@inproceedings{krishnamurthy-etal-2020-soccogcom,
title = "{S}oc{C}og{C}om at {S}em{E}val-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features",
author = "Krishnamurthy, Gangeshwar and
Gupta, Raj Kumar and
Yang, Yinping",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.235",
doi = "10.18653/v1/2020.semeval-1.235",
pages = "1793--1801",
abstract = "This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the {``}loaded language{''} and {``}slogan{''} techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, {``}flag waving{''} and {``}appeal to fear-prejudice{''} have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting {``}loaded language{''} (F1 = 0.772), {``}name calling and labeling{''} (F1 = 0.673), {``}doubt{''} (F1 = 0.604) and {``}flag waving{''} (F1 = 0.543).",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="krishnamurthy-etal-2020-soccogcom">
<titleInfo>
<title>SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features</title>
</titleInfo>
<name type="personal">
<namePart type="given">Gangeshwar</namePart>
<namePart type="family">Krishnamurthy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Raj</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Gupta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yinping</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">May</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>International Committee for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona (online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the “loaded language” and “slogan” techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, “flag waving” and “appeal to fear-prejudice” have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting “loaded language” (F1 = 0.772), “name calling and labeling” (F1 = 0.673), “doubt” (F1 = 0.604) and “flag waving” (F1 = 0.543).</abstract>
<identifier type="citekey">krishnamurthy-etal-2020-soccogcom</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.235</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.235</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>1793</start>
<end>1801</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features
%A Krishnamurthy, Gangeshwar
%A Gupta, Raj Kumar
%A Yang, Yinping
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F krishnamurthy-etal-2020-soccogcom
%X This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the “loaded language” and “slogan” techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, “flag waving” and “appeal to fear-prejudice” have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting “loaded language” (F1 = 0.772), “name calling and labeling” (F1 = 0.673), “doubt” (F1 = 0.604) and “flag waving” (F1 = 0.543).
%R 10.18653/v1/2020.semeval-1.235
%U https://aclanthology.org/2020.semeval-1.235
%U https://doi.org/10.18653/v1/2020.semeval-1.235
%P 1793-1801
Markdown (Informal)
[SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features](https://aclanthology.org/2020.semeval-1.235) (Krishnamurthy et al., SemEval 2020)
ACL