4,674 research outputs found

    PANACEA: An Automated Misinformation Detection System on COVID-19

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    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-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available

    Context-Aware Message-Level Rumour Detection with Weak Supervision

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    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

    Classifying Tweet Level Judgements of Rumours in Social Media

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    Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We formulate the problem of classifying tweet level judgements of rumours as a supervised learning task. Both supervised and unsupervised domain adaptation are considered, in which tweets from a rumour are classified on the basis of other annotated rumours. We demonstrate how multi-task learning helps achieve good results on rumours from the 2011 England riots

    Crowdsourced Rumour Identification During Emergencies

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    When a significant event occurs, many social media users leverage platforms such as Twitter to track that event. Moreover, emergency response agencies are increasingly looking to social media as a source of real-time information about such events. However, false information and rumours are often spread during such events, which can influence public opinion and limit the usefulness of social media for emergency management. In this paper, we present an initial study into rumour identification during emergencies using crowdsourcing. In particular, through an analysis of three tweet datasets relating to emergency events from 2014, we propose a taxonomy of tweets relating to rumours. We then perform a crowdsourced labeling experiment to determine whether crowd assessors can identify rumour-related tweets and where such labeling can fail. Our results show that overall, agreement over the tweet labels produced were high (0.7634 Fleiss Kappa), indicating that crowd-based rumour labeling is possible. However, not all tweets are of equal difficulty to assess. Indeed, we show that tweets containing disputed/controversial information tend to be some of the most difficult to identify

    PANACEA: an automated misinformation detection system on COVID-19

    Get PDF
    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-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-ofthe-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available

    Statistical inference framework for source detection of contagion processes on arbitrary network structures

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    In this paper we introduce a statistical inference framework for estimating the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on a maximum likelihood estimation of a partial epidemic realization and involves large scale simulation of contagion spreading processes from the set of potential source locations. We present a number of different likelihood estimators that are used to determine the conditional probabilities associated to observing partial epidemic realization with particular source location candidates. This statistical inference framework is also applicable for arbitrary compartment contagion spreading processes on networks. We compare estimation accuracy of these approaches in a number of computational experiments performed with the SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS (ignorant-spreading-stifler) contagion spreading models on synthetic and real-world complex networks

    Online Misinformation: Challenges and Future Directions

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    Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view.We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications
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