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        <title>Detecting Hashtag Hijacking for Hashtag Activism</title>
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        <namePart type="given">Pooneh</namePart>
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            <title>Proceedings of the 1st Workshop on NLP for Positive Impact</title>
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            <namePart type="given">Anjalie</namePart>
            <namePart type="family">Field</namePart>
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            <namePart type="given">Maarten</namePart>
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    <abstract>Social media has changed the way we engage in social activities. On Twitter, users can participate in social movements using hashtags such as #MeToo; this is known as hashtag activism. However, while these hashtags can help reshape social norms, they can also be used maliciously by spammers or troll communities for other purposes, such as signal boosting unrelated content, making a dent in a movement, or sharing hate speech. We present a Tweet-level hashtag hijacking detection framework focusing on hashtag activism. Our weakly-supervised framework uses bootstrapping to update itself as new Tweets are posted. Our experiments show that the system adapts to new topics in a social movement, as well as new hijacking strategies, maintaining strong performance over time.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4posimpact-1.9</identifier>
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%0 Conference Proceedings
%T Detecting Hashtag Hijacking for Hashtag Activism
%A Mousavi, Pooneh
%A Ouyang, Jessica
%Y Field, Anjalie
%Y Prabhumoye, Shrimai
%Y Sap, Maarten
%Y Jin, Zhijing
%Y Zhao, Jieyu
%Y Brockett, Chris
%S Proceedings of the 1st Workshop on NLP for Positive Impact
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F mousavi-ouyang-2021-detecting
%X Social media has changed the way we engage in social activities. On Twitter, users can participate in social movements using hashtags such as #MeToo; this is known as hashtag activism. However, while these hashtags can help reshape social norms, they can also be used maliciously by spammers or troll communities for other purposes, such as signal boosting unrelated content, making a dent in a movement, or sharing hate speech. We present a Tweet-level hashtag hijacking detection framework focusing on hashtag activism. Our weakly-supervised framework uses bootstrapping to update itself as new Tweets are posted. Our experiments show that the system adapts to new topics in a social movement, as well as new hijacking strategies, maintaining strong performance over time.
%R 10.18653/v1/2021.nlp4posimpact-1.9
%U https://aclanthology.org/2021.nlp4posimpact-1.9
%U https://doi.org/10.18653/v1/2021.nlp4posimpact-1.9
%P 82-92

Markdown (Informal)

[Detecting Hashtag Hijacking for Hashtag Activism](https://aclanthology.org/2021.nlp4posimpact-1.9) (Mousavi & Ouyang, NLP4PI 2021)

• Detecting Hashtag Hijacking for Hashtag Activism (Mousavi & Ouyang, NLP4PI 2021)

ACL

• Pooneh Mousavi and Jessica Ouyang. 2021. Detecting Hashtag Hijacking for Hashtag Activism. In Proceedings of the 1st Workshop on NLP for Positive Impact, pages 82–92, Online. Association for Computational Linguistics.