| 3 |
Philosophische Körper. Von digitalem Text zu greifbarem Material. ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Employing Argumentation Knowledge Graphs for Neural Argument Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Webis Argument Quality Corpus 2020 (Webis-ArgQuality-20) ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Webis Argument Quality Corpus 2020 (Webis-ArgQuality-20) ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Persuasiveness of News Editorials depending on Ideology and Personality ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Detecting Media Bias in News Articles using Gaussian Bias Distributions ...
|
|
|
|
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 featurebased 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 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/2010.10649
https://dx.doi.org/10.48550/arxiv.2010.10649
|
|
BASE
|
|
Hide details
|
|
|
| 13 |
Style Analysis of Argumentative Texts by Mining Rhetorical Devices ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
