23 citations · 93 across the 44 of their papers we have counts for
12 papers · 1 filter
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Ben Wu, Yue Li, Yida Mu +3
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks…
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
Tomas Goldsack, Zhihao Zhang, Chen Tang +2
Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e.g., researchers), is unlikel…
Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study
Freddy Heppell, Kalina Bontcheva, Carolina Scarton
This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn.world) and WarOnFakes (waronfakes.com), which publish content in Ara…
Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals
João A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva +1
Credibility signals represent a wide range of heuristics typically used by journalists and fact-checkers to assess the veracity of online content. Automating the extraction of cred…
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Olesya Razuvayevskaya, Ben Wu, Joao A. Leite +5
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrate…
Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking
Iknoor Singh, Carolina Scarton, Xingyi Song +1
Finding previously debunked narratives involves identifying claims that have already undergone fact-checking. The issue intensifies when similar false claims persist in multiple la…
Noisy Self-Training with Data Augmentations for Offensive and Hate Speech Detection Tasks
João A. Leite, Carolina Scarton, Diego F. Silva
Online social media is rife with offensive and hateful comments, prompting the need for their automatic detection given the sheer amount of posts created every second. Creating hig…
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
Yida Mu, Ye Jiang, Freddy Heppell +4
The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challe…
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Yida Mu, Ben P. Wu, William Thorne +5
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts. However, due to…
Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification
Yue Li, Carolina Scarton
Considering a conversation thread, rumour stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a target (rumour story). Although the targe…
SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
Ben Wu, Olesya Razuvayevskaya, Freddy Heppell +4
This paper describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multi-lingual setup. For Subtask 1…
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Yida Mu, Mali Jin, Charlie Grimshaw +3
Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vacci…