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Found 8 papers, 1 papers with code
Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text
Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B.
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets.
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
First, we incorporate prompt learning into multimodal fake news detection.
Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques
The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of the task.
Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide.
Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
This paper describes the participation of team {``}bertha-von-suttner{''} in the SemEval2019 task 4 Hyperpartisan News Detection task.
Comparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation
Preliminary analysis identifies clearly different attitudes on the same issue presented in different news sources.
