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    <titleInfo>
        <title>Rumour Detection in the Wild: A Browser Extension for Twitter</title>
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    <name type="personal">
        <namePart type="given">Andrej</namePart>
        <namePart type="family">Jovanovic</namePart>
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        <namePart type="given">Björn</namePart>
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            <title>Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)</title>
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        <name type="personal">
            <namePart type="given">Liling</namePart>
            <namePart type="family">Tan</namePart>
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        <name type="personal">
            <namePart type="given">Dmitrijs</namePart>
            <namePart type="family">Milajevs</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Geeticka</namePart>
            <namePart type="family">Chauhan</namePart>
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        <name type="personal">
            <namePart type="given">Jeremy</namePart>
            <namePart type="family">Gwinnup</namePart>
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        <name type="personal">
            <namePart type="given">Elijah</namePart>
            <namePart type="family">Rippeth</namePart>
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    <abstract>Rumour detection, particularly on social media, has gained popularity in recent years. The machine learning community has made significant contributions in investigating automatic methods to detect rumours on such platforms. However, these state-of-the-art (SoTA) models are often deployed by social media companies; ordinary end-users cannot leverage the solutions in the literature for their own rumour detection. To address this issue, we put forward a novel browser extension that allows these users to perform rumour detection on Twitter. Particularly, we leverage the performance from SoTA architectures, which has not been done previously. Initial results from a user study confirm that this browser extension provides benefit. Additionally, we examine the performance of our browser extension’s rumour detection model in a simulated deployment environment. Our results show that additional infrastructure for the browser extension is required to ensure its usability when deployed as a live service for Twitter users at scale.</abstract>
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    <identifier type="doi">10.18653/v1/2023.nlposs-1.15</identifier>
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            <start>130</start>
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%0 Conference Proceedings
%T Rumour Detection in the Wild: A Browser Extension for Twitter
%A Jovanovic, Andrej
%A Ross, Björn
%Y Tan, Liling
%Y Milajevs, Dmitrijs
%Y Chauhan, Geeticka
%Y Gwinnup, Jeremy
%Y Rippeth, Elijah
%S Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jovanovic-ross-2023-rumour
%X Rumour detection, particularly on social media, has gained popularity in recent years. The machine learning community has made significant contributions in investigating automatic methods to detect rumours on such platforms. However, these state-of-the-art (SoTA) models are often deployed by social media companies; ordinary end-users cannot leverage the solutions in the literature for their own rumour detection. To address this issue, we put forward a novel browser extension that allows these users to perform rumour detection on Twitter. Particularly, we leverage the performance from SoTA architectures, which has not been done previously. Initial results from a user study confirm that this browser extension provides benefit. Additionally, we examine the performance of our browser extension’s rumour detection model in a simulated deployment environment. Our results show that additional infrastructure for the browser extension is required to ensure its usability when deployed as a live service for Twitter users at scale.
%R 10.18653/v1/2023.nlposs-1.15
%U https://aclanthology.org/2023.nlposs-1.15
%U https://doi.org/10.18653/v1/2023.nlposs-1.15
%P 130-140

Markdown (Informal)

[Rumour Detection in the Wild: A Browser Extension for Twitter](https://aclanthology.org/2023.nlposs-1.15) (Jovanovic & Ross, NLPOSS-WS 2023)

• Rumour Detection in the Wild: A Browser Extension for Twitter (Jovanovic & Ross, NLPOSS-WS 2023)

ACL

• Andrej Jovanovic and Björn Ross. 2023. Rumour Detection in the Wild: A Browser Extension for Twitter. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 130–140, Singapore. Association for Computational Linguistics.