Abstract: Based on a convenience sample of 2,092 participants from across all 50 states of the United States, a survey was conducted to explore Americans’ mask-wearing behaviors during COVID-19 according to their political convictions, religious beliefs, and ethnic cultures from late July to early September, 2020. The purpose of the study is to provide evidential support for government policymaking so as to drive up more effective public policies by taking into consideration the variance in these social factors. It was found that the respondents’ party affiliation or preference, religious belief, and ethnicity, in addition to their health condition, gender, level of concern of contracting COVID-19, all affected their mask-wearing habits both in March, the initial coronavirus outbreak stage, and in August, when mask-wearing had been made mandatory by state governments. The study concludes that pandemic awareness campaigns must be run among all citizens, especially among African Americans, Muslims, and Republicans, who have the lowest rates of wearing masks, in order to protect themselves and others. It is recommended that complementary cognitive bias awareness programs should be implemented in non-Black and non-Muslim communities to eliminate social concerns that deter them from wearing masks.
Abstract: By the evolvement in technology, the way of
expressing opinions switched direction to the digital world. The
domain of politics, as one of the hottest topics of opinion mining
research, merged together with the behavior analysis for affiliation
determination in texts, which constitutes the subject of this paper.
This study aims to classify the text in news/blogs either as
Republican or Democrat with the minimum number of features. As
an initial set, 68 features which 64 were constituted by Linguistic
Inquiry and Word Count (LIWC) features were tested against 14
benchmark classification algorithms. In the later experiments, the
dimensions of the feature vector reduced based on the 7 feature
selection algorithms. The results show that the “Decision Tree”,
“Rule Induction” and “M5 Rule” classifiers when used with “SVM”
and “IGR” feature selection algorithms performed the best up to
82.5% accuracy on a given dataset. Further tests on a single feature
and the linguistic based feature sets showed the similar results. The
feature “Function”, as an aggregate feature of the linguistic category,
was found as the most differentiating feature among the 68 features
with the accuracy of 81% in classifying articles either as Republican
or Democrat.