@inproceedings{teng-ohman-2025-using,
title = "Using Multimodal Models for Informative Classification of Ambiguous Tweets in Crisis Response",
author = {Teng, Sumiko and
{\"O}hman, Emily},
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and
{\"O}hman, Emily and
Bizzoni, Yuri and
Miyagawa, So and
Alnajjar, Khalid},
booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
month = may,
year = "2025",
address = "Albuquerque, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4dh-1.23/",
doi = "10.18653/v1/2025.nlp4dh-1.23",
pages = "265--271",
ISBN = "979-8-89176-234-3",
abstract = "Social media platforms like X provide real-time information during crises but often include noisy, ambiguous data, complicating analysis. This study examines the effectiveness of multimodal models, particularly a cross-attention-based approach, in classifying tweets about the California wildfires as ``informative'' or ``uninformative,'' leveraging both text and image modalities. Using a dataset containing both ambiguous and unambiguous tweets, models were evaluated for their ability to handle real-world noisy data. Results show that the multimodal model outperforms unimodal counterparts, especially for ambiguous tweets, demonstrating its resilience and ability to integrate complementary modalities. These findings highlight the potential of multimodal approaches to enhance humanitarian response efforts by reducing information overload."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="teng-ohman-2025-using">
<titleInfo>
<title>Using Multimodal Models for Informative Classification of Ambiguous Tweets in Crisis Response</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sumiko</namePart>
<namePart type="family">Teng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emily</namePart>
<namePart type="family">Öhman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mika</namePart>
<namePart type="family">Hämäläinen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emily</namePart>
<namePart type="family">Öhman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuri</namePart>
<namePart type="family">Bizzoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">So</namePart>
<namePart type="family">Miyagawa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khalid</namePart>
<namePart type="family">Alnajjar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-234-3</identifier>
</relatedItem>
<abstract>Social media platforms like X provide real-time information during crises but often include noisy, ambiguous data, complicating analysis. This study examines the effectiveness of multimodal models, particularly a cross-attention-based approach, in classifying tweets about the California wildfires as “informative” or “uninformative,” leveraging both text and image modalities. Using a dataset containing both ambiguous and unambiguous tweets, models were evaluated for their ability to handle real-world noisy data. Results show that the multimodal model outperforms unimodal counterparts, especially for ambiguous tweets, demonstrating its resilience and ability to integrate complementary modalities. These findings highlight the potential of multimodal approaches to enhance humanitarian response efforts by reducing information overload.</abstract>
<identifier type="citekey">teng-ohman-2025-using</identifier>
<identifier type="doi">10.18653/v1/2025.nlp4dh-1.23</identifier>
<location>
<url>https://aclanthology.org/2025.nlp4dh-1.23/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>265</start>
<end>271</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Using Multimodal Models for Informative Classification of Ambiguous Tweets in Crisis Response
%A Teng, Sumiko
%A Öhman, Emily
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Bizzoni, Yuri
%Y Miyagawa, So
%Y Alnajjar, Khalid
%S Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-234-3
%F teng-ohman-2025-using
%X Social media platforms like X provide real-time information during crises but often include noisy, ambiguous data, complicating analysis. This study examines the effectiveness of multimodal models, particularly a cross-attention-based approach, in classifying tweets about the California wildfires as “informative” or “uninformative,” leveraging both text and image modalities. Using a dataset containing both ambiguous and unambiguous tweets, models were evaluated for their ability to handle real-world noisy data. Results show that the multimodal model outperforms unimodal counterparts, especially for ambiguous tweets, demonstrating its resilience and ability to integrate complementary modalities. These findings highlight the potential of multimodal approaches to enhance humanitarian response efforts by reducing information overload.
%R 10.18653/v1/2025.nlp4dh-1.23
%U https://aclanthology.org/2025.nlp4dh-1.23/
%U https://doi.org/10.18653/v1/2025.nlp4dh-1.23
%P 265-271
Markdown (Informal)
[Using Multimodal Models for Informative Classification of Ambiguous Tweets in Crisis Response](https://aclanthology.org/2025.nlp4dh-1.23/) (Teng & Öhman, NLP4DH 2025)
ACL