Extra posting. How to know if a news piece is biased? how to detect bias in news?/Mingyeong Joo(주민경)

Q) How to know if a news piece is biased? 

How to detect bias in news?

A) We can detect the bias in the news through different texts and contents, even though each press is the same subject. Each press has its own political color. This can be seen in the media reports of Hillary and Trump at the time of the US presidential election.

[references: https://www.washingtonpost.com/news/monkey-cage/wp/2016/09/20/is-the-media-biased-toward-clinton-or-trump-heres-some-actual-hard-data/?utm_term=.f7f592fd6fe2]

How did you go about measuring media coverage of Hillary Clinton and Donald Trump?

We compiled a total of 21,981 articles written about the election dating back to July 1, 2015. To be included in our data set, each article had to reference either Donald Trump or Hillary Clinton in its headline (but not both). The articles came from the websites of eight major media outlets: the New York Times, The Washington Post, Chicago Tribune, Wall Street Journal, Slate, Politico, Fox News and the Weekly Standard. We wanted a mixture liberal and conservative outlets, at least according to conventional wisdom.

We looked at the number of articles that were published about each candidate over time, which captures their ability to dictate the news cycle. And using the actual text of the articles, we evaluated the tone of the coverage — how positive or negative it was toward each candidate — and how it has shifted throughout the campaign.

How do you know whether a story is positive or negative about either of the candidates?

We did this via a computer algorithm, which is becoming increasingly common as social scientists work with huge data sets of text. There are a variety of approaches to what’s often called sentiment analysis, but our methodology was this: for each article, the algorithm identified every adjective. Then, using a very large word bank, it scored the adjectives on a scale of -1.0 (most negative) to +1.0 (most positive). The computer then averaged those values to generate an overall sentiment score for each article.

This obviously isn’t perfect. A computer’s sense of sentiment can be tripped up by things like satire, slang or misspellings. But given that we were working with news articles (the Onion wasn’t among our outlets), we believe these concerns are less relevant. Moreover, sentiment analysis has been shown to be surprisingly effective in predicting the stock market, summarizing customer feedback and delineating a population’s political views.