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揭示围绕新冠疫情的阿拉伯语推文的主题与情感:主题建模与情感分析方法

Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach.

作者信息

Alshanik Farah, Khasawneh Rawand, Dalky Alaa, Qawasmeh Ethar

机构信息

Department of Computer Science, Faculty of Computer and Information Technology, Jordan University of Science and Technology, Irbid, Jordan.

Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan.

出版信息

JMIR Infodemiology. 2025 Feb 10;5:e53434. doi: 10.2196/53434.

Abstract

BACKGROUND

The worldwide effects of the COVID-19 pandemic have been profound, and the Arab world has not been exempt from its wide-ranging consequences. Within this context, social media platforms such as Twitter have become essential for sharing information and expressing public opinions during this global crisis. Careful investigation of Arabic tweets related to COVID-19 can provide invaluable insights into the common topics and underlying sentiments that shape discussions about the COVID-19 pandemic.

OBJECTIVE

This study aimed to understand the concerns and feelings of Twitter users in Arabic-speaking countries about the COVID-19 pandemic. This was accomplished through analyzing the themes and sentiments that were expressed in Arabic tweets about the COVID-19 pandemic.

METHODS

In this study, 1 million Arabic tweets about COVID-19 posted between March 1 and March 31, 2020, were analyzed. Machine learning techniques, such as topic modeling and sentiment analysis, were applied to understand the main topics and emotions that were expressed in these tweets.

RESULTS

The analysis of Arabic tweets revealed several prominent topics related to COVID-19. The analysis identified and grouped 16 different conversation topics that were organized into eight themes: (1) preventive measures and safety, (2) medical and health care aspects, (3) government and social measures, (4) impact and numbers, (5) vaccine development and research, (6) COVID-19 and religious practices, (7) global impact of COVID-19 on sports and countries, and (8) COVID-19 and national efforts. Across all the topics identified, the prevailing sentiments regarding the spread of COVID-19 were primarily centered around anger, followed by disgust, joy, and anticipation. Notably, when conversations revolved around new COVID-19 cases and fatalities, public tweets revealed a notably heightened sense of anger in comparison to other subjects.

CONCLUSIONS

The study offers valuable insights into the topics and emotions expressed in Arabic tweets related to COVID-19. It demonstrates the significance of social media platforms, particularly Twitter, in capturing the Arabic-speaking community's concerns and sentiments during the COVID-19 pandemic. The findings contribute to a deeper understanding of the prevailing discourse, enabling stakeholders to tailor effective communication strategies and address specific public concerns. This study underscores the importance of monitoring social media conversations in Arabic to support public health efforts and crisis management during the COVID-19 pandemic.

摘要

背景

新冠疫情的全球影响深远,阿拉伯世界也未能免受其广泛后果的影响。在此背景下,推特等社交媒体平台在这场全球危机期间对于信息共享和表达公众意见变得至关重要。仔细研究与新冠疫情相关的阿拉伯语推文能够为塑造有关新冠疫情讨论的常见话题和潜在情绪提供宝贵见解。

目的

本研究旨在了解阿拉伯语国家推特用户对新冠疫情的担忧和感受。这是通过分析关于新冠疫情的阿拉伯语推文中表达的主题和情绪来实现的。

方法

在本研究中,分析了2020年3月1日至3月31日期间发布的100万条关于新冠疫情的阿拉伯语推文。应用了机器学习技术,如主题建模和情感分析,以了解这些推文中表达的主要主题和情绪。

结果

对阿拉伯语推文的分析揭示了几个与新冠疫情相关的突出主题。分析识别并归类了16个不同的对话主题,这些主题被组织成八个主题:(1)预防措施与安全,(2)医疗与卫生保健方面,(3)政府与社会措施,(4)影响与数据,(5)疫苗研发与研究,(6)新冠疫情与宗教活动,(7)新冠疫情对体育和国家的全球影响,以及(8)新冠疫情与国家努力。在所有确定的主题中,关于新冠疫情传播的主要情绪主要集中在愤怒上,其次是厌恶、喜悦和期待。值得注意的是,当对话围绕新冠疫情的新病例和死亡人数展开时,与其他主题相比,公众推文显示出明显更高的愤怒情绪。

结论

该研究为与新冠疫情相关的阿拉伯语推文中表达的主题和情绪提供了宝贵见解。它展示了社交媒体平台,特别是推特,在捕捉新冠疫情期间阿拉伯语社区的担忧和情绪方面的重要性。这些发现有助于更深入地理解当前的话语,使利益相关者能够制定有效的沟通策略并解决特定的公众关切。本研究强调了监测阿拉伯语社交媒体对话对于支持新冠疫情期间的公共卫生努力和危机管理的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6860/11851025/a8fd03dea022/infodemiology_v5i1e53434_fig1.jpg

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