揭示围绕COVID-19大流行的阿拉伯语推文中的主题和情感:主题建模和情感分析方法。

IF 2.3 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2025-02-10 DOI:10.2196/53434
Farah Alshanik, Rawand Khasawneh, Alaa Dalky, Ethar Qawasmeh
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引用次数: 0

摘要

背景:2019冠状病毒病大流行在世界范围内产生了深远影响,阿拉伯世界也未能幸免。在这种背景下,在这场全球危机中,Twitter等社交媒体平台已成为分享信息和表达公众意见的必要工具。仔细研究与COVID-19相关的阿拉伯语推文,可以为形成关于COVID-19大流行的讨论的共同话题和潜在情绪提供宝贵的见解。目的:本研究旨在了解阿拉伯语国家Twitter用户对COVID-19大流行的关注和感受。这是通过分析有关COVID-19大流行的阿拉伯语推文表达的主题和情绪来实现的。方法:本研究对2020年3月1日至3月31日期间发布的100万条关于COVID-19的阿拉伯语推文进行分析。机器学习技术,如主题建模和情感分析,被用于理解这些推文中表达的主要主题和情感。结果:对阿拉伯语推文的分析揭示了与COVID-19相关的几个突出话题。分析确定并分组了16个不同的对话话题,这些话题分为八个主题:(1)预防措施和安全,(2)医疗和卫生保健方面,(3)政府和社会措施,(4)影响和数字,(5)疫苗开发和研究,(6)COVID-19与宗教习俗,(7)COVID-19对体育和国家的全球影响,以及(8)COVID-19与国家努力。在所有确定的话题中,关于COVID-19传播的普遍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,当讨论新的COVID-19病例和死亡人数时,与其他话题相比,公开推文显示出明显增强的愤怒感。结论:该研究为了解与COVID-19相关的阿拉伯语推文中表达的主题和情绪提供了有价值的见解。它显示了社交媒体平台,特别是推特,在2019冠状病毒病大流行期间捕捉阿拉伯语社区关切和情绪的重要性。研究结果有助于更深入地理解主流话语,使利益相关者能够制定有效的沟通策略,解决具体的公众关切。本研究强调了监测阿拉伯语社交媒体对话对于支持2019冠状病毒病大流行期间的公共卫生工作和危机管理的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach.

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.

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