標題: Titlebook: Combating Online Hostile Posts in Regional Languages during Emergency Situation; First International Tanmoy Chakraborty,Kai Shu,Md Shad Ak [打印本頁] 作者: 添加劑 時間: 2025-3-21 16:23
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書目名稱Combating Online Hostile Posts in Regional Languages during Emergency Situation讀者反饋
書目名稱Combating Online Hostile Posts in Regional Languages during Emergency Situation讀者反饋學科排名
作者: incite 時間: 2025-3-21 21:15
Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evalugh it spontaneously. Unfortunately, with this rapid advancement, social media misuse has also been proliferated, which leads to an increase in aggressive, offensive and abusive activities. Most of these unlawful activities performed through textual communication. Therefore, it is monumental to crea作者: inferno 時間: 2025-3-22 02:34 作者: 摘要 時間: 2025-3-22 07:53
Revealing the Blackmarket Retweet Game: A Hybrid Approach, is measured by the number of retweets it gains. A significant number of retweets help to broadcast a tweet well and makes the topic of the tweet popular. Individuals and organizations involved in product launches, promotional events, etc. look for a broader reach in their audience and approach blac作者: intrigue 時間: 2025-3-22 12:23 作者: insolence 時間: 2025-3-22 16:32
LaDiff ULMFiT: A Layer Differentiated Training Approach for ULMFiT,ks COVID19 Fake News Detection in English and Hostile Post Detection in Hindi. We propose a Layer Differentiated training procedure for training a pre-trained ULMFiT [.] model. We used special tokens to annotate specific parts of the tweets to improve language understanding and gain insights on the 作者: insolence 時間: 2025-3-22 18:12
Extracting Latent Information from Datasets in CONSTRAINT 2021 Shared Task,Fake News Detection task in English, a binary classification task. This paper chooses RoBERTa as the pre-trained model, and tries to build a graph from news datasets. Finally, our system achieves an accuracy of 98.64% and an F1-score of 98.64% on the test dataset. Subtask2 is a Hostile Post Detectio作者: 水槽 時間: 2025-3-22 21:42 作者: 創(chuàng)造性 時間: 2025-3-23 01:46
,Transformer-Based Language Model Fine-Tuning Methods for COVID-19 Fake?News Detection,can cause great trouble to people’s life. However, universal language models may perform weakly in these fake news detection for lack of large-scale annotated data and sufficient semantic understanding of domain-specific knowledge. While the model trained on corresponding corpora is also mediocre fo作者: 膽汁 時間: 2025-3-23 05:47
Tackling the Infodemic: Analysis Using Transformer Based Models,xtensive analysis to understand the pattern of the data distribution. To achieve an F1 score of 0.96, we incorporate external sources of misinformation and fine tune multiple state of the art pretrained deep learning models. In the end, we visualise the true and false positives predicted by our mode作者: Emasculate 時間: 2025-3-23 09:42 作者: conscience 時間: 2025-3-23 15:01
g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detscussed on social media. However, not all social media posts are truthful. Many of them spread fake news that cause panic among readers, misinform people and thus exacerbate the effect of the pandemic. In this paper, we present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News D作者: gratify 時間: 2025-3-23 20:57 作者: LUT 時間: 2025-3-23 22:40
: ,arly Det,tion of ,VID ,ies Using Content, Prior Knowledge and Source Information,are domain. Therefore, it is important to automatically detect fake news in an early stage before they get widely spread. This paper analyzes the impact of incorporating content information, prior knowledge, and credibility of sources into models for the early detection of fake news. We propose a fr作者: 原告 時間: 2025-3-24 03:36 作者: 竊喜 時間: 2025-3-24 09:03 作者: 心胸開闊 時間: 2025-3-24 11:12
Identification of COVID-19 Related Fake News via Neural Stacking,olution to the shared task titled ., scoring the 50th place amongst 168 submissions. The solution was within 1.5% of the best performing solution. The proposed solution employs a heterogeneous representation ensemble, adapted for the classification task via an additional neural classification head c作者: 語言學 時間: 2025-3-24 15:51
Fake News Detection System Using XLNet Model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Tout truthful information from fake ones. The research community is now faced with the task of automatic detection of fake news, which carries real-world socio-political impact. One such research contribution came in the form of the Constraint@AAA12021 Shared Task on COVID19 Fake News Detection in En作者: 遠地點 時間: 2025-3-24 20:06 作者: 慢跑鞋 時間: 2025-3-25 01:43 作者: fleeting 時間: 2025-3-25 07:23
https://doi.org/10.1007/b138363Roberta, Ernie, etc. with various training strategies including warm-up, learning rate schedule and .-fold cross-validation. We also conduct an extensive analysis of the samples that are not correctly classified. The code is available at: ..作者: musicologist 時間: 2025-3-25 09:16
Sinkhole classification and nomenclature, proposed solution employs a heterogeneous representation ensemble, adapted for the classification task via an additional neural classification head comprised of multiple hidden layers. The paper consists of detailed ablation studies further displaying the proposed method’s behavior and possible implications. The solution is freely available...作者: artless 時間: 2025-3-25 12:13
Exploring Text-Transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English,Roberta, Ernie, etc. with various training strategies including warm-up, learning rate schedule and .-fold cross-validation. We also conduct an extensive analysis of the samples that are not correctly classified. The code is available at: ..作者: 臭了生氣 時間: 2025-3-25 16:16 作者: 褲子 時間: 2025-3-25 22:43
Conference proceedings 2021r?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 soc作者: NICE 時間: 2025-3-26 02:40 作者: ingenue 時間: 2025-3-26 05:14
Sinkholes induced by engineering works,he models used, the ways of text preprocessing and adding extra data. As a result, our best model achieved the weighted F1-score of 98.69 on the test set (the first place in the leaderboard) of this shared task that attracted 166 submitted teams in?total.作者: Mindfulness 時間: 2025-3-26 10:07 作者: 全部逛商店 時間: 2025-3-26 13:43 作者: 整潔 時間: 2025-3-26 19:59 作者: 平項山 時間: 2025-3-27 00:44
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.作者: enmesh 時間: 2025-3-27 03:08 作者: strdulate 時間: 2025-3-27 08:06 作者: ingestion 時間: 2025-3-27 12:31
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..作者: Ballerina 時間: 2025-3-27 16:24
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 作者: 階層 時間: 2025-3-27 17:48
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.作者: AGOG 時間: 2025-3-28 00:43
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: ..作者: Palpitation 時間: 2025-3-28 02:17
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.作者: hair-bulb 時間: 2025-3-28 06:22
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.作者: Magisterial 時間: 2025-3-28 12:35
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: ..作者: 很像弓] 時間: 2025-3-28 17:48 作者: artless 時間: 2025-3-28 19:03
Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/229842.jpg作者: countenance 時間: 2025-3-29 00:28
Combating Online Hostile Posts in Regional Languages during Emergency Situation978-3-030-73696-5Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 極為憤怒 時間: 2025-3-29 06:33 作者: 迷住 時間: 2025-3-29 07:14
Epilogue: The Sinister After Milton, We experimented with the pre-trained model based on the transformer and adopted the method of Ensemble Learning. We observed that the model ensemble was able to obtain better text classification results than a single model, the weighted fine-grained F1 score of our model in subtask B was 0.643998 (ranking 1/45).作者: BROTH 時間: 2025-3-29 12:58
Sinkholes induced by engineering works,xtensive analysis to understand the pattern of the data distribution. To achieve an F1 score of 0.96, we incorporate external sources of misinformation and fine tune multiple state of the art pretrained deep learning models. In the end, we visualise the true and false positives predicted by our model as improvement in future work.作者: 繁榮地區(qū) 時間: 2025-3-29 19:00 作者: neuron 時間: 2025-3-29 22:19 作者: 軌道 時間: 2025-3-30 01:43 作者: 影響帶來 時間: 2025-3-30 04:24
Algebraic lyapunov and riccati equations, is measured by the number of retweets it gains. A significant number of retweets help to broadcast a tweet well and makes the topic of the tweet popular. Individuals and organizations involved in product launches, promotional events, etc. look for a broader reach in their audience and approach blac作者: 小平面 時間: 2025-3-30 09:16
https://doi.org/10.1007/BFb0005209the CONSTRAINT Workshop at AAAI 2021. The shared tasks are ‘COVID19 Fake News Detection in English’ and ‘Hostile Post Detection in Hindi’. The tasks attracted 166 and 44 team submissions respectively. The most successful models were BERT or its variations.作者: Diverticulitis 時間: 2025-3-30 14:18 作者: 停止償付 時間: 2025-3-30 20:00
https://doi.org/10.1007/978-3-319-52797-0Fake News Detection task in English, a binary classification task. This paper chooses RoBERTa as the pre-trained model, and tries to build a graph from news datasets. Finally, our system achieves an accuracy of 98.64% and an F1-score of 98.64% on the test dataset. Subtask2 is a Hostile Post Detectio作者: 白楊魚 時間: 2025-3-31 00:40
Epilogue: The Sinister After Milton, We experimented with the pre-trained model based on the transformer and adopted the method of Ensemble Learning. We observed that the model ensemble was able to obtain better text classification results than a single model, the weighted fine-grained F1 score of our model in subtask B was 0.643998 (作者: Enliven 時間: 2025-3-31 01:11 作者: altruism 時間: 2025-3-31 07:09
Sinkholes induced by engineering works,xtensive analysis to understand the pattern of the data distribution. To achieve an F1 score of 0.96, we incorporate external sources of misinformation and fine tune multiple state of the art pretrained deep learning models. In the end, we visualise the true and false positives predicted by our mode作者: –scent 時間: 2025-3-31 11:29
https://doi.org/10.1007/b138363h the weighted . score of 0.9859 on the test set. Specifically, we proposed an ensemble method of different pre-trained language models such as BERT, Roberta, Ernie, etc. with various training strategies including warm-up, learning rate schedule and .-fold cross-validation. We also conduct an extens作者: colloquial 時間: 2025-3-31 13:37
Sinkholes induced by engineering works,scussed on social media. However, not all social media posts are truthful. Many of them spread fake news that cause panic among readers, misinform people and thus exacerbate the effect of the pandemic. In this paper, we present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News D作者: 小口啜飲 時間: 2025-3-31 19:53 作者: Limerick 時間: 2025-3-31 23:43
