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    <titleInfo>
        <title>Not All Comments Are Equal: Insights into Comment Moderation from a Topic-Aware Model</title>
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    <name type="personal">
        <namePart type="given">Elaine</namePart>
        <namePart type="family">Zosa</namePart>
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    <name type="personal">
        <namePart type="given">Ravi</namePart>
        <namePart type="family">Shekhar</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Mladen</namePart>
        <namePart type="family">Karan</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Matthew</namePart>
        <namePart type="family">Purver</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision. Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model’s outputs.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.185</url>
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        <date>2021-09</date>
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            <start>1652</start>
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%0 Conference Proceedings
%T Not All Comments Are Equal: Insights into Comment Moderation from a Topic-Aware Model
%A Zosa, Elaine
%A Shekhar, Ravi
%A Karan, Mladen
%A Purver, Matthew
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F zosa-etal-2021-comments
%X Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision. Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model’s outputs.
%U https://aclanthology.org/2021.ranlp-1.185
%P 1652-1662

Markdown (Informal)

[Not All Comments Are Equal: Insights into Comment Moderation from a Topic-Aware Model](https://aclanthology.org/2021.ranlp-1.185) (Zosa et al., RANLP 2021)

• Not All Comments Are Equal: Insights into Comment Moderation from a Topic-Aware Model (Zosa et al., RANLP 2021)

ACL

• Elaine Zosa, Ravi Shekhar, Mladen Karan, and Matthew Purver. 2021. Not All Comments Are Equal: Insights into Comment Moderation from a Topic-Aware Model. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1652–1662, Held Online. INCOMA Ltd..