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        <title>Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis</title>
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        <namePart type="given">Twin</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Kalina</namePart>
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        <name type="personal">
            <namePart type="given">Sebastian</namePart>
            <namePart type="family">Padó</namePart>
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        <name type="personal">
            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</namePart>
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    <abstract>Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.</abstract>
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    <identifier type="doi">10.18653/v1/D19-3020</identifier>
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%0 Conference Proceedings
%T Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis
%A Karmakharm, Twin
%A Aletras, Nikolaos
%A Bontcheva, Kalina
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F karmakharm-etal-2019-journalist
%X Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.
%R 10.18653/v1/D19-3020
%U https://aclanthology.org/D19-3020
%U https://doi.org/10.18653/v1/D19-3020
%P 115-120

Markdown (Informal)

[Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis](https://aclanthology.org/D19-3020) (Karmakharm et al., EMNLP-IJCNLP 2019)

• Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis (Karmakharm et al., EMNLP-IJCNLP 2019)

ACL

• Twin Karmakharm, Nikolaos Aletras, and Kalina Bontcheva. 2019. Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 115–120, Hong Kong, China. Association for Computational Linguistics.