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Do You Think It's Biased? How To Ask For The Perception Of Media Bias

Authors: Spinde, Timo; Kreuter, Christina; Gaissmaier, Wolfgang; Hamborg, Felix; Gipp, Bela; Giese, Helge;

Do You Think It's Biased? How To Ask For The Perception Of Media Bias

Abstract

Media coverage possesses a substantial effect on the public perception of events. The way media frames events can significantly alter the beliefs and perceptions of our society. Nevertheless, nearly all media outlets are known to report news in a biased way. While such bias can be introduced by altering the word choice or omitting information, the perception of bias also varies largely depending on a reader's personal background. Therefore, media bias is a very complex construct to identify and analyze. Even though media bias has been the subject of many studies, previous assessment strategies are oversimplified, lack overlap and empirical evaluation. Thus, this study aims to develop a scale that can be used as a reliable standard to evaluate article bias. To name an example: Intending to measure bias in a news article, should we ask, "How biased is the article?" or should we instead ask, "How did the article treat the American president?". We conducted a literature search to find 824 relevant questions about text perception in previous research on the topic. In a multi-iterative process, we summarized and condensed these questions semantically to conclude a complete and representative set of possible question types about bias. The final set consisted of 25 questions with varying answering formats, 17 questions using semantic differentials, and six ratings of feelings. We tested each of the questions on 190 articles with overall 663 participants to identify how well the questions measure an article's perceived bias. Our results show that 21 final items are suitable and reliable for measuring the perception of media bias. We publish the final set of questions on http://bias-question-tree.gipplab.org/.

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL), news bias, news slant, perception of text

22 references, page 1 of 3

T. Spinde et al., “Automated identification of bias inducing words in news articles using linguistic and context-oriented features,” Information Processing & Management, vol. 58, no. 3, p. 102505, 2021, doi: https://doi.org/10.1016/j.ipm.2021.102505.

T. Spinde, F. Hamborg, K. Donnay, A. Becerra, and B. Gipp, “Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias,” in Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, Virtual Event China, Aug. 2020, pp. 389-392. doi: 10.1145/3383583.3398619. [OpenAIRE]

[19] [20] [21] M. Levendusky and N. Malhotra, “Does Media Coverage of Partisan Polarization Affect Political Attitudes?,” Political Communication, vol. 33, no. 2, pp. 283-301, 2016, doi: 10.1080/10584609.2015.1038455.

T. Spinde, L. Rudnitckaia, Sinha Kanishka, F. Hamborg, B. and Gipp, and K. Donnay, “MBIC - A Media Bias Annotation Dataset Including Annotator Characteristics,” presented at the 16th International Conference (iConference 2021), Mar. 2021.

A. C. Gunther, N. Miller, and J. L. Liebhart, “Assimilation and Contrast in a Test of the Hostile Media Effect,” Communication Research, vol. 36, no. 6, pp. 747-764, Dec. 2009, doi: 10.1177/0093650209346804.

18, no. 1, pp. 17-36, Jan. 2015, doi: 10.1080/15205436.2013.877486.

M. Kim, “The Role of Partisan Sources and Audiences' Involvement in Bias Perceptions of Controversial News,” Media Psychology, vol. 19, no. 2, pp. 203-223, Apr. 2016, doi: 10.1080/15213269.2014.1002941.

T.-T. Lee, “The Liberal Media Myth Revisited: An Examination of Factors Influencing Perceptions of Media Bias,” Journal of Broadcasting & Electronic Media, vol. 49, no. 1, pp. 43-64, Mar. 2005, doi: 10.1207/s15506878jobem4901_4.

A. C. Gunther and J. L. Liebhart, “Broad Reach or Biased Source? Decomposing the Hostile Media Effect,” Journal of Communication, vol. 56, no. 3, pp. 449-466, Sep. 2006, doi: 10.1111/j.1460-2466.2006.00295.x.

C. J. Glynn and M. E. Huge, “How Pervasive Are Perceptions of Bias? Exploring Judgments of Media Bias in Financial News,” International Journal of Public Opinion Research, vol. 26, no. 4, pp. 543-553, Dec. 2014, doi: 10.1093/ijpor/edu004.

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    5
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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