A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing

We provide a large data set consisting of 2,057 sentences from 90 news articles and annotations of crowdworkers with respect to bias itself and the following bias dimensions:

1. hidden assumptions
2. subjectivity
3. representation tendencies

Our data set contains 44,547 labels in total (43,197 sentence labels and 1,350 article labels).

The news articles deal with the Ukraine crisis. They were published in 33 countries in total and were selected based on the data set of Cremisini et al. (Cremisini, A., Aguilar, D., & Finlayson, M. A. A Challenging Dataset for Bias Detection: The Case of the Crisis in the Ukraine, Proc. of SBP-BRiMS'19, pp. 173-183, 2019).

Each sentence was annotated by 5 crowdworkers. In total, we spent $ 3,335 for the crowdworkers annotations.

More information can be found in our GitHub repository. A description of the used file format is given in the codebook attached to the dataset.

Please cite our data set as follows:

@unpublished{Faerber2020Bias,
 author = {Michael F{\"{a}}rber and Victoria Burkard and Adam Jatowt and Sora Lim},
 title  = {{A Multidimensional Dataset for Analyzing and Detecting News Bias based on Crowdsourcing}},
 year   = {2020}
}