分析 COVID-19 大流行期间与自杀有关的推文

K.D.S. Balasooriya, R. Rupasingha, B. Kumara
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引用次数: 0

摘要

COVID-19 病毒始于 2019 年,严重影响了世界上许多国家的各个领域。在此基础上,经济困难、失去亲人、突发危险、人际关系、家庭纠纷、心理困扰等问题增多,人们无法正常地生活,有些人甚至产生了自杀的动机。我们通过 Twitter API(应用程序接口)收集了 9750 条原始数据。我们通过 Twitter API(应用程序接口)收集了 9750 条原始数据,经过 TF-IDF(词频-反向文档频率)预处理和特征提取后,我们应用 LDA(潜在德里希特分配)和概率潜在语义分析(PLSA)主题建模算法识别主题。我们使用主题间距离图(Intertopic DistanceMap)、最显著术语(Most Salient Terms)和词云可视化(Word Clouds Visualization)来检验结果。一致性分数(coherencescore)和令人困惑值(perplexing value)用于衡量提取的主题对人类的可解释性。PLSA 还提取了 25 个主题及其概率,并使用 Kullback-Leibler (KL) 分歧来检验结果。专家反馈证明,LDA 的结果优于 PLSA。在此基础上,我们发现了 COVID-19 对人类生活的主要影响,在此期间人类情感发生了哪些积极和消极的变化,人们使用了哪些支持和宣传方法,以及他们更喜欢哪些方法。这样,负责任的当局和个人就可以采取必要的措施来预防、减少和应对未来的自杀事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Suicide-related Tweets During the COVID-19 Pandemic
The COVID-19 virus started in 2019 and badly affected the different sectors of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden anger, relationships, family disputes, and psychological distress increased, and individuals were stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated to commit suicide. It is important to reduce the number of suicides and identify the reasons for this situation. Through this research, the focus is on identifying the main topics discussed relevant to suicides during the COVID-19 pandemic. Individuals use Twitter, a social media platform, to share their ideas freely and publically. We collected 9750 primary data through Twitter API (Application Programming Interface). After preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA) topic modeling algorithms to identify topics. Based on the LDA results, we extracted ten different topics under the three themes, such as the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence score and perplexing value are used to measure how interpretable the extracted topics are to humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence was used to check the results. We were able to gain insight into human emotions and the main motivations behind suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human feelings were changed positively and negatively during that period, what supporting and awareness methods people used, and what they preferred. The required measures can then be taken by those responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident in the future.
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