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    <titleInfo>
        <title>Classification of Censored Tweets in Chinese Language using XLNet</title>
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        <namePart type="given">Shaikh</namePart>
        <namePart type="given">Sahil</namePart>
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            <title>Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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            <namePart type="given">Giovanni</namePart>
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            <namePart type="given">Chris</namePart>
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    <abstract>In the growth of today‘s world and advanced technology, social media networks play a significant role in impacting human lives. Censorship is the overthrowing of speech, public transmission, or other details that play a vast role in social media. The content may be considered harmful, sensitive, or inconvenient. Authorities like institutes, governments, and other organizations conduct Censorship. This paper has implemented a model that helps classify censored and uncensored tweets as a binary classification. The paper describes submission to the Censorship shared task of the NLP4IF 2021 workshop. We used various transformer-based pre-trained models, and XLNet outputs a better accuracy among all. We fine-tuned the model for better performance and achieved a reasonable accuracy, and calculated other performance metrics.</abstract>
    <identifier type="citekey">ahmed-kumar-m-2021-classification</identifier>
    <identifier type="doi">10.18653/v1/2021.nlp4if-1.21</identifier>
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        <date>2021-06</date>
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%0 Conference Proceedings
%T Classification of Censored Tweets in Chinese Language using XLNet
%A Ahmed, Shaikh Sahil
%A Kumar M., Anand
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F ahmed-kumar-m-2021-classification
%X In the growth of today‘s world and advanced technology, social media networks play a significant role in impacting human lives. Censorship is the overthrowing of speech, public transmission, or other details that play a vast role in social media. The content may be considered harmful, sensitive, or inconvenient. Authorities like institutes, governments, and other organizations conduct Censorship. This paper has implemented a model that helps classify censored and uncensored tweets as a binary classification. The paper describes submission to the Censorship shared task of the NLP4IF 2021 workshop. We used various transformer-based pre-trained models, and XLNet outputs a better accuracy among all. We fine-tuned the model for better performance and achieved a reasonable accuracy, and calculated other performance metrics.
%R 10.18653/v1/2021.nlp4if-1.21
%U https://aclanthology.org/2021.nlp4if-1.21/
%U https://doi.org/10.18653/v1/2021.nlp4if-1.21
%P 136-139

Markdown (Informal)

[Classification of Censored Tweets in Chinese Language using XLNet](https://aclanthology.org/2021.nlp4if-1.21/) (Ahmed & Kumar M., NLP4IF 2021)

• Classification of Censored Tweets in Chinese Language using XLNet (Ahmed & Kumar M., NLP4IF 2021)

ACL

• Shaikh Sahil Ahmed and Anand Kumar M.. 2021. Classification of Censored Tweets in Chinese Language using XLNet. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 136–139, Online. Association for Computational Linguistics.