Details
| Original language | English |
|---|---|
| Title of host publication | Findings of the Association for Computational Linguistics |
| Subtitle of host publication | EMNLP 2020 |
| Pages | 4290-4300 |
| Number of pages | 11 |
| Publication status | Published - Nov 2020 |
| Externally published | Yes |
| Event | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020 |
Abstract
Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that feature-based and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their “bias predictiveness” is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness. In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model. On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary. Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computational Theory and Mathematics
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Findings of the Association for Computational Linguistics: EMNLP 2020. 2020. p. 4290-4300.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Detecting Media Bias in News Articles using Gaussian Bias Distributions
AU - Chen, Wei Fan
AU - Al-Khatib, Khalid
AU - Stein, Benno
AU - Wachsmuth, Henning
N1 - Funding Information: This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center “On-The-Fly Computing” (SFB 901/3) under the project number 160364472.
PY - 2020/11
Y1 - 2020/11
N2 - Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that feature-based and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their “bias predictiveness” is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness. In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model. On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary. Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.
AB - Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that feature-based and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their “bias predictiveness” is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness. In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model. On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary. Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.
UR - http://www.scopus.com/inward/record.url?scp=85115719536&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2010.10649
DO - 10.48550/arXiv.2010.10649
M3 - Conference contribution
AN - SCOPUS:85115719536
SP - 4290
EP - 4300
BT - Findings of the Association for Computational Linguistics
T2 - Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020
Y2 - 16 November 2020 through 20 November 2020
ER -