We Love or Hate When Celebrities Speak Up about Climate Change: Receptivity to Celebrity Involvement in Environmental Campaigns

This study investigates public receptivity to celebrity\u27s climate change advocacy on YouTube through a semantic network analysis. The results of this study suggest that the YouTube video generated a number of viewers\u27 responses. Celebrity endorsement not only leaded public voices on climate change issue, but also their opinions on the celebrity endorser. This study found that most of viewers were polarized in their judgment and attitude toward the celebrity advocate either positively or negatively. This study offers an exploratory examination of the perceived star power and the role of celebrities as spokespersons for social causes. This study contributes to the theoretical foundation of the role of celebrity advocacy using social media. In addition, this study offers methodological insights into how to detect public perceptions and attitudes toward celebrity endorsement of social causes by analyzing public comments

Celebrities’ Climate Change Advocacy on Twitter and its Effects on Public Perception and Behavioral Change

This research adds the growing body of literature on the role of celebrities as emergent spokespersons in climate advocacy and the process and consequences of its effects on public attitudes and behaviors to resolve the climate crisis. By applying social cognitive theory in conjunction with emotional appeals and language styles as message frames, the study examines the effects of role-modeling in adoption of eco-attitudes and behaviors. In a 2 x 2 design, the independent variables were emotion frame (fear, hope) and celebrity involvement frame (first person pronouns; FPP, non-first person pronouns; NFPP). For the manipulation check, the tweets were pilot tested. The main study was an experiment that asked participants to read tweets attributed to Leonardo DiCaprio or Pharrell Williams. Four main dependent variables were attitudes toward climate change mitigation and three behaviors, including support for government action, intention to engage in sustainable behavior, and intention to participate in activism for climate change mitigation. The role of two mediating variables (risk awareness, response efficacy) and one moderating variable, parasocial interaction (PSI) with the celebrity, were also examined. First, one-way ANCOVAs compared the effects of emotion frames to the control group. No evidence of the effects of emotion frame over unrelated messages on any dependent variables was found. Second, 2 (fear vs hope) x 2 (FPP vs NFPP) ANCOVAs found that fear-framed messages were more effective than hope-framed messages in driving intention for participation in activism, but emotion frame did not affect any other variables. The results also found that FPP frames led to more positive attitude (compared to NFPP frames), but had no effect on behaviors. Third, regression analyses found no evidence that risk awareness or response efficacy mediated the effect of emotion frames on attitudes or behaviors. In addition, the study discovered that PSI was a strong positive predictor of attitudes and all behaviors, but PSI did not moderate the impact of the celebrity involvement frame. The findings provide empirical evidence of the potential for celebrities to serve as role models in climate advocacy by psychologically involving people, which can be translated to the adoption of attitudes and behaviors

The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis

This study proposes alternative measures of infotainment’s effects on audience perception and reception of news on social media, focusing on infotainment coverage of North Korea. We determine the elements of framing strategies and narrative styles in facilitating public attention, positive and negative responses, and engagement in news content. We used the YouTube application programming interface to collect data from VideoMug, Korea’s most popular YouTube channel, run by the Seoul Broadcasting System. We examined 23,774 replies commenting on North Korea-related video clips from July 1, 2018, to May 17, 2019. The findings show that entertainment and human interest frames were effective in drawing public attention to news coverage about North Korea. Using humor and colloquial language facilitated public attention (both positive and negative) and public engagement. Over half (59.55%) of the comments generated positive emotions; less than one-third generated negative emotions (31.41%); and a few generated neutral ones (9.03%). The infotainment approach helped make South Koreans’ attitudes toward North Korea and inter-Korean relations more positive. A small number of users who served as top authorities were extremely partisan and conducted intense debates about infotainment practices. This study’s hybrid analytical framework using computerized text mining techniques offers both theoretical and methodological insights into the function of infotainment in the context of social media

Infodemiološka studija o upotrebi maski za lice tijekom pandemije COVID-19: usporedba SAD-a i Južne Koreje

In the midst of the COVID-19 pandemic, there have been varied responses to public health officials\u27 recommendations about wearing face masks as a means to slow the spread of the virus. This study, by using Twitter data, aims to explore the role of digital technology in facilitating public conversations and formulating public perception regarding face masks during the COVID-19 pandemic in two contrasting contexts: the U.S. and South Korea. From January 1, 2020 to April 14, 2020, a total of 22,928 users generated 27,501 tweets regarding face masks in the U.S. network, whereas 17,267 users produced 18,686 tweets in that of South Korea. The results of the semantic network analysis shed light on Americans\u27 initial resistance to wearing masks as well as Koreans\u27 willingness to comply. Details of the results are discussed further in the paper. With real-time data aggregation, this study gives insight into the rising controversy regarding wearing face masks during COVID-19 while providing implications for health officials designing strategic communication messages.Usred pandemije COVID-19, dobivamo različite odgovore na preporuke službenika javnoga zdravstva o nošenju maski za lice kao mjere koja usporava širenje virusa. Ova se studija koristi podacima s Twittera kako bi istražila ulogu digitalne tehnologije u olakšavanju javne komunikacije i formuliranju percepcije javnosti o nošenju maski tijekom pandemije COVID-19 u dva konteksta: SAD-u i Južnoj Koreji. Od 1. siječnja do 14. travnja 2020. u mreži SAD-a ukupno je 22 928 korisnika generiralo 27 501 tweet na temu maski za lice, dok ih je u Južnoj Koreji 17 267 korisnika proizvelo 18 686. Rezultati analize semantičke mreže otkrivaju početni otpor Amerikanaca prema nošenju maski, kao i spremnost Korejaca da se mjera pridržavaju. Pojedinosti rezultata iznesene su u radu. Uz agregaciju podataka u stvarnom vremenu, ova studija daje uvid u sve veći prijepor povezan s nošenjem maski za lice tijekom pandemije COVID-19, dajući u isti mah informacije zdravstvenim djelatnicima koji oblikuju strateške komunikacijske poruke

Analytical framework for evaluating digital diplomacy using network analysis and topic modeling: Comparing South Korea and Japan

This study introduces a data-driven approach for assessing the practices and effectiveness of digital diplomacy, using the cases of South Korea and Japan. The study compared the networking power of public diplomacy organizations based on social media use, engagement with the public, interaction patterns among the public, and public perceptions and attitudes toward organizations. This was accomplished through a three-step method employing social network analysis and topic modeling. The network analysis found that the Korean public diplomacy organization generated a larger, more loosely connected, and decentralized comment network than the Japanese organization, which presented a “small-world” connectivity pattern with highly interconnected actors. The findings also suggest that, compared to the Japanese organization, the Korean organization was successful in not only enhancing its soft power through social media but also building international networks among the foreign public

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

This article investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak

Identification of dendritic cell precursor from the CD11c+ cells expressing high levels of MHC class II molecules in the culture of bone marrow with FLT3 ligand

Dendritic cells (DCs) are readily generated from the culture of mouse bone marrow (BM) treated with either granulocyte macrophage-colony stimulating factor (GM-CSF) or FMS-like tyrosine kinase 3 ligand (FLT3L). CD11c+MHCII+ or CD11c+MHCIIhi cells are routinely isolated from those BM cultures and generally used as in vitro-generated DCs for a variety of experiments and therapies. Here, we examined CD11c+ cells in the BM culture with GM-CSF or FLT3L by staining with a monoclonal antibody 2A1 that is known to recognize mature or activated DCs. Most of the cells within the CD11c+MHCIIhi DC gate were 2A1+ in the BM culture with GM-CSF (GM-BM culture). In the BM culture with FLT3L (FL-BM culture), almost of all the CD11c+MHCIIhi cells were within the classical DC2 (cDC2) gate. The analysis of FL-BM culture revealed that a majority of cDC2-gated CD11c+MHCIIhi cells exhibited a 2A1-CD83-CD115+CX3CR1+ phenotype, and the others consisted of 2A1+CD83+CD115-CX3CR1- and 2A1-CD83-CD115-CX3CR1- cells. According to the antigen uptake and presentation, morphologies, and gene expression profiles, 2A1-CD83-CD115-CX3CR1- cells were immature cDC2s and 2A1+CD83+CD115-CX3CR1- cells were mature cDC2s. Unexpectedly, however, 2A1-CD83-CD115+CX3CR1+ cells, the most abundant cDC2-gated MHCIIhi cell subset in FL-BM culture, were non-DCs. Adoptive cell transfer experiments in the FL-BM culture confirmed that the cDC2-gated MHCIIhi non-DCs were precursors to cDC2s, i.e., MHCIIhi pre-cDC2s. MHCIIhi pre-cDC2s also expressed the higher level of DC-specific transcription factor Zbtb46 as similarly as immature cDC2s. Besides, MHCIIhi pre-cDC2s were generated only from pre-cDCs and common DC progenitor (CDP) cells but not from monocytes and common monocyte progenitor (cMoP) cells, verifying that MHCIIhi pre-cDC2s are close lineage to cDCs. All in all, our study identified and characterized a new cDC precursor, exhibiting a CD11c+MHCIIhiCD115+CX3CR1+ phenotype, in FL-BM culture

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40% FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands

Optimal Infrastructure System Maintenance and Repair Policies with Random Deterioration Model Parameters

Accurate facility deterioration models are important inputs for the selection of Infrastructure Maintenance, Repair, and Reconstruction (MR & R) policies. Deterioration models are developed based on expert judgment or empirical observations. These resources, however, might not be sufficient to accurately represent the performance of infrastructure facilities. Incorrect deterioration models may lead to wrong predictions of infrastructure performance and selection of inappropriate MR & R policies. This results in higher lifecycle costs. Existing infrastructure MR & R decisionmaking models assume that deterioration models represent the real deterioration process of infrastructure facilities. This assumption ignores the uncertainty in empiricallyderived facility deterioration models. This dissertation presents a methodology for selecting MR & R policies for systems of infrastructure facilities under uncertainty in the deterioration model parameters. It is assumed that inspections reveal the true conditions of facilities. Based on the inspection results, the deterioration model parameters can be updated to express the deterioration process more accurately. It is expected that more appropriate maintenance policies will be selected as a result. In the first part of this dissertation, it is assumed that facility inspections are performed at the beginning of every year. The model parameters are updated and MR & R policies are selected every year using the updated deterioration models. In the second part, the assumption is relaxed and alternate inspection frequencies are considered. In this case, the updates of the model parameters and the selection of optimal MR & R policies are executed only after an inspection. The results of the parametric analyses demonstrate that updating the deterioration models reduces the expected system costs. The results also show that relaxing the facility inspection frequency can reduce the total costs further