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Abstract. Although there is growing evidence that partisans believe they are further apart than they actually are, the causes and consequences of this misperception are not always clear. Informed by the literature on news framing and self-categorization theory, we hypothesize that the media’s focus on partisan conflict increases partisans’ perceptions of public polarization, which fuels partisan attitude polarization on disputed issues in news coverage. Study 1 supports this contention in the political domain. By retesting the hypotheses in a gender context, Study 2 further demonstrates that the impact of conflict news framing on attitude polarization is not simply due to preexisting political polarization. The implications of the present study are discussed in light of its generalizability to varying political systems.

Background: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. Objective: This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. Methods: We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. Results: This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. Conclusions: This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.

Two experiments tested whether exposure to partisan conflict-framed news polarizes news consumers along party lines. Partisan self-categorization and motivated reasoning were examined as potential ...

Background COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. Objective This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. Methods We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. Results This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. Conclusions This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.

The Purpose of the study was to analyze system of accreditation for mechanical engineering education between A university in Korea and M university in U.S.A. which were evaluated in 2007 under EC2000. Literature review through self-study report and homepage of two programs was used to accomplish objectives of the study. Strengths of U.S.A for accreditation evaluation system were systematic curriculum operation, design education with cooperation system, course based program outcome evaluation system, supportive circumstances for research of faculty and guidance to students. On the other hands, strengths of Korea were likely to use quantitative data for educational improvement and were able to systematic guide to students with portfolio. In the future, plan and research for design education, curriculum operation and program outcome evaluation are needed to establish ABEEK evaluation.

We report the application of two-dimensional (2D) photonic crystal (PC) array substrates for polymer:fullerene solar cells of which the active layer is made with blended films of poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM). The 2D PC array substrates were fabricated by employing a nanosphere lithography technique. Two different hole depths (200 and 300 nm) were introduced for the 2D PC arrays to examine the hole depth effect on the light harvesting (trapping). The optical effect by the 2D PC arrays was investigated by the measurement of optical transmittance either in the direction normal to the substrate (direct transmittance) or in all directions (integrated transmittance). The results showed that the integrated transmittance was higher for the 2D PC array substrates than the conventional planar substrate at the wavelengths of ca. 400 nm, even though the direct transmittance of 2D PC array substrates was much lower over the entire visible light range. The short circuit current density (J(SC)) was higher for the device with the 2D PC array (200 nm hole depth) than the reference device. However, the device with the 2D PC array (300 nm hole depth) showed a slightly lower J(SC) value at a high light intensity in spite of its light harvesting effect proven at a lower light intensity.