# Identification of Biased Terms in News Articles by Comparison of Outlet-Specific Word Embeddings

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/identification-of-biased-terms-in-news-articles-by-comparison-of-outlet-specific/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Timo Spinde,Lada Rudnitckaia,Felix Hamborg,Béla Gipp |
| Citations | 21 |
| DOI | 10.1007/978-3-030-71305-8_17 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2112.07384 |
| OpenAlex ID | https://openalex.org/W3138270512 |
| Type | conference-paper |
| Year | 2021 |

## Paper authors

- [Timo Spinde](https://scholariq.org/researchers/timo-spinde/)

## Paper journal

- [Lecture notes in computer science](https://scholariq.org/journals/lecture-notes-in-computer-science/)

## Paper primary topic

- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)

## Paper topics

- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)
- [Natural Language Processing Techniques](https://scholariq.org/topics/natural-language-processing-techniques/)
- [Advanced Text Analysis Techniques](https://scholariq.org/topics/advanced-text-analysis-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.