基于情感分析、主观性分析和毒性分析的COVID-19期间Twitter上关于在线学习的性别话语调查

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers Pub Date : 2023-10-31 DOI:10.3390/computers12110221
Nirmalya Thakur, Shuqi Cui, Karam Khanna, Victoria Knieling, Yuvraj Nihal Duggal, Mingchen Shao
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引用次数: 0

摘要

本文介绍了对2021年11月9日至2022年7月13日期间Twitter上发布的约5万条有关COVID-19期间在线学习的推文进行综合分析后得出的几项新发现。首先,来自VADER、Afinn和TextBlob的情绪分析结果显示,这些推文中积极的比例更高。性别情感分析的结果表明,在男性和女性之间,对于积极的推文、消极的推文和中性的推文,男性发布的推文比例更高。其次,主观性分析结果显示,最不自以为是、中性自以为是和高度自以为是的推文比例分别为56.568%、30.898%和12.534%。主观性分析的性别结果表明,与男性相比,女性发布的高度固执己见的推文比例更高。然而,与女性相比,男性发布的最不固执己见和中立固执己见的推文比例更高。第三,对推文进行毒性检测,检测不同类别的有毒内容——毒性、淫秽、身份攻击、侮辱、威胁和性暴露。针对这些有毒内容的每个类别,对每个性别发布的推文百分比进行了针对性别的分析,揭示了与男性和女性发布的与在线学习相关的有毒内容的程度、类型、变化和趋势相关的一些新颖见解。第四,计算了在这种情况下男性和女性每月的平均活动。研究结果表明,除了2022年3月,女性的平均活动在所有月份都高于男性。最后,还分析了男性和女性在特定国家的推文模式,提供了多种新颖的见解,例如,在印度,与女性相比,男性在2019冠状病毒病期间发布的关于在线学习的推文比例更高。
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Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis
This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these Tweets were positive. The results of gender-specific sentiment analysis indicate that for positive Tweets, negative Tweets, and neutral Tweets, between males and females, males posted a higher percentage of the Tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated Tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that females posted a higher percentage of highly opinionated Tweets as compared to males. However, males posted a higher percentage of least opinionated and neutral opinionated Tweets as compared to females. Third, toxicity detection was performed on the Tweets to detect different categories of toxic content—toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of Tweets posted by each gender for each of these categories of toxic content revealed several novel insights related to the degree, type, variations, and trends of toxic content posted by males and females related to online learning. Fourth, the average activity of males and females per month in this context was calculated. The findings indicate that the average activity of females was higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India, a higher percentage of the Tweets about online learning during COVID-19 were posted by males as compared to females.
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来源期刊
Computers
Computers COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.40
自引率
3.60%
发文量
153
审稿时长
11 weeks
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