Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models

2023-04-02 · Rabia Bounaama, Mohammed El Amine Abderrahim

The present paper is about the participation of our team "techno" on CERIST'22 shared tasks. We used an available dataset "task1.c" related to covid-19 pandemic. It comprises 4128 tweets for sentiment analysis task and 8661 tweets for fake news detection task. We used natural language processing tools with the combination of the most renowned pre-trained language models BERT (Bidirectional Encoder Representations from Transformers). The results shows the efficacy of pre-trained language models as we attained an accuracy of 0.93 for the sentiment analysis task and 0.90 for the fake news detection task.

📄 PDF Abstract BibTeX arXiv:2304.00636

Code (0)

등록된 구현이 없습니다.

Tasks

Fake News Detection Sentiment Analysis

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Refunds@Expedia|||How do I get a full refund from Expedia? “How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Quick Help &…

Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…

Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings

2023-03-07 · Abdul Rehman, Rabeeh Ayaz Abbasi, Irfan ul Haq Qureshi, Akmal Saeed Khattak

The FakeNews task in MediaEval 2022 investigates the challenge of finding accurate and high-performance models for the classification of conspiracy tweets related to COVID-19. In this paper, we used BERT, ELMO, and their…

Word Embeddings

Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection

2021-02-17 · William Scott Paka, Rachit Bansal, Abhay Kaushik, Shubhashis Sengupta 외

As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-1…

Fake News Detection Misinformation

Written and spoken corpus of real and fake social media postings about COVID-19

2023-10-06 · Ng Bee Chin, Ng Zhi Ee Nicole, Kyla Kwan, Lee Yong Han Dylann 외

This study investigates the linguistic traits of fake news and real news. There are two parts to this study: text data and speech data. The text data for this study consisted of 6420 COVID-19 related tweets re-filtered f…

A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection

2021-01-10 · Sourya Dipta Das, Ayan Basak, Saikat Dutta

The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world stay connected. With the COVID-19 pandemic raging, social media has become…

Fake News Detection

Hostility Detection and Covid-19 Fake News Detection in Social Media

2021-01-15 · Ayush Gupta, Rohan Sukumaran, Kevin John, Sundeep Teki

Withtheadventofsocialmedia,therehasbeenanextremely rapid increase in the content shared online. Consequently, the propagation of fake news and hostile messages on social media platforms has also skyrocketed. In this pape…

Abusive Language Fake News Detection Multi-Label Text Classification Word Embeddings