Methods of Bias and Terminology
*Voting Polls do not affect MBFC bias ratings
Bias by Omission: leaving one side out of an article, or a series of articles over a period of time; ignoring facts that tend to disprove liberal or conservative claims, or that support liberal or conservative beliefs.
Bias by Labeling: Bias by labeling comes in two forms. The first is the tagging of conservative politicians and groups with extreme labels while leaving liberal politicians and groups unlabeled or with more mild labels, or vice versa. The second kind of bias by labeling occurs when a reporter not only fails to identify a liberal as a liberal or a conservative as a conservative, but describes the person or group with positive labels, such as “an expert” or “independent consumer group.”
Bias by Placement: is where on a website (or newspaper) or in an article a story or event is printed; a pattern of placing news stories so as to downplay information supportive of either conservative views or liberal views.
Bias by Selection of Sources: including more sources that support one view over another.
Bias by Spin: is a reporter’s subjective comments about objective facts; makes one side’s ideological perspective look better than another.
Bias by Story Selection: a pattern of highlighting news stories that coincide with the agenda of either the Left or the Right, while ignoring stories that coincide with the opposing view.
Confirmation Bias: also called confirmatory bias or my side bias, is the tendency to search for, interpret, favor, and recall information in a way that confirms one’s beliefs or hypotheses, while giving disproportionately less consideration to alternative possibilities.
Connotation: the emotional and imaginative association surrounding a word that can be either positive or negative.
Denotation: the strict dictionary meaning of the word.
Loaded Language (Words): (also known as loaded terms or emotive language) is wording that attempts to influence an audience by using appeal to emotion or stereotypes. Such wording is also known as high-inference language or language persuasive techniques.
Purr Words: words used to describe something that is favored or loved.
Snarl Words: words used when describing something that a person is against or hates.
Other factors to look for:
Do the headlines and stories match?
Are important stories featured prominently?
Does the story offer an alternative point of view?
Consider the source!
When calculating Bias we take into consideration all of the above. We also use an objective formula as follows:
For Example, CNN looks like this:
USA Today looks like this:
Breitbart looks like this:
MSNBC looks like this:
Placement of the yellow dot is determined by ranking bias in four different categories. In each category the source is rated on a 0-10 scale, with 0 meaning without bias and 10 being the maximum bias(worst). These four numbers are then added up and divided by 4. This 0-10 number is then placed on the line according to their Left or Right bias. Scoring is as follows:
0 – 2 = Least Biased
2 – 5 = Left/Right Center Bias
5 – 8 = Left/Right Bias
8 – 10 = Extreme Bias
The categories are as follows:
- Biased Wording- Does the source use loaded words to convey emotion to sway the reader.
- Factual/Sourcing- Does the source report factually and back up claims with well sourced evidence.
- Story Choices: Does the source report news from both sides or do they only publish one side.
- Political Affiliation: How strongly does the source endorse a particular political ideology? In other words how extreme are their views. (This can be rather subjective)
Here is an example of how CNN scored and why they were placed in the middle of Left-Center:
Biased Wording = 3 (CNN uses slightly biased words that favor liberals)
Factual/Sourcing = 2 (CNN is pretty trustworthy for providing evidence and sources)
Story Choices = 4 (CNN moderately favors pro-liberal stories and publishes negative conservative stories)
Political Affiliation = 5 (CNN moderately favors liberal ideology)
Total = 14
Average 14/4 = 3.5
3.5 = Solid Left-Center Bias
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