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Anti-Asian prejudice increased during the COVID-19 pandemic, evidenced by a rise in physical attacks on individuals of Asian descent. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media platforms such as Twitter. In the present study, we investigated temporal and geographic patterns in the prevalence of social media content relevant to anti-Asian prejudice within the U.S. and worldwide. Specifically, we used the Twitter Data Collection API to query over 13 million tweets posted during the first 15 months of the pandemic (i.e., from January 30, 2020 to April 30, 2021), for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. Results of a range of exploratory and descriptive analyses offer novel insights. For instance, in the U.S., results from a burst analysis indicated that the prevalence of negative (anti-Asian) and positive (counter-hate) messages fluctuated over time in patterns that largely mirrored salient events relevant to COVID-19 (e.g., political tweets, highly-visible hate crimes targeting Asians). Other representative findings include geographic differences in the frequency of negative and positive keywords that shed light on the regions within the U.S. and the countries worldwide in which negative and positive messages were most frequent. Additional analyses revealed informative patterns in the prevalence of original tweets versus retweets, the co-occurrence of negative and positive content within a tweet, and fluctuations in content in relation to the number of new COVID-19 cases and reported COVID-related deaths. Together, 

Prejudice and hate directed toward Asian individuals has increased in prevalence and salience during the COVID-19 pandemic, with notable rises in physical violence. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media. In the present study, we investigated temporal and geographical patterns in social media content relevant to anti-Asian prejudice during the COVID-19 pandemic. Using the Twitter Data Collection API, we queried over 13 million tweets posted between January 30, 2020, and April 30, 2021, for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. In a series of descriptive analyses, we found differences in the frequency of negative and positive keywords based on geographic location. Using burst detection, we also identified distinct increases in negative and positive content in relation to key political tweets and events. These largely exploratory analyses shed light on the role of social media in the expression and proliferation of prejudice as well as positive responses online. 

We report the first evidence for the $h_b\left(2\mathrm{P}\right)\to \mathrm{\Upsilon }\left(1\mathrm{S}\right)\eta$transition with a significance of 3.5 standard deviations. The decay branching fraction is measured to be $\mathcal{B}\left[h_b\right(2\mathrm{P})\to \mathrm{\Upsilon }(1\mathrm{S}\left)\eta \right]=({7.1}_{-3.2}^{+3.7}\pm 0.8)\times {10}^{-3}$, which is noticeably smaller than expected. We also set upper limits on ${\pi }^0$transitions of $\mathcal{B}\left[h_b\right(2\mathrm{P})\to \mathrm{\Upsilon }(1\mathrm{S}\left){\pi }^0\right]<1.8\times {10}^{-3}$, and $\mathcal{B}\left[h_b\right(1\mathrm{P})\to \mathrm{\Upsilon }(1\mathrm{S}\left){\pi }^0\right]<1.8\times {10}^{-3}$, at the 90% confidence level. These results are obtained with a $131.4{\mathrm{fb}}^{-1}$data sample collected near the $\mathrm{\Upsilon }\left(5\mathrm{S}\right)$resonance with the Belle detector at the KEKB asymmetric-energy $e^{+}{e}^{-}$collider. Published by the American Physical Society2024 

We search for the rare decay $B^{+}\to K^{+}\nu \overline{\nu }$in a $362{\mathrm{fb}}^{-1}$sample of electron-positron collisions at the $\mathrm{\Upsilon }\left(4S\right)$resonance collected with the Belle II detector at the SuperKEKB collider. We use the inclusive properties of the accompanying $B$meson in $\mathrm{\Upsilon }\left(4S\right)\to B\overline{B}$events to suppress background from other decays of the signal $B$candidate and light-quark pair production. We validate the measurement with an auxiliary analysis based on a conventional hadronic reconstruction of the accompanying $B$meson. For background suppression, we exploit distinct signal features using machine learning methods tuned with simulated data. The signal-reconstruction efficiency and background suppression are validated through various control channels. The branching fraction is extracted in a maximum likelihood fit. Our inclusive and hadronic analyses yield consistent results for the $B^{+}\to K^{+}\nu \overline{\nu }$branching fraction of $[2.7\pm 0.5(\mathrm{stat})\pm 0.5(\mathrm{syst}\left)\right]\times {10}^{-5}$and $\left[{1.1}_{-0.8}^{+0.9}\right(\mathrm{stat}{)}_{-0.5}^{+0.8}\left(\mathrm{syst}\right)]\times {10}^{-5}$, respectively. Combining the results, we determine the branching fraction of the decay $B^{+}\to K^{+}\nu \overline{\nu }$to be $[2.3\pm 0.5(\mathrm{stat}{)}_{-0.4}^{+0.5}\left(\mathrm{syst}\right)]\times {10}^{-5}$, providing the first evidence for this decay at 3.5 standard deviations. The combined result is 2.7 standard deviations above the standard model expectation. Published by the American Physical Society2024