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

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incite

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

inferno

摘要

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

intrigue

insolence

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

insolence

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

水槽

創(chuàng)造性

,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

膽汁

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