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.
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.
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.
This is a retrospective observational study.
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.
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.
Except for one tweet, tweets written and retweeted by Japanese physician influencers concerning the COVID-19-related drugs contained predominantly accurate information.
In recent disaster situations, social media platforms, such as Twitter, played a major role in information sharing and widespread communication. These situations require efficient information ...sharing; therefore, it is important to understand the trends in popular topics and the underlying dynamics of information flow on social media better. Developing new methods to help us in these situations, and testing their effectiveness so that they can be used in future disasters is an important research problem. In this study, we proposed a new model, “topic jerk detector.” This model is ideal for identifying topic bursts. The main advantage of this method is that it is better fitted to sudden bursts, and accurately detects the timing of the bursts of topics compared to the existing method, topic dynamics. Our model helps capture important topics that have rapidly risen to the top of the agenda in respect of time in the study of specific social issues. It is also useful to track the transition of topics more effectively and to monitor tweets related to specific events, such as disasters. We attempted three experiments that verified its effectiveness. First, we presented a case study applied to the tweet dataset related to the Fukushima disaster to show the outcomes of the proposed method. Next, we performed a comparison experiment with the existing method. We showed that the proposed method is better fitted to sudden burst accurately detects the timing of the bursts of the topic. Finally, we received expert feedback on the validity of the results and the practicality of the methodology.
Information spreading on social media is a crucial issue to build a safe society. In particular, during emergencies, misinformation and uncertain information can lead to social disruption and cause ...significant damage to our lives. Here we built a retweet network from 24 million radiation-related tweets by 1.3 million accounts in the immediate aftermath of the Fukushima nuclear power plant accident in 2011. Then we simulated the information spreading on the network to explore ways to spread scientifically accurate information. Our simulation replicated the reality in which the number of scientific evidence-based tweets experienced a gradual decline while the number of emotional tweets increased. We also showed that increasing new direct retweets from the influencers could effectively spread scientific evidence-based information in our hypothetical simulations.
The mass mμ− of the negative muon is one of the parameters of the elementary particle Standard Model and it allows us to verify the CPT (charge–parity–time) symmetry theorem by comparing mμ− value ...with the mass mμ+ of the positive muon. However, the experimental determination precision of mμ− is 3.1ppm, which is an order of magnitude lower than the determination precision of mμ+ at 120ppb. The authors aim to determine mμ− and the magnetic moment μμ− with a precision of O(10ppb) through spectroscopy of the hyperfine structure (HFS) of muonic helium-4 atom (4Heμ−e−) under high magnetic fields. He4μ−e− is an exotic atom where one of the two electrons of the He4 atom is replaced by a negative muon. To achieve the goal, it is necessary to determine the HFS of He4μ−e− with a precision of O(1ppb). This paper describes the determination procedure of the HFS of He4μ−e− in weak magnetic fields reported recently, and the work towards achieving the goal of higher precision measurement.
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.
The measurement of the ground state hyperfine structure of muonic helium has the potential to improve the precision of the mass of the negative muon by a factor of 50 or more. The mass of the ...negative muon is very important because it enables us to test the CPT theorem by comparison with positive muon mass. We aim to measure the hyperfine structure of muonic helium with a precision 1000 times higher than previous experiments 1,2 using the highintensity muon beam at J-PARC and have already obtained results better than the current precision in zero-field measurements in a test experiment in March 2021. To further improve the precision, we plan to measure in a high magnetic field and incorporate a technique that can produce highly polarized muonic helium atom 3. In this paper, we will report on these developments.
Muonium (Mu) is a hydrogenlike atom comprising a positive muon and an electron, both pointlike leptons. Spectroscopy of Mu is a promising method in the search for new physics in particle-physics ...research, superior to that of hydrogen for which the uncertainty in the proton radius and its internal structure gives limitations. Microwave spectroscopy of the Mu hyperfine structure (HFS) provides the most precise estimation of the magnetic moment and the mass of the muon, opening a way to search for a physics beyond the Standard Model. MuSEUM Collaboration is studying Mu-HFS at the J-PARC muon facility in Japan, aiming at a precision one order of magnitude better than before. With our new spectroscopic technique which does not require any frequency sweep or Fourier transformation, the resonance frequency can be obtained directly by fitting a simulated function to the time evolution of the Rabi oscillation at a fixed microwave frequency. This method, named Rabi-oscillation spectroscopy, can improve the precision by eliminating systematic uncertainties due to power fluctuations. After a decade of our study under zero magnetic field with fruitful results, we are about to start our experiment under a high magnetic field.
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