<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="weber-etal-2026-says">
    <titleInfo>
        <title>Says Who? Argument Convincingness and Reader Stance Are Correlated with Perceived Author Personality</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sabine</namePart>
        <namePart type="family">Weber</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lynn</namePart>
        <namePart type="family">Greschner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Roman</namePart>
        <namePart type="family">Klinger</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)</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">Valentin</namePart>
            <namePart type="family">Barriere</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>
        <name type="personal">
            <namePart type="given">Célia</namePart>
            <namePart type="family">Nouri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debora</namePart>
            <namePart type="family">Nozza</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pranaydeep</namePart>
            <namePart type="family">Singh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Rabat, Morocco</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-378-4</identifier>
    </relatedItem>
    <abstract>Alongside its literal meaning, text also carries implicit social signals: information that is used by the reader to assign the author of the text a specific identity or make assumptions about the author’s character. The reader creates a mental image of the author which influences the interpretation of the presented information. This is especially relevant for argumentative text, where the credibility of the information might depend on who provides it. We therefore focus on the question: How do readers of an argument imagine its author? Using the ContArgA corpus, we study arguments annotated for convincingness and perceived author properties (level of education and Big Five personality traits). We find that annotators perceive an author to be similar to themselves when they agree with the stance of the argument. We also find that the envisioned personality traits and education level of the author are statistically significantly correlated with the argument’s convincingness. We conduct experiments with four generative LLMs and a RoBERTa-based regression model showing that LLMs do not replicate the annotators judgments. Argument convincingness can however provide a useful signal for modeling perceived author personality when it is explicitly used during training.</abstract>
    <identifier type="citekey">weber-etal-2026-says</identifier>
    <location>
        <url>https://aclanthology.org/2026.wassa-1.20/</url>
    </location>
    <part>
        <date>2026-03</date>
        <extent unit="page">
            <start>265</start>
            <end>277</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Says Who? Argument Convincingness and Reader Stance Are Correlated with Perceived Author Personality
%A Weber, Sabine
%A Greschner, Lynn
%A Klinger, Roman
%Y Barnes, Jeremy
%Y Barriere, Valentin
%Y De Clercq, Orphée
%Y Klinger, Roman
%Y Nouri, Célia
%Y Nozza, Debora
%Y Singh, Pranaydeep
%S The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-378-4
%F weber-etal-2026-says
%X Alongside its literal meaning, text also carries implicit social signals: information that is used by the reader to assign the author of the text a specific identity or make assumptions about the author’s character. The reader creates a mental image of the author which influences the interpretation of the presented information. This is especially relevant for argumentative text, where the credibility of the information might depend on who provides it. We therefore focus on the question: How do readers of an argument imagine its author? Using the ContArgA corpus, we study arguments annotated for convincingness and perceived author properties (level of education and Big Five personality traits). We find that annotators perceive an author to be similar to themselves when they agree with the stance of the argument. We also find that the envisioned personality traits and education level of the author are statistically significantly correlated with the argument’s convincingness. We conduct experiments with four generative LLMs and a RoBERTa-based regression model showing that LLMs do not replicate the annotators judgments. Argument convincingness can however provide a useful signal for modeling perceived author personality when it is explicitly used during training.
%U https://aclanthology.org/2026.wassa-1.20/
%P 265-277

Markdown (Informal)

[Says Who? Argument Convincingness and Reader Stance Are Correlated with Perceived Author Personality](https://aclanthology.org/2026.wassa-1.20/) (Weber et al., WASSA 2026)

• Says Who? Argument Convincingness and Reader Stance Are Correlated with Perceived Author Personality (Weber et al., WASSA 2026)

ACL

• Sabine Weber, Lynn Greschner, and Roman Klinger. 2026. Says Who? Argument Convincingness and Reader Stance Are Correlated with Perceived Author Personality. In The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026), pages 265–277, Rabat, Morocco. Association for Computational Linguistics.