739 citations · 2.4k across the 93 of their papers we have counts for
8 papers · 1 filter
Generalizing Political Leaning Inference to Multi-Party Systems: Insights from the UK Political Landscape
Joseba Fernandez de Landa, Arkaitz Zubiaga, Rodrigo Agerri
An ability to infer the political leaning of social media users can help in gathering opinion polls thereby leading to a better understanding of public opinion. While there has bee…
Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering
Weihe Zhai, Arkaitz Zubiaga
The fusion of language models (LMs) and knowledge graphs (KGs) is widely used in commonsense question answering, but generating faithful explanations remains challenging. Current m…
Some Observations on Fact-Checking Work with Implications for Computational Support
Rob Procter, Miguel Arana-Catania, Yulan He +4
Social media and user-generated content (UGC) have become increasingly important features of journalistic work in a number of different ways. However, the growth of misinformation…
PANACEA: An Automated Misinformation Detection System on COVID-19
Runcong Zhao, Miguel Arana-Catania, Lixing Zhu +6
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-check…
Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes
Xiaoyu Guo, Jing Ma, Arkaitz Zubiaga
Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enablin…
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis
Xiaoyu Guo, Jing Ma, Arkaitz Zubiaga
This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal fusion method, Squeeze-and-…
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection
Wenjie Yin, Vibhor Agarwal, Aiqi Jiang +2
Supervised approaches generally rely on majority-based labels. However, it is hard to achieve high agreement among annotators in subjective tasks such as hate speech detection. Exi…
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings
Parisa Jamadi Khiabani, Arkaitz Zubiaga
Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classificat…