Browsing by Author "Procter, Rob"

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    Embedding digital participatory budgeting within local government: motivations, strategies and barriers faced
    (Association for Computing Machinery (ACM), 2022-11-18) Davies, Jonathan; Arana-Catania, Miguel; Procter, Rob
    The challenging task of embedding innovative participatory processes and technologies within local government often falls upon local council officers. Using qualitative data collection and analysis, we investigate the ongoing work of Scottish local councils seeking to run the process of participatory budgeting (PB) within their institution, the use of digital platforms to support this and the challenges faced. In doing so this paper draws on empirical material to support the growing discussion on the dynamics or forces behind embedding. Our analysis shows that formal agreement alone does not make the process a certainty. Local council officers must work as mediators in the transitional space between representative structures and new, innovative ways of working, unsettling the entrenched power dynamics. To do so they must be well trained and well resourced, including the ability to use digital platforms effectively as part of the process. This provides the necessary, accessible, transparent and deliberative space for participation.
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    PANACEA: an automated misinformation detection system on COVID-19
    (Association for Computational Linguistics, 2023-05-04) Zhao, Runcong; Arana-Catania, Miguel; Zhu, Lixing; Kochkina, Elena; Gui, Lin; Zubiaga, Arkaitz; Procter, Rob; Liakata, Maria; He, Yulan
    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.