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        <title>Dreaddit: A Reddit Dataset for Stress Analysis in Social Media</title>
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        <namePart type="given">Elsbeth</namePart>
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            <title>Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)</title>
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        <name type="personal">
            <namePart type="given">Eben</namePart>
            <namePart type="family">Holderness</namePart>
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        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Jimeno Yepes</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Alberto</namePart>
            <namePart type="family">Lavelli</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Anne-Lyse</namePart>
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        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Pustejovsky</namePart>
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    <abstract>Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.</abstract>
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    <identifier type="doi">10.18653/v1/D19-6213</identifier>
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%0 Conference Proceedings
%T Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
%A Turcan, Elsbeth
%A McKeown, Kathy
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F turcan-mckeown-2019-dreaddit
%X Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.
%R 10.18653/v1/D19-6213
%U https://aclanthology.org/D19-6213
%U https://doi.org/10.18653/v1/D19-6213
%P 97-107

Markdown (Informal)

[Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://aclanthology.org/D19-6213) (Turcan & McKeown, Louhi 2019)

• Dreaddit: A Reddit Dataset for Stress Analysis in Social Media (Turcan & McKeown, Louhi 2019)

ACL

• Elsbeth Turcan and Kathy McKeown. 2019. Dreaddit: A Reddit Dataset for Stress Analysis in Social Media. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 97–107, Hong Kong. Association for Computational Linguistics.