Detecting Bias.

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Presentation transcript:

Detecting Bias

Bias A particular tendency, feeling, or opinion, especially one that is preconceived or unreasoned

Spin News and information that is manipulated or slanted to affect i ts interpretation and influence public opinion Example: Was the game…a loss, a close game, or a near-win??

Omission Not reporting on some events or only reporting selected material Are all sides covered? (i.e. in an article about an abortion clinic are both pro-life and pro-choice sides discussed?) Headlines: Misleading information – sometimes to shock

9 minutes, 36 seconds

labeling Like SPIN: Putting a label on someone to influence another’s opinion Ex- con vs. someone who got arrested for protesting Terrorist vs. Freedom fighter Republican vs. staunch conservative Democrat vs. far-left liberal vs. hippie Like OMISSION: purposely leaving out a label “According to experts…” but are these ‘experts’ for BOTH sides and are they actually ‘academic’ experts or just normal people?

POV & Backers Who wrote the article or who paid for the article? Was it a pro-smoking article written by a cigarette manufacturer? Was it a survey put out by the rancher’s lobby? Was it a study paid for by PETA? Is the newspaper owned by a staunch Socialist?

POV & Backers

Loaded Language Language that has ‘emotional’ connections used to persuade Revolution vs. coup Know-it-all vs. expert Infanticide vs. abortion Abortion vs. terminating a pregnancy

Context ‘Taking something out of context’: by removing context of the event, the original meaning is misrepresented

statistics Manipulating or interpreting data in various ways Chart Perception The ‘Cool’ Answer (Social Desirability Bias) Your ‘Pool’ (Area Bias)

statistics Chart Perception

statistics Chart Perception: what is the real data and what is it really telling you?

statistics The ‘Cool’ Answer (Social Desirability Bias): don’t want to give an answer that makes you look bad or vice versa

statistics Your ‘Pool’ (Area Bias): how well are the responders represented?

sources https://cseweb.ucsd.edu/~ricko/CSE3/Lie_with_Statistics.pdf http://faculty.washington.edu/jalbano/bias.html http://www.studentnewsdaily.com/ http://examples.yourdictionary.com/loaded-language-examples.html http://mediasmarts.ca/sites/mediasmarts/files/pdfs/lesson- plan/Lesson_Bias_News_Sources.pdf http://www.edu.gov.mb.ca/k12/cur/socstud/foundation_gr9/blms/9-1-3g.pdf http://fair.org/take-action-now/media-activism-kit/how-to-detect-bias-in-news-media/