平项山
发表于 2025-3-27 00:44:42
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.
enmesh
发表于 2025-3-27 03:08:06
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strdulate
发表于 2025-3-27 08:06:57
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ingestion
发表于 2025-3-27 12:31:16
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
发表于 2025-3-27 16:24:37
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
阶层
发表于 2025-3-27 17:48:36
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
发表于 2025-3-28 00:43:18
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
发表于 2025-3-28 02:17:05
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.
hair-bulb
发表于 2025-3-28 06:22:38
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
发表于 2025-3-28 12:35:02
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: ..