SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience Features Article Swipe
Gangeshwar Krishnamurthy
,
Raj Kumar Gupta
,
Yinping Yang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2008.13012
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2008.13012
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).
Related Topics
Concepts
Salience (neuroscience)
Anger
Sadness
Computer science
Sentence
Valence (chemistry)
Psychology
Correlation
Artificial intelligence
SemEval
Classifier (UML)
Social psychology
Natural language processing
Cognitive psychology
Task (project management)
Mathematics
Economics
Physics
Management
Quantum mechanics
Geometry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2008.13012
- https://arxiv.org/pdf/2008.13012
- OA Status
- green
- Cited By
- 1
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3081917022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3081917022Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2008.13012Digital Object Identifier
- Title
-
SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience FeaturesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-29Full publication date if available
- Authors
-
Gangeshwar Krishnamurthy, Raj Kumar Gupta, Yinping YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2008.13012Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2008.13012Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2008.13012Direct OA link when available
- Concepts
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Salience (neuroscience), Anger, Sadness, Computer science, Sentence, Valence (chemistry), Psychology, Correlation, Artificial intelligence, SemEval, Classifier (UML), Social psychology, Natural language processing, Cognitive psychology, Task (project management), Mathematics, Economics, Physics, Management, Quantum mechanics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2020-08-29 |
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| referenced_works | https://openalex.org/W2964121744, https://openalex.org/W2805744755, https://openalex.org/W2963177779, https://openalex.org/W2915177913, https://openalex.org/W2970487286, https://openalex.org/W2320182752, https://openalex.org/W3041412367, https://openalex.org/W2963341956, https://openalex.org/W2805939548, https://openalex.org/W3113763975, https://openalex.org/W2966487074, https://openalex.org/W3037000009, https://openalex.org/W747544350, https://openalex.org/W2095705004, https://openalex.org/W2047572973, https://openalex.org/W2759820691, https://openalex.org/W2137142689, https://openalex.org/W2978871345 |
| referenced_works_count | 18 |
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