Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 19 |
| Fachzeitschrift | Journal of Intelligent Information Systems |
| Jahrgang | 58 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 15 Mai 2021 |
| Publikationsstatus | Veröffentlicht - Feb. 2022 |
Abstract
Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through classification models that do not consider the highly imbalanced class distribution. Therefore, they are particularly ineffective in detecting the minority classes (for instance, ‘disagree’), even though such instances are crucial for tasks such as fact-checking by providing evidence for detecting false claims. In this paper, we exploit the hierarchical nature of stance classes which allows us to propose a modular pipeline of cascading binary classifiers, enabling performance tuning on a per step and class basis. We implement our approach through a combination of neural and traditional classification models that highlight the misclassification costs of minority classes. Evaluation results demonstrate state-of-the-art performance of our approach and its ability to significantly improve the classification performance of the important ‘disagree’ class.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Hardware und Architektur
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Artificial intelligence
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in: Journal of Intelligent Information Systems, Jahrgang 58, Nr. 1, 02.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
AU - Roy, Arjun
AU - Fafalios, Pavlos
AU - Ekbal, Asif
AU - Zhu, Xiaofei
AU - Dietze, Stefan
PY - 2022/2
Y1 - 2022/2
N2 - Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through classification models that do not consider the highly imbalanced class distribution. Therefore, they are particularly ineffective in detecting the minority classes (for instance, ‘disagree’), even though such instances are crucial for tasks such as fact-checking by providing evidence for detecting false claims. In this paper, we exploit the hierarchical nature of stance classes which allows us to propose a modular pipeline of cascading binary classifiers, enabling performance tuning on a per step and class basis. We implement our approach through a combination of neural and traditional classification models that highlight the misclassification costs of minority classes. Evaluation results demonstrate state-of-the-art performance of our approach and its ability to significantly improve the classification performance of the important ‘disagree’ class.
AB - Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through classification models that do not consider the highly imbalanced class distribution. Therefore, they are particularly ineffective in detecting the minority classes (for instance, ‘disagree’), even though such instances are crucial for tasks such as fact-checking by providing evidence for detecting false claims. In this paper, we exploit the hierarchical nature of stance classes which allows us to propose a modular pipeline of cascading binary classifiers, enabling performance tuning on a per step and class basis. We implement our approach through a combination of neural and traditional classification models that highlight the misclassification costs of minority classes. Evaluation results demonstrate state-of-the-art performance of our approach and its ability to significantly improve the classification performance of the important ‘disagree’ class.
KW - Cascading classifiers
KW - Fact-checking
KW - Fake News
KW - Stance detection
UR - http://www.scopus.com/inward/record.url?scp=85106289190&partnerID=8YFLogxK
U2 - 10.1007/s10844-021-00642-z
DO - 10.1007/s10844-021-00642-z
M3 - Article
AN - SCOPUS:85106289190
VL - 58
JO - Journal of Intelligent Information Systems
JF - Journal of Intelligent Information Systems
SN - 0925-9902
IS - 1
ER -