Measuring the Bias of the Media's Many Voices
Document Type
Oral Presentation
Campus where you would like to present
SURC 137B
Start Date
21-5-2015
End Date
21-5-2015
Keywords
Media, Bias, Metric
Abstract
Breaking news is often delivered by various sources of media, but the wording used can elicit a specific response from the readers, creating bias. We have created a suite of tools for conducting media bias research. Our beta version uses an open source web spider toolkit called Scrapy to obtain the media website’s text. This web spider is implemented by custom built back-end Python and Bash scripts. These scripts generate an XML file containing the text gathered from the media websites. Our web tool calculates metrics for performing media bias analysis by use of a large library of adjectives that are rated as either positive or negative. Finally, the tool displays those metrics for the user on a web graphical user interface. Using this first version of our tool, we are able to demonstrate a ranking of the bias of the text of two media sources.
Recommended Citation
Williams, Paul, "Measuring the Bias of the Media's Many Voices" (2015). Symposium Of University Research and Creative Expression (SOURCE). 18.
https://digitalcommons.cwu.edu/source/2015/oralpresentations/18
Department/Program
Computer Science
Additional Mentoring Department
Computer Science
Measuring the Bias of the Media's Many Voices
SURC 137B
Breaking news is often delivered by various sources of media, but the wording used can elicit a specific response from the readers, creating bias. We have created a suite of tools for conducting media bias research. Our beta version uses an open source web spider toolkit called Scrapy to obtain the media website’s text. This web spider is implemented by custom built back-end Python and Bash scripts. These scripts generate an XML file containing the text gathered from the media websites. Our web tool calculates metrics for performing media bias analysis by use of a large library of adjectives that are rated as either positive or negative. Finally, the tool displays those metrics for the user on a web graphical user interface. Using this first version of our tool, we are able to demonstrate a ranking of the bias of the text of two media sources.
Faculty Mentor(s)
Filip Jagodzinski