Exclusion of foreigners related to COVID-19 – Florida News Times
Since the outbreak of the COVID-19 pandemic in Wuhan, China, a number of totally unjustified anti-Asian sentiments have emerged on social media in the United States and elsewhere, a global that we all must face. It’s a problem. Researchers in China and the United States are investigating how this alien exclusion is categorized on one of the most prominent social media platforms, Twitter, and understand how best to deal with it. ..
Write in International Journal of Social Systems SciencePeng Zhao and Xin Wang of Big Data and AI Labs at Intelligent Rabbit LLC in New Jersey, and Xi Chen of the Faculty of Humanitarian Law at Beijing University of Civil Engineering and Technology suggest: Deep learning Can be used for investigation public opinion Regarding political opinions and geographical diversity.
The team has developed a new way to categorize Twitter users who are posting updates with pandemic-related anti-Asian sentiment. They used a new dataset to track users based on 10 million tweets. It was possible to take advantage of the known sentiment surrounding US elections and geopositions at home. “Experiential results are political sentiment and county-level election results. Model building“The team writes. They can use data from more than 190,000 Twitter users to train deep neural network (DNN) models and classify Twitter activity as “dislike” or “non-dislike” with 61% accuracy. I did. ..
Such classification should be sufficient to guide other classification systems and manual intervention to determine who is expressing xenophobic emotions. You can then use it to determine if a particular user should be responsible for further investigation, suspension, or education. The team points out that anti-Asian sentiment is not limited to the Twitter platform, nor is it limited to the United States. It can be found on all platforms including comments and posts from around the world, including Facebook, Instagram and YouTube. As such, the team also hates online in the context of COVID-19 at the global level, extracting features from other platforms (image, audio, video), providing a multifaceted understanding of anti-Asian alien exclusion. He added that it would be useful for.
Write in International Journal of Social Systems SciencePeng Zhao and Xin Wang of Big Data and AI Labs at Intelligent Rabbit LLC in New Jersey, and Xi Chen of the Faculty of Humanitarian Law at Beijing University of Civil Engineering and Technology suggest: Deep learning Can be used for investigation public opinion Regarding political opinions and geographical diversity.
The team has developed a new way to categorize Twitter users who are posting updates with pandemic-related anti-Asian sentiment. They used a new dataset to track users based on 10 million tweets. It was possible to take advantage of the known sentiment surrounding US elections and geopositions at home. “Experiential results are political sentiment and county-level election results. Model building“The team writes. They can use data from more than 190,000 Twitter users to train deep neural network (DNN) models and classify Twitter activity as “dislike” or “non-dislike” with 61% accuracy. I did. ..
Such classification should be sufficient to guide other classification systems and manual intervention to determine who is expressing xenophobic emotions. You can then use it to determine if a particular user should be responsible for further investigation, suspension, or education. The team points out that anti-Asian sentiment is not limited to the Twitter platform, nor is it limited to the United States. It can be found on all platforms including comments and posts from around the world, including Facebook, Instagram and YouTube. As such, the team also hates online in the context of COVID-19 at the global level, extracting features from other platforms (image, audio, video), providing a multifaceted understanding of anti-Asian alien exclusion. He added that it would be useful for.
floridanewstimes.com
