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    <titleInfo>
        <title>A Semantics-based Approach to Disclosure Classification in User-Generated Online Content</title>
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    <name type="personal">
        <namePart type="given">Chandan</namePart>
        <namePart type="family">Akiti</namePart>
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    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Squicciarini</namePart>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="family">Rajtmajer</namePart>
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        <dateIssued>2020-11</dateIssued>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
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            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social networks, as well as the power of modern AI to synthesize and gain insights from this data. This paper presents an approach to detect emotional and informational self-disclosure in natural language. We hypothesize that identifying frame semantics can meaningfully support this task. Specifically, we use Semantic Role Labeling to identify the lexical units and their semantic roles that signal self-disclosure. Experimental results on Reddit data show the performance gain of our method when compared to standard text classification methods based on BiLSTM, and BERT. In addition to improved performance, our approach provides insights into the drivers of disclosure behaviors.</abstract>
    <identifier type="citekey">akiti-etal-2020-semantics</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.312</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.312</url>
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    <part>
        <date>2020-11</date>
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            <start>3490</start>
            <end>3499</end>
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%0 Conference Proceedings
%T A Semantics-based Approach to Disclosure Classification in User-Generated Online Content
%A Akiti, Chandan
%A Squicciarini, Anna
%A Rajtmajer, Sarah
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F akiti-etal-2020-semantics
%X As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social networks, as well as the power of modern AI to synthesize and gain insights from this data. This paper presents an approach to detect emotional and informational self-disclosure in natural language. We hypothesize that identifying frame semantics can meaningfully support this task. Specifically, we use Semantic Role Labeling to identify the lexical units and their semantic roles that signal self-disclosure. Experimental results on Reddit data show the performance gain of our method when compared to standard text classification methods based on BiLSTM, and BERT. In addition to improved performance, our approach provides insights into the drivers of disclosure behaviors.
%R 10.18653/v1/2020.findings-emnlp.312
%U https://aclanthology.org/2020.findings-emnlp.312
%U https://doi.org/10.18653/v1/2020.findings-emnlp.312
%P 3490-3499

Markdown (Informal)

[A Semantics-based Approach to Disclosure Classification in User-Generated Online Content](https://aclanthology.org/2020.findings-emnlp.312) (Akiti et al., Findings 2020)

• A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (Akiti et al., Findings 2020)

ACL

• Chandan Akiti, Anna Squicciarini, and Sarah Rajtmajer. 2020. A Semantics-based Approach to Disclosure Classification in User-Generated Online Content. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3490–3499, Online. Association for Computational Linguistics.