Summary: Unfair and imbalanced news stories always mislead readers, hiding and even distort truths, thus decreasing the credibility of media as well as increasing ‘news victims’. As a qualified news organization, one must get its news as close to the fact as possible. This time we want to take China Daily as an example, to analyze whether its news is fair or not.
We decided to rely on data to quantize the requirement, thus we use python to show the most effective way to figure out the fairness of news.
Background: Difficulty to Reach Absolute Objectivity
According to the Cambridge online dictionary, objectivity means “not influenced by personal opinion or feeling.” For a long time in journalism, objectivity meant writing a story without putting any personal opinion into it.
Over the last several years, many journalists stopped using “objectivity” in favor of the word “fairness.” Complete objectivity, they reasoned, is impossible. Fairness is more possible. Fairness means that you tell a story in ways that are fair to all sides once all the available information is considered.
Telling a story fairly is more difficult than it sounds. Reporters try to put colorful images and descriptions into their stories. For fresh reporters, especially those working in a second language, it can sometimes be difficult to distinguish between colorful description and editorializing. Some words have a feeling or connotation to them that is hard to recognize. Some English words have “loaded” or “double” meanings that are extremely positive or negative. Writers should be aware of the positive or negative meanings of a word and how its use to affect an article. Also, as human beings, we all have feelings and opinions about events and issues around us—-it is sometimes difficult to conceal those feelings, especially if we feel strongly about something. These feelings sometimes come through in our stories in the words we choose.
Therefore, the TextBlob, a module of python, is designed for pointing out humans’ subjectivity in news.
▲ A Bond’s Lifetime (Source:
The Data & News Society 