During the COVID-19 pandemic, racists remarks accompanied by racist hashtags were disseminated via social media. Particularly, Asian Americans in the U.S. have been suffered from racism and xenophobia, resulting in physical violence and mental harassment in many cases. Despite the major function of the social media as an openaccess platform for unedited and free speech for people with a diverse background, the global episodes of the soaring racism and xenophobia occurred in online public arenas reaffirmed that the platforms could be used for a nurturing ground of racism and xenophobia. This study examined the top influencers in the racist hashtag Twitter network and top shared neighboring hashtags with #Chinavirus or #Chinesevirus. We extracted topics from the racist hashtag Twitter network applying the state-of-the-art BERTopic modeling technique and conducted a geolocational analysis of the participants of the network globally and by U.S. states. Trump was identified as the most influential actor in the #Chinavirus and #Chinesevirus Twitter network. This study confirmed previous literature that the political elite's public communication strategy to deviate the attention of the public suffered from the new disease and went through hardships under the epidemic crisis.
Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.
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