Characteristics of Japanese physician influencers on Twitter during the COVID-19 pandemic and fact-checking their tweets on COVID-19-related drugs.

BACKGROUND: During the coronavirus disease-2019 (COVID-19) pandemic, segments of the public relied on social media platforms such as Twitter for medical information shared by medical personnel. Although physicians are likely to disseminate more accurate information on Twitter than non-medical individuals, it cannot be taken for granted. As such, tweets written by physicians in Japan should also be scrutinized for accuracy. PURPOSE: The purpose of this study was to create a profile of the most popular physician influencers on Twitter in Japan, and to do a fact-check of their tweets regarding COVID-19-related drugs. DESIGN: This is a retrospective observational study. METHODS: We purchased Twitter data for Japan for the initial 9 months of the COVID-19 pandemic (from January 2020 to September 2020), and extracted tweets with keywords related to COVID-19 at a sampling rate of 3%. The most popular physicians were identified and selected consecutively by searching for the top 1000 accounts using Twitter's search function. These top accounts were considered influencers and their tweets and retweets concerning COVID-19-related drugs were fact-checked against scientific literature. RESULTS: We identified 21 physician influencers with real names: most were male in their 40s and 50s working at private medical facilities. The contents of their tweets were mainly sourced from scientific publications that were current at that time. The fact-check revealed that only one of 50 tweets was not correct while the others had no identifiable inaccuracies. CONCLUSIONS: Except for one tweet, tweets written and retweeted by Japanese physician influencers concerning the COVID-19-related drugs contained predominantly accurate information.

Net-TF-SW: Event Popularity Quantification with Network Structure.

Event popularity quantification is essential in the determination of current trends in events on social media and the internet. Particularly, it is important during a crisis to ensure appropriate information transmission and prevention of false-rumor diffusion. Here, we propose Net-TF-SW - a noise-robust and explainable topic popularity analysis method. This method is applied to tweets related to COVID-19 and the Fukushima Daiichi Nuclear Disaster, which are two significant crises that have caused significant anxiety and confusion among Japanese citizens. The proposed method is compared to existing methods, and it is verified to be more robust with respect to noise.

Twitter use in scientific communication revealed by visualization of information spreading by influencers within half a year after the Fukushima Daiichi nuclear power plant accident.

Scientific communication through social media, particularly Twitter has been gaining importance in recent years. As such, it is critical to understand how information is transmitted and dispersed through outlets such as Twitter, particularly in emergency situations where there is an urgent need to relay scientific information. The purpose of this study is to examine how original tweets and retweets on Twitter were used to diffuse radiation related information after the Fukushima Daiichi nuclear power plant accident. Out of the Twitter database, we purchased all tweets (including replies) and retweets related to Fukushima Daiichi nuclear power plant accident and or radiation sent from March 2nd, 2011 to September 15th, 2011. This time frame represents the first six months after the East Japan earthquake, which occurred on March 11th, 2011. Using the obtained data, we examined the number of tweets and retweets and found that only a small number of Twitter users were the source of the original posts that were retweeted during the study period. We have termed these specific accounts as "influencers". We identified the top 100 influencers and classified the contents of their tweets into 3 groups by analyzing the document vectors of the text. Then, we examined the number of retweets for each of the 3 groups of influencers, and created a retweet network diagram to assess how the contents of their tweets were being spread. The keyword "radiation" was mentioned in over 24 million tweets and retweets during the study period. Retweets accounted for roughly half (49.7%) of this number, and the top 2% of Twitter accounts defined as "influencers" were the source of the original posts that accounted for 80.3% of the total retweets. The majority of the top 100 influencers had individual Twitter accounts bearing real names. While retweets were intensively diffused within a fixed population, especially within the same groups with similar document vectors, a group of influencers accounted for the majority of retweets one month after the disaster, and the share of each group did not change even after proven scientific information became more available.

Resolution enhancement of pHe+ atomic line profiles measured with a pulsed dye laser and a Fizeau wavelength meter.

A Fizeau wavelength meter was used to compensate for fluctuations in the longitudinal mode structure and wavelength of a pulsed dye laser. The average laser linewidth was effectively narrowed by selection of laser pulses with a single longitudinal mode. These techniques were recently employed to measure some atomic transition wavelengths in pHe+ to fractional precisions greater than 1 part in 10(7). The wavelengths were absolutely calibrated against iodine or tellurium lines by absorption spectroscopy or against neon or argon lines by optogalvanic spectroscopy.