<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sutton-etal-2023-read">
    <titleInfo>
        <title>You Are What You Read: Inferring Personality From Consumed Textual Content</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Adam</namePart>
        <namePart type="family">Sutton</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Almog</namePart>
        <namePart type="family">Simchon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Matthew</namePart>
        <namePart type="family">Edwards</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stephan</namePart>
        <namePart type="family">Lewandowsky</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, &amp; Social Media Analysis</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jeremy</namePart>
            <namePart type="family">Barnes</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Orphée</namePart>
            <namePart type="family">De Clercq</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roman</namePart>
            <namePart type="family">Klinger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content (N = 913) or news content (N = 213). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of r=.06 between the model’s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as r=.34 when predicting personality from the text being read.</abstract>
    <identifier type="citekey">sutton-etal-2023-read</identifier>
    <identifier type="doi">10.18653/v1/2023.wassa-1.4</identifier>
    <location>
        <url>https://aclanthology.org/2023.wassa-1.4</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>28</start>
            <end>38</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T You Are What You Read: Inferring Personality From Consumed Textual Content
%A Sutton, Adam
%A Simchon, Almog
%A Edwards, Matthew
%A Lewandowsky, Stephan
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sutton-etal-2023-read
%X In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content (N = 913) or news content (N = 213). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of r=.06 between the model’s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as r=.34 when predicting personality from the text being read.
%R 10.18653/v1/2023.wassa-1.4
%U https://aclanthology.org/2023.wassa-1.4
%U https://doi.org/10.18653/v1/2023.wassa-1.4
%P 28-38

Markdown (Informal)

[You Are What You Read: Inferring Personality From Consumed Textual Content](https://aclanthology.org/2023.wassa-1.4) (Sutton et al., WASSA 2023)

• You Are What You Read: Inferring Personality From Consumed Textual Content (Sutton et al., WASSA 2023)

ACL

• Adam Sutton, Almog Simchon, Matthew Edwards, and Stephan Lewandowsky. 2023. You Are What You Read: Inferring Personality From Consumed Textual Content. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 28–38, Toronto, Canada. Association for Computational Linguistics.