Titlebook: Combating Online Hostile Posts in Regional Languages during Emergency Situation; First International Tanmoy Chakraborty,Kai Shu,Md Shad Ak

平項(xiàng)山

Fake News Detection System Using XLNet Model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Tstributions from Latent Dirichlet Allocation (LDA) with contextualized representations from XLNet. We also compared our method with existing baselines to show that XLNet . Topic Distributions outperforms other approaches by attaining an F1-score of 0.967.

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ingestion

Conference proceedings 2021rs 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..

Ballerina

1865-0929 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..978-3-030-73695-8978-3-030-73696-5Series ISSN 1865-0929 Series E-ISSN 1865-0937

階層

Application to the differential games,lowing this, we propose the use of fine-tuning Distilled Bert using both OLID and an additional hate speech and offensive language dataset. Then, we evaluate our model on the test set, yielding a macro f1 score of 78.8.

AGOG

Algebraic lyapunov and riccati equations,set with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.32% F1-score with SVM on the test set. The data and code is available at: ..

Palpitation

Rock failure under imposed load over caves,amework modeling those features by using BERT language model and external sources, namely Simple English Wikipedia and source reliability tags. The conducted experiments on CONSTRAINT datasets demonstrated the benefit of integrating these features for the early detection of fake news in the healthcare domain.

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Identifying Offensive Content in Social Media Posts,lowing this, we propose the use of fine-tuning Distilled Bert using both OLID and an additional hate speech and offensive language dataset. Then, we evaluate our model on the test set, yielding a macro f1 score of 78.8.

Magisterial

Fighting an Infodemic: COVID-19 Fake News Dataset,set with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.32% F1-score with SVM on the test set. The data and code is available at: ..