Media Bias in German News Articles: A Combined Approach

Abstract

Slanted news coverage, also called media bias, can heavily influence how news consumers interpret and react to the news. Models to identify and describe biases have been proposed across various scientific fields, focusing mostly on English media. In this paper, we propose a method for analyzing media bias in German media. We test different natural language processing techniques and combinations thereof. Specifically, we combine an IDF-based component, a specially created bias lexicon, and a linguistic lexicon. We also flexibly extend our lexica by the usage of word embeddings. We evaluate the system and methods in a survey (N = 46), comparing the bias words our system detected to human annotations. So far, the best component combination results in an F$$_{1}$$ 1 score of 0.31 of words that were identified as biased by our system and our study participants. The low performance shows that the analysis of media bias is still a difficult task, but using fewer resources, we achieved the same performance on the same task than recent research on English. We summarize the next steps in improving the resources and the overall results.

A domain-adaptive pre-training approach for language bias detection in news

“…The linguistic subtlety of slanted news coverage is known to be a great challenge for automated classification methods [42]. Recent media bias studies have progressed from manually generated linguistic features [37,38] to state-of-the-art NLP models yielding internal word representations by unsupervised or supervised training on massive text corpora. The Transformer architecture [48] has shown superior performance in several downstream tasks, such as, text classification [26][27][28], plagiarism detection [50,51], word sense disambiguation [52] and fake news detection on the health domain [49].…”

Machine-learning media bias

“…Early efforts in this direction have shown great promise, as reviewed in [9]. For example, various ML natural language processing (NLP) techniques have been employed to discover bias-inducing words from articles in four German newspapers [10] and six 20th Century Dutch newspapers [11]. ML NLP techniques have also been used to detect gender bias in sports interviews [12], to detect political bias in coverage of climate change [13], to identify trolling in social media posts [14], and to analyze bigram/trigram frequencies in the U.S. congressional record [15].…”