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The celebrated result of Eskin, Margulis and Mozes [ 8 ] and Dani and Margulis [ 7 ] on quantitative Oppenheim conjecture says that for irrational quadratic forms \begin{document}$ q $\end{document} of rank at least 5, the number of integral vectors \begin{document}$ \mathbf v $\end{document} such that \begin{document}$ q( \mathbf v) $\end{document} is in a given bounded interval is asymptotically equal to the volume of the set of real vectors \begin{document}$ \mathbf v $\end{document} such that \begin{document}$ q( \mathbf v) $\end{document} is in the same interval. In rank \begin{document}$ 3 $\end{document} or \begin{document}$ 4 $\end{document} , there are exceptional quadratic forms which fail to satisfy the quantitative Oppenheim conjecture. Even in those cases, one can say that two asymptotic limits coincide for almost all quadratic forms([ 8 , Theorem 2.4]). In this paper, we extend this result to the \begin{document}$ S $\end{document} -arithmetic version.

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.

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.

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.

Informed by uncertainty–identity theory, this study tested the polarizing effect of partisan-led politicization of science and ways to combat it. Using a national sample of South Koreans ( N = 840), our online experiment found that when partisan elites, as opposed to scientists (or civic activists), spearheaded politicization, attitude polarization emerged via partisan motivated reasoning. Such polarizing effects of party cues did not persist when subjective certainty and self-affirmation enhanced the level of certainty partisans felt about their surroundings and themselves. These patterns proved consistent across multiple scientific issues, including climate change, genetically modified foods, and algae blooms. The implications of the findings are discussed in light of how to attenuate the polarizing effect of partisan-led politicization through the lens of social identity approaches. Given that this study provides one of the first pieces of evidence on the topic outside the Western context, the advantages of using a South Korean sample are noted.

This study explored the presence of digital echo chambers in the realm of partisan media’s news comment sections in South Korea. We analyzed the political slant of 152 K user comments written by 76 K unique contributors on NAVER, the country’s most popular news aggregator. We found that the political slant of the average user comments to be in alignment with the political leaning of the conservative news outlets; however, this was not true of the progressive media. A considerable number of comment contributors made a crossover from like-minded to cross-cutting partisan media and argued with their political opponents. The majority of these crossover commenters were “headstrong ideologues,” followed by “flip-floppers” and “opponents.” The implications of the present study are discussed in light of the potential for the news comment sections to be the digital cafés of Public Sphere 2.0 rather than echo chambers.

Restrictions like work requirements and constraints on voucher transfers are often used in social welfare systems, but little empirical evidence exists on their impact on wellbeing. We conducted a 10-day randomized experiment with 432 individuals living below the poverty line in the Kawangware settlement of Nairobi, Kenya, testing two elements of social welfare design: workfare versus welfare and restricted versus unrestricted vouchers. Participants were randomly assigned to a “Work” condition, involving daily work for unrestricted vouchers, or one of two “Wait” conditions, involving daily waiting for vouchers that were either unrestricted or partially restricted to staple foods. We find that working improved psychological wellbeing relative to waiting, suggesting that the means of implementing welfare programs may have important effects on individuals beyond the impact of monetary benefit alone. Furthermore, although the restrictions were inframarginal, partially restricted vouchers crowded-in spending on staple foods, suggesting the existence of a “flypaper effect” in spending from restricted vouchers.

Health message design combines selected visual and textual components that are thought to work in concert to produce a particular intended message effect. Most health message effects research assumes rather than determines that message recipients attend to those visual and textual components. In contrast, the present research mapped viewing patterns of 50 participants in response to a set of anti-binge drinking print messages using eye-tracking methodology. Results showed that participants primarily viewed faces of persons portrayed in the messages, as well as alcohol use cues and cryptic one-liners. Textual components (e.g., information about consequences of heavy drinking) were viewed infrequently and briefly. Viewing patterns were associated with perceptions of message effectiveness, but more so for women than for men. Additionally, men, for whom anti-binge drinking messages were more self-relevant than for women, viewed message components more often and longer than women. These findings suggest that when message recipients view a self-relevant health message, they may attend primarily to a subset of components that do not necessarily convey the full message.

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 ...

Supplemental material, Supplemental_Material_Merchants_of_Doubt for Defeating Merchants of Doubt: Subjective certainty and self-affirmation ameliorate attitude polarization via partisan motivated reasoning by Jiyoung Han and Yungwook Kim in Public Understanding of Science

We prove higher moment formulas for Siegel transforms defined over the space of unimodular $S$-lattices in $\mathbb Q_S^d$, $d\ge 3$, where in the real case, the formulas are introduced by Rogers (1955). As applications, we obtain the random statements of Gauss circle problem for any convex sets in $\mathbb Q_S^d$ containing the origin and of the effective Oppenheim conjecture for $S$-arithmetic quadratic forms., 27 pages

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.

This study analyzes the political slants of user comments on Korean partisan media. We built a BERT-based classifier to detect political leaning of short comments via the use of semi-unsupervised deep learning methods that produced an F1 score of 0.83. As a result of classifying 21.6K comments, we found the high presence of conservative bias on both conservative and liberal news outlets. Moreover, this study discloses an asymmetry across the partisan spectrum in that more liberals (48.0%) than conservatives (23.6%) comment not only on news stories resonating with their political perspectives but also on those challenging their viewpoints. These findings advance the current understanding of online echo chambers.

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.

The purpose of this study is to examine the effectiveness of the educational methods used in creativity enhancement classes. These are the independent and integrated approach method. For investigation, the changes in the participants before and after the Creative Engineering Design and Creative Thinking Training classes in the Engineering Department were analyzed using the TTCT creativity test. Participants in the Creative Thinking Training class showed more statistically significant increases in their scores for the detailed categories of Fluency and Abstractness of title. Thus, the independent approach method was found to be more effective in enhancing the creativity of engineering students.