QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions

This paper describes the participation of the QMUL-SDS team for Task 1 of the CLEF 2020 CheckThat! shared task. The purpose of this task is to determine the check-worthiness of tweets about COVID-19 to identify and prioritise tweets that need fact-checking. The overarching aim is to further support ongoing efforts to protect the public from fake news and help people find reliable information. We describe and analyse the results of our submissions. We show that a CNN using COVID-Twitter-BERT (CT-BERT) enhanced with numeric expressions can effectively boost performance from baseline results. We also show results of training data augmentation with rumours on other topics. Our best system ranked fourth in the task with encouraging outcomes showing potential for improved results in the future

COVID-19 misinformation on Twitter: the role of deceptive support

2022 Summer.Includes bibliographical references.Social media platforms like Twitter are a major dissemination point for information and the COVID-19 pandemic is no exception. But not all of the information comes from reliable sources, which raises doubts about their validity. In social media posts, writers reference news articles to gain credibility by leveraging the trust readers have in reputable news outlets. However, there is not always a positive correlation between the cited article and the social media posting. Targeting the Twitter platform, this study presents a novel pipeline to determine whether a Tweet is indeed supported by the news article it refers to. The approach follows two general objectives: to develop a model capable of detecting Tweets containing claims that are worthy of fact-checking and then, to assess whether the claims made in a given Tweet are supported by the news article it cites. In the event that a Tweet is found to be trustworthy, we extract its claim via a sequence labeling approach. In doing so, we seek to reduce the noise and highlight the informative parts of a Tweet. Instead of detecting erroneous and invalid information by analyzing the propagation patterns or ensuing examination of Tweets against already proven statements, this study aims to identify reliable support (or lack thereof) before misinformation spreads. Our research reveals that 14.5% of the Tweets are not factual and therefore not worth checking. An effective filter like this is especially useful when looking at a platform such as Twitter, where hundreds of thousands of posts are created every day. Further, our analysis indicates that among the Tweets which refer to a news article as evidence of a factual claim, at least 1% of those Tweets are not substantiated by the article, and therefore mislead the reader

EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets

Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they're observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (disaster relief organizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from large volumes of data. In this paper, we present our submission for WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. Our most successful model is an ensemble of transformers including RoBERTa, XLNet, and BERTweet trained in a semi-supervised experimental setting. The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard), and shows significant gains in performance compared to a baseline system using fasttext embeddings.Comment: 5 pages + 1 Appendix draft (after review

Coping with low data availability for social media crisis message categorisation

During crisis situations, social media allows people to quickly share information, including messages requesting help. This can be valuable to emergency responders, who need to categorise and prioritise these messages based on the type of assistance being requested. However, the high volume of messages makes it difficult to filter and prioritise them without the use of computational techniques. Fully supervised filtering techniques for crisis message categorisation typically require a large amount of annotated training data, but this can be difficult to obtain during an ongoing crisis and is expensive in terms of time and labour to create. This thesis focuses on addressing the challenge of low data availability when categorising crisis messages for emergency response. It first presents domain adaptation as a solution for this problem, which involves learning a categorisation model from annotated data from past crisis events (source domain) and adapting it to categorise messages from an ongoing crisis event (target domain). In many-to-many adaptation, where the model is trained on multiple past events and adapted to multiple ongoing events, a multi-task learning approach is proposed using pre-trained language models. This approach outperforms baselines and an ensemble approach further improves performance..

Context-Aware Message-Level Rumour Detection with Weak Supervision

Social media has become the main source of all sorts of information beyond a communication medium. Its intrinsic nature can allow a continuous and massive flow of misinformation to make a severe impact worldwide. In particular, rumours emerge unexpectedly and spread quickly. It is challenging to track down their origins and stop their propagation. One of the most ideal solutions to this is to identify rumour-mongering messages as early as possible, which is commonly referred to as "Early Rumour Detection (ERD)". This dissertation focuses on researching ERD on social media by exploiting weak supervision and contextual information. Weak supervision is a branch of ML where noisy and less precise sources (e.g. data patterns) are leveraged to learn limited high-quality labelled data (Ratner et al., 2017). This is intended to reduce the cost and increase the efficiency of the hand-labelling of large-scale data. This thesis aims to study whether identifying rumours before they go viral is possible and develop an architecture for ERD at individual post level. To this end, it first explores major bottlenecks of current ERD. It also uncovers a research gap between system design and its applications in the real world, which have received less attention from the research community of ERD. One bottleneck is limited labelled data. Weakly supervised methods to augment limited labelled training data for ERD are introduced. The other bottleneck is enormous amounts of noisy data. A framework unifying burst detection based on temporal signals and burst summarisation is investigated to identify potential rumours (i.e. input to rumour detection models) by filtering out uninformative messages. Finally, a novel method which jointly learns rumour sources and their contexts (i.e. conversational threads) for ERD is proposed. An extensive evaluation setting for ERD systems is also introduced