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020			_a9783030736965
024	7		_a10.1007/978-3-030-73696-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aPN4784.F27 _b2021 EB
245	0	0	_aCombating Online Hostile Posts in Regional Languages during Emergency Situation : _bFirst International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers _cedited by Tanmoy Chakraborty, Kai Shu, H Russell Bernard, Huan Liu, Md Shad Akhtar
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XI, 258 páginas) _b19 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aCommunications in Computer and Information Science _x1865-0929 _v1402
505	0		_aIdentifying 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 -- 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.
520	3		_aThis 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.
988			_aSpringer_Computer_2021
650		0	_aNoticias falsas _vCongresos y asambleas _9671085
700	1		_aChakraborty, Tanmoy _eeditor literario _0(orcid)0000-0002-0210-0369 _1https://orcid.org/0000-0002-0210-0369 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aShu, Kai _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBernard, H. Russell _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLiu, Huan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aAkhtar, Md Shad _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9783030736958
776	0	8	_iPrinted edition: _z9783030736972
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-73696-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _ek _zSI