The task of retrieving already debunked narratives aims to detect stories that have already been fact-checked. The successful detection of claims that have already been debunked not only reduces the manual efforts of professional fact-checkers but can also contribute to slowing the spread of misinformation. Mainly due to the lack of readily available data, this is an understudied problem, particularly when considering the cross-lingual task, i.e. the retrieval of fact-checking articles in a language different from the language of the online post being checked. This paper fills this gap by (i) creating a novel dataset to enable research on cross-lingual retrieval of already debunked narratives, using tweets as queries to a database of fact-checking articles; (ii) presenting an extensive experiment to benchmark fine-tuned and off-the-shelf multilingual pre-trained Transformer models for this task; and (iii) proposing a novel multistage framework that divides this cross-lingual debunk retrieval task into refinement and re-ranking stages. Results show that the task of cross-lingual retrieval of already debunked narratives is challenging and off-the-shelf Transformer models fail to outperform a strong lexical-based baseline (BM25). Nevertheless, our multistage retrieval framework is robust, outperforming BM25 in most scenarios and enabling cross-domain and zero-shot learning, without significantly harming the model's performance.

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 challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. 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. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9\% based on average F1 measure.

The Coronavirus (COVID-19) pandemic has led to a rapidly growing `infodemic' online. Thus, the accurate retrieval of reliable relevant data from millions of documents about COVID-19 has become urgently needed for the general public as well as for other stakeholders. The COVID-19 Multilingual Information Access (MLIA) initiative is a joint effort to ameliorate exchange of COVID-19 related information by developing applications and services through research and community participation. In this work, we present a search system called Multistage BiCross Encoder, developed by team GATE for the MLIA task 2 Multilingual Semantic Search. Multistage BiCross-Encoder is a sequential three stage pipeline which uses the Okapi BM25 algorithm and a transformer based bi-encoder and cross-encoder to effectively rank the documents with respect to the query. The results of round 1 show that our models achieve state-of-the-art performance for all ranking metrics for both monolingual and bilingual runs.

The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide. To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research. This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results.