<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cavalheiro-etal-2023-stance">
    <titleInfo>
        <title>Stance Prediction from Multimodal Social Media Data</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Lais</namePart>
        <namePart type="given">Carraro</namePart>
        <namePart type="given">Leme</namePart>
        <namePart type="family">Cavalheiro</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Matheus</namePart>
        <namePart type="given">Camasmie</namePart>
        <namePart type="family">Pavan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ivandré</namePart>
        <namePart type="family">Paraboni</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Stance prediction - the computational task of inferring attitudes towards a given target topic of interest - relies heavily on text data provided by social media or similar sources, but it may also benefit from non-text information such as demographics (e.g., users’ gender, age, etc.), network structure (e.g., friends, followers, etc.), interactions (e.g., mentions, replies, etc.) and other non-text properties (e.g., time information, etc.). However, so-called hybrid (or in some cases multimodal) approaches to stance prediction have only been developed for a small set of target languages, and often making use of count-based text models (e.g., bag-of-words) and time-honoured classification methods (e.g., support vector machines). As a means to further research in the field, in this work we introduce a number of text- and non-text models for stance prediction in the Portuguese language, which make use of more recent methods based on BERT and an ensemble architecture, and ask whether a BERT stance classifier may be enhanced with different kinds of network-related information.</abstract>
    <identifier type="citekey">cavalheiro-etal-2023-stance</identifier>
    <location>
        <url>https://aclanthology.org/2023.ranlp-1.27</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>242</start>
            <end>248</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Stance Prediction from Multimodal Social Media Data
%A Cavalheiro, Lais Carraro Leme
%A Pavan, Matheus Camasmie
%A Paraboni, Ivandré
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F cavalheiro-etal-2023-stance
%X Stance prediction - the computational task of inferring attitudes towards a given target topic of interest - relies heavily on text data provided by social media or similar sources, but it may also benefit from non-text information such as demographics (e.g., users’ gender, age, etc.), network structure (e.g., friends, followers, etc.), interactions (e.g., mentions, replies, etc.) and other non-text properties (e.g., time information, etc.). However, so-called hybrid (or in some cases multimodal) approaches to stance prediction have only been developed for a small set of target languages, and often making use of count-based text models (e.g., bag-of-words) and time-honoured classification methods (e.g., support vector machines). As a means to further research in the field, in this work we introduce a number of text- and non-text models for stance prediction in the Portuguese language, which make use of more recent methods based on BERT and an ensemble architecture, and ask whether a BERT stance classifier may be enhanced with different kinds of network-related information.
%U https://aclanthology.org/2023.ranlp-1.27
%P 242-248

Markdown (Informal)

[Stance Prediction from Multimodal Social Media Data](https://aclanthology.org/2023.ranlp-1.27) (Cavalheiro et al., RANLP 2023)

• Stance Prediction from Multimodal Social Media Data (Cavalheiro et al., RANLP 2023)

ACL

• Lais Carraro Leme Cavalheiro, Matheus Camasmie Pavan, and Ivandré Paraboni. 2023. Stance Prediction from Multimodal Social Media Data. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 242–248, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.