An expert on the interplay between the media\u2014especially social media\u2014and public opinion, Guo uses big data research methods to study trust in journalism and how social media giants like Facebook and Twitter impact democracy. By leveraging computing power, she can sort through thousands\u2014sometimes millions\u2014of articles and posts to better understand mass media\u2019s influence. To figure out where the public was turning to get the buzz on GM mosquitoes, Guo compared online news coverage of the story with the debate on Twitter. With Weirui Wang, an associate professor at Florida International University, she looked at 464 news articles and thousands of tweets, finding Twitter had an \u201cinadequate discussion of risk\u201d when it came to the modified mosquitoes.<\/p>\n\n\n\n
While what they called elite (like the New York Times<\/a><\/em>) and emerging (HuffPost<\/a><\/em>) news outlets plunged into reporting on topics as diverse as experimental dangers, cost-effectiveness and ethics, the Twitter debate remained mostly stuck on the potential health impacts. \u201cTo obtain a well-rounded perspective about an issue,\u201d wrote Guo and Wang, who published their findings in the Journal of Risk Research in August 2020, \u201cpeople may still want to access online news sites as their main source of information instead of relying only on Twitter.\u201d<\/p>\n\n\n\n And what\u2019s true for people scrambling to understand GM mosquitoes may also say something about how their views are shaped on other topics, like the coronavirus, gun violence or presidential elections\u2014topics Guo has also studied.<\/p>\n\n\n\n A trained journalist and a founding member of BU\u2019s Faculty of Computing & Data Sciences<\/a>, Guo notes a common thread between her work as a reporter and as a researcher: \u201cWe\u2019re curious,\u201d she says. \u201cThe job of a journalist is to gather data, interview people to learn their thoughts. As a researcher, I\u2019m doing the same thing, but the method I use to get an answer is more systematic.\u201d<\/p>\n\n\n\n As well as studying media impact, she\u2019s developing tools to help journalists find stories in the world\u2019s data\u2014and navigate a polarized world.<\/p>\n\n\n\n A decade or two ago, studying media impact was relatively straightforward\u2014if a little laborious. Researchers would watch a handful of core news channels or shows, sift through newspaper articles and poll audiences and readers for their views on different subjects. Then came aggregators and blogs and podcasts and Twitter. Not to mention the proliferation of fake news. Now, researchers can access billions of tweets\u2014500 million new missives are posted on Twitter every day, according to Internet Live Stats\u2014not just for news, but to gauge ever-changing shifts and patterns in public opinion.<\/p>\n\n\n\n \u201cWhen I started my journey in this field, we still used a lot of traditional methods, for instance manual content analysis,\u201d says Guo, who began studying media effects\u2014an academic term for mass media\u2019s influence\u2014as a graduate student research assistant at the University of Texas at Austin. \u201cBut there\u2019s no way for me to go through a billion tweets, so I started to explore computational methods.\u201d<\/p>\n\n\n\n Working with computer scientists, she\u2019s helped design and perfect software that can analyze streams of data. For instance, in a recent study examining the link between how media outlets frame stories on gun violence and the issue\u2019s prominence among midterm election voters, Guo used a computer program to evaluate 42,917 news articles. She was looking to see if there was any difference in influence between articles that just reported on a single incident, one shooting or the impact on one victim\u2014 what researchers call an episodic frame\u2014and those stories weighing bigger societal issues and questions, called a thematic frame. Instead of manually sorting all the articles by their angle, as well as by outlet type\u2014conservative, liberal, nonpartisan, mainstream\u2014Guo and her fellow BU researchers taught a computer program to do the work for them.<\/p>\n\n\n\n \u201cWe start with a sample of articles and manually go through them and classify them,\u201d says Guo. \u201cThen we use methods in computer science, like machine learning, to train a machine model to classify all of the unlabeled data.\u201d<\/p>\n\n\n\n The program, nicknamed BERT\u2014for Bidirectional Encoder Representations from Transformers\u2014provided them with a much more comprehensive dataset than humans alone could amass. The researchers then combined that information with the results of a general population survey that asked people about their media use habits and views on gun violence.<\/p>\n\n\n\n \u201cWe correlate the two types of data to see whether exposure to certain media or certain media\u2019s framing will influence how they think,\u201d says Guo.<\/p>\n\n\n\n They\u2019re techniques she\u2019s also applied to the study of COVID and fake news, tracking differences between pandemic coverage in various countries and the power of misinformation to sway public debate. In one COVID project, Guo and her team analyzed the topics making headlines in countries as diverse as the United States, Egypt, China and Germany. They then compiled all of the information into an interactive world map (covid19.philemerge.com<\/a>), allowing users to drill down and see how the news focus shifted in each country over the first three months of the pandemic.<\/p>\n\n\n\nA Billion Tweets<\/h3>\n\n\n\n
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The Power of Journalism<\/h3>\n\n\n\n