Name
Abstract | ||
|---|---|---|
Media bias may often affect individuals' opinions on reported topics. Many existing methods that aim to identify such bias forms employ individual, specialized techniques and focus only on English texts. We propose to combine the state-of-the-art in order to further improve the performance in bias identification. Our prototype consists of three analysis components to identify media bias words in German news articles. We use an IDF-based component, a component utilizing a topic-dependent bias dictionary created using word embeddings, and an extensive dictionary of German emotional terms compiled from multiple sources. Finally, we discuss two not yet implemented analysis components that use machine learning and network analysis to identify media bias. All dictionary-based analysis components are experimentally extended with the use of general word embeddings. We also show the results of a user study.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3383583.3398585 | JCDL '20: The ACM/IEEE Joint Conference on Digital Libraries in 2020
Virtual Event
China
August, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7585-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Timo Spinde | 1 | 0 | 3.38 |
| felix hamborg | 2 | 19 | 9.34 |
| Bela Gipp | 3 | 432 | 51.77 |