Power of Data Journalism
Data journalism is the analysis of statistics to numerically justify stories and make predictions. To some degree, journalists have used data since the start of its widespread accessibility; some of the first examples of computer-analyzed data-based stories come from Harvard’s Nieman Foundation in the 1960s. The increased volume of polls that became publicly accessible with the development of the internet in the early 2000s made data journalism more mainstream.
Among the first sites that began focusing on data journalism was RealClearPolitics (RCP), whose purpose was to collate polls along with interesting political editorials to allow the public to find both forms of political information on one site. RCP’s first foray into true data analysis was the development of the RCP polling aggregate, which summarized all the publically available polls by reporting their median. This simple statistical analysis took the first step towards eliminating possible polling biases and allowing the public to gain a better perception to the true state of the race. However, data-based predictions using these techniques in the 2004 presidential elections were not particularly successful compared to their non-data-based counterparts.
In 2008, Nate Silver, a relatively unknown baseball statistician, correctly predicted every Senate race and all but one state in the presidential election. He accomplished this by neither physically reporting from the ground nor by using some esoteric technique of political science. Instead, he used basic statistics to analyze the large volume of polls available and predict an outcome. The message was clear: data-based electoral predictions appeared to be significantly more accurate than predictions based on traditional political science. Since then, data journalism has become increasingly popular and has made analysis of elections and other issues more accurate and quantitative.
FiveThirtyEight and other sites, such as Vox.com, have attempted to apply data to other forms of journalism beyond electoral and sports predictions. Common applications include testing conventional wisdom in political science such as the opposition party gaining seats during midterms and the party affiliation of a state fully determining Congressional races, and looking at the effects of certain media-hyped events on the campaign. Another trend is the increasing use of journalist-created or crowd-sourced datasets. More recently, data journalism has been extended by the relaunch of FiveThirtyEight at ESPN to analyze the benefits of college, nutritional guidelines, and restaurant rating systems. Nate Silver is pushing the limits of data journalism and appears to be outcompeting traditional journalism across all fields.
However, data is a double-edged sword. It is wrought with difficulties. Most journalists are not trained statisticians and don’t know how to interpret accurately the probabilistic nature of data nor do they know how to deal with models with seemingly contradictory conclusions. The few are publishing their predictions and analyses on sites that aren’t mainstream. More importantly, journalism is not yet fully aware of the latent limits of data-based reporting. As powerful a tool as data is, it is also easy to misuse. Many prominent media outlets such as The New York Times unintentionally misreport data predictions when they report to the general public. As a result, data is often a double-edged sword: it can help improve the public’s awareness of the world around them, but it can also dramatically mislead the public.
The first, most common, pitfall is that data is inherently probabilistic. Predictions are not reported as certainties or facts; they have associated probabilities, which in more advanced analysis have their own error terms. These probabilities arise from a variety of sources: sample sizes and random error of polls, polling biases, potential flaws in the method, etc. The probabilistic nature of data is not respected by many publications.
The second, more subtle, pitfall is that while data is objective, any analysis of data must be subjective. The increasing volume of data available has highlighted a significant problem for data journalists: it is possible to find data saying almost anything. Data analysis must be performed to determine what the “truth” is or to make predictions, but this analysis has assumptions built into the model. At best, journalists should synthesize all the scientifically accurate models and report their results with as little bias as possible; this ensures that the public receives an accurate perspective of what the election would look like. However, a major effect is that many organizations only look at data that supports their own views or perform data analyses with assumptions that lead to favorable results.(https://www.daowen.com)
Then how far can data go? When correctly done, data journalism can seem incredibly powerful. Probabilistic predictions of virtually anything are possible with sufficiently complicated models and complete information. One of the most powerful applications of data journalism evaluates the effects of a particular action, hypothetical or real, on public opinion or on the state of the campaign. It is virtually impossible to compete with data by using traditional means; in the time a traditional journalist can identify a potential trend by interviewing five people, a data journalist can analyze statistics relating to five million people and make generalizations about the whole country. But even the most powerful and most accurate data journalist is limited. Data can only analyze or confirm trends that are observed on the ground. Data cannot identify movements of people, nor can it explain how people think or why they think the way they do. It can only generate hypotheses about how people will behave and make models that analyze whether those hypotheses are true.
The attempted overuse and extended misuse of data today is not just limited to journalism. Society is approaching a cult of scientism, where all aspects of our lives are distilled into numeric calculations and decisions are made based on calculations. This approach is incredibly powerful, as numbers are more informed and more objective than a person’s feelings can be. However, people must be aware of the limitations of data: any model inherently introduces its own assumptions, which must be tested, and no model can understand certain aspects of an issue. Data will be a ubiquitous part of our lives, just as it will be a ubiquitous part of journalism. It remains to be seen whether it will be a boon or a hindrance for us.
Words & Expressions
foray into：涉足
quantitative：量化的
probabilistic nature：或然性
hypothetical：假设的
ubiquitous：无所不在的