Hwalbin Kim

Talking About Genetically Modified (GM) Foods in South Korea: The Role of the Internet in the Spiral of Silence Process

Using the issue of genetically modified foods, this study examines how the Internet may affect the spiral of silence phenomenon in South Korea. More specifically, we explore whether the Internet has created a social environment, where people can exchange opinions more freely without being fearful of social isolation. Analyzing data from an online survey, we first examine whether the Internet can play a role as a source of information, from which people assess the climates of public opinion. Also examined is whether the opinions of netizens can comprise another form of opinion climate, exerting pressure on ones willingness to speak out. Finally, we explore whether expressing an opinion on the Internet is subject to the pressure of opinion climates. Findings suggest that the Internet may play an important role in shaping peoples perceptions of opinion climates. Perceived opinion congruence with other people were significantly associated with ones willingness to participate in an online forum, indicating that expressing an opinion on the Internet may be subject to the spiral of silence effect. We conclude that the Internet in South Korea may not have helped to diminish the social pressure that keeps citizens from expressing a minority view.

News photos of scientists skew race but not gender

This analysis of the photographs of scientists published in The Science Times, the weekly science section of The New York Times, shows the actual gender distribution among U.S. scientists was accurately represented in the newspaper. A race gap, however, still exists, with non-white scientists being significantly underrepresented. The analysis of visual framing indicates The Science Times portrays scientists as expert professionals.

Framing marijuana: How U.S. newspapers frame marijuana legalization stories (1995–2014)

Marijuana legalization has been one of the most controversial issues in the public health field. Since news frames can influence the publics perceptions about public health policy including marijuana legalization, it is important to understand how the media report this issue. Thus, we explore how U.S. newspapers present marijuana legalization stories, examining two key dimensions of framing: an organizing theme and a story tone. We analyzed news articles of national and regional newspapers between 1995 and 2014 (Nu202f=u202f640). Findings revealed that newspapers have largely presented marijuana legalization as a law enforcement issue, rather than an economic issue or a medical issue. Marijuana legalization has been differently presented according to each presidential period. Overall, marijuana legalization stories have been described using a neutral tone. However, findings showed that newspapers that were classified as politically liberal adopted a positive tone more frequently than newspapers that were classified as conservative. Our findings can help public health providers or policy makers understand the relationships between news stories and public opinion toward marijuana legalization. Conclusively, this study can provide a comprehensive analysis of news framing of marijuana legalization, examining two key dimensions of framing: organizing theme and story tone.

Evaluating Sampling Methods for Content Analysis of Twitter Data

Despite the existing evaluation of the sampling options for periodical media content, only a few empirical studies have examined whether probability sampling methods can be applicable to social media content other than simple random sampling. This article tests the efficiency of simple random sampling and constructed week sampling, by varying the sample size of Twitter content related to the 2014 South Carolina gubernatorial election. We examine how many weeks were needed to adequately represent 5u2009months of tweets. Our findings show that a simple random sampling is more efficient than a constructed week sampling in terms of obtaining a more efficient and representative sample of Twitter data. This study also suggests that it is necessary to produce a sufficient sample size when analyzing social media content.

A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis

Abstract To improve the flow of quality information and combat fake news on social media, it is essential to identify the origins and evolution patterns of false information. However, scholarship dedicated to this area is lacking. Using a recent development in the field of computational network science (i.e., evolution tree analysis), this study examined this issue in the context of the 2016 US presidential election. By retrieving 307,738 tweets about 30 fake and 30 real news stories, we examined the root content, producers of original source, and evolution patterns. The findings revealed that root tweets about fake news were mostly generated by accounts from ordinary users, but they often included a link to non-credible news websites. Additionally, we observed significant differences between real and fake news stories in terms of evolution patterns. In our evolution tree analysis, tweets about real news showed wider breadth and shorter depth than tweets about fake news. The results also indicated that tweets about real news spread widely and quickly, but tweets about fake news underwent a greater number of modifications in content over the spreading process.