread influence. Despite growing and interdisciplinary efforts in detecting fake content in social media, some common research challenges remain to be addressed such as humans’ cognitive bias and scarcity of labeled data for training supervised machine learning models. This study aims to tackle both challenges by developing unsupervised deep learning models for the detection of fake content in social media. In view that traditional linguistic features fail to capture context information, our proposed method learns feature representations from the context in social media content. The empirical evaluation results with fake comments from YouTube demonstrate that our proposed methods not only outperform baseline models with traditional unsupervised machine learning techniques, but also achieve comparable performance to the state-of-the-art supervised models. The proposed analytical pipeline provides an end-to-end solution to detecting fake social media contents, which largely reduce the human labor required in collaborative data science teams (i.e., particularly the data labeling). The findings of this study can be used to facilitate collaboration in data science by reducing humans’ cognitive bias and improve the collaboration efficiency.
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Keyword:
Collaboration for Data Science; deep learning; natural language processing; social media analytics; unsupervised learning
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URL: http://hdl.handle.net/10125/70643 https://doi.org/10.24251/HICSS.2021.032
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Cognitive biases in developing biased Artificial Intelligence recruitment system
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Unpacking 'the Next Black Box': Investigating the Cognitive and Affective Underpinnings of Student Self-Assessment
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Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?
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In: Faculty Publications (2021)
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Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’
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