@inproceedings{flaccavento-etal-2025-automated,
title = "Automated Detection of Tropes In Short Texts",
author = "Flaccavento, Alessandra and
Peskine, Youri and
Papotti, Paolo and
Torlone, Riccardo and
Troncy, Raphael",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.396/",
pages = "5936--5951",
abstract = "Tropes {---} recurring narrative elements like the {\textquotedblleft}smoking gun{\textquotedblright} or the {\textquotedblleft}veil of secrecy{\textquotedblright} {---} are often used in movies to convey familiar patterns. However, they also play a significant role in online communication about societal issues, where they can oversimplify complex matters and deteriorate public discourse. Recognizing these tropes can offer insights into the emotional manipulation and potential bias present in online discussions. This paper addresses the challenge of automatically detecting tropes in social media posts. We define the task, distinguish it from previous work, and create a ground-truth dataset of social media posts related to vaccines and immigration, manually labeled with tropes. Using this dataset, we develop a supervised machine learning technique for multi-label classification, fine-tune a model, and demonstrate its effectiveness experimentally. Our results show that tropes are common across domains and that fine-tuned models can detect them with high accuracy."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="flaccavento-etal-2025-automated">
<titleInfo>
<title>Automated Detection of Tropes In Short Texts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alessandra</namePart>
<namePart type="family">Flaccavento</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Youri</namePart>
<namePart type="family">Peskine</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paolo</namePart>
<namePart type="family">Papotti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Riccardo</namePart>
<namePart type="family">Torlone</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Raphael</namePart>
<namePart type="family">Troncy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 31st International Conference on Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Owen</namePart>
<namePart type="family">Rambow</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leo</namePart>
<namePart type="family">Wanner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="given">Di</namePart>
<namePart type="family">Eugenio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Schockaert</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Tropes — recurring narrative elements like the “smoking gun” or the “veil of secrecy” — are often used in movies to convey familiar patterns. However, they also play a significant role in online communication about societal issues, where they can oversimplify complex matters and deteriorate public discourse. Recognizing these tropes can offer insights into the emotional manipulation and potential bias present in online discussions. This paper addresses the challenge of automatically detecting tropes in social media posts. We define the task, distinguish it from previous work, and create a ground-truth dataset of social media posts related to vaccines and immigration, manually labeled with tropes. Using this dataset, we develop a supervised machine learning technique for multi-label classification, fine-tune a model, and demonstrate its effectiveness experimentally. Our results show that tropes are common across domains and that fine-tuned models can detect them with high accuracy.</abstract>
<identifier type="citekey">flaccavento-etal-2025-automated</identifier>
<location>
<url>https://aclanthology.org/2025.coling-main.396/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>5936</start>
<end>5951</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Automated Detection of Tropes In Short Texts
%A Flaccavento, Alessandra
%A Peskine, Youri
%A Papotti, Paolo
%A Torlone, Riccardo
%A Troncy, Raphael
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F flaccavento-etal-2025-automated
%X Tropes — recurring narrative elements like the “smoking gun” or the “veil of secrecy” — are often used in movies to convey familiar patterns. However, they also play a significant role in online communication about societal issues, where they can oversimplify complex matters and deteriorate public discourse. Recognizing these tropes can offer insights into the emotional manipulation and potential bias present in online discussions. This paper addresses the challenge of automatically detecting tropes in social media posts. We define the task, distinguish it from previous work, and create a ground-truth dataset of social media posts related to vaccines and immigration, manually labeled with tropes. Using this dataset, we develop a supervised machine learning technique for multi-label classification, fine-tune a model, and demonstrate its effectiveness experimentally. Our results show that tropes are common across domains and that fine-tuned models can detect them with high accuracy.
%U https://aclanthology.org/2025.coling-main.396/
%P 5936-5951
Markdown (Informal)
[Automated Detection of Tropes In Short Texts](https://aclanthology.org/2025.coling-main.396/) (Flaccavento et al., COLING 2025)
ACL
- Alessandra Flaccavento, Youri Peskine, Paolo Papotti, Riccardo Torlone, and Raphael Troncy. 2025. Automated Detection of Tropes In Short Texts. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5936–5951, Abu Dhabi, UAE. Association for Computational Linguistics.