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Record Nr. |
UNINA9910483661103321 |
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Titolo |
Combating Online Hostile Posts in Regional Languages during Emergency Situation : First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers / / edited by Tanmoy Chakraborty, Kai Shu, H. Russell Bernard, Huan Liu, Md Shad Akhtar |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (268 pages) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 1402 |
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Disciplina |
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Soggetti |
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Database management |
Artificial intelligence |
Social sciences - Data processing |
Application software |
Database Management System |
Artificial Intelligence |
Computer Application in Social and Behavioral Sciences |
Computer and Information Systems Applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Identifying Offensive Content in Social Media Posts -- Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation -- Fighting an Infodemic: COVID-19 Fake News Dataset -- Revealing the Blackmarket Retweet Game: A Hybrid Approach -- Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts -- LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT -- Extracting latent information from datasets in The CONSTRAINT-2020 shared task on the hostile post detection -- Fake news and hostile posts detection using an ensemble learning model -- Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection -- |
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Tackling the infodemic : Analysis using Transformer based models -- Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English -- g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection -- Model Generalization on COVID-19 Fake News Detection -- ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information -- Evaluating Deep Learning Approaches for Covid19 Fake News Detection -- A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection -- Identification of COVID-19 related Fake News via Neural Stacking -- Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task -- Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings -- Hostility Detection in Hindi leveraging Pre-Trained Language Models -- Stacked embeddings and multiple fine-tuned XLM-RoBERTa models for Enhanced hostility identification -- Task Adaptive Pretraining of Transformers for Hostility Detection -- Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi. |
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Sommario/riassunto |
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This book constitutes selected and revised papers from the First International Workshop on Combating On line Ho st ile Posts in Regional Languages dur ing Emerge ncy Si tuation, CONSTRAINT 2021, Collocated with AAAI 2021, held as virtual event, in February 2021. The 14 full papers and 9 short papers presented were thoroughly reviewed and selected from 62 qualified submissions. The papers present interdisciplinary approaches on multilingual social media analytics and non-conventional ways of combating online hostile posts. |
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