{"title":"分析 COVID-19 大流行期间与自杀有关的推文","authors":"K.D.S. Balasooriya, R. Rupasingha, B. Kumara","doi":"10.2174/0126667975296097240321060634","DOIUrl":null,"url":null,"abstract":"\n\nThe COVID-19 virus started in 2019 and badly affected the different sectors\nof many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden\nanger, relationships, family disputes, and psychological distress increased, and individuals were\nstalled from carrying out their lifestyle in a normal way, and some individuals were even motivated\nto commit suicide.\n\n\n\nIt is important to reduce the number of suicides and identify the reasons for this situation.\nThrough this research, the focus is on identifying the main topics discussed relevant to suicides during\nthe COVID-19 pandemic.\n\n\n\nIndividuals use Twitter, a social media platform, to share their ideas freely and publically.\nWe collected 9750 primary data through Twitter API (Application Programming Interface). After\npreprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we\napplied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA)\ntopic modeling algorithms to identify topics.\n\n\n\nBased on the LDA results, we extracted ten different topics under the three themes, such as\nthe impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance\nMap, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence\nscore and perplexing value are used to measure how interpretable the extracted topics are to\nhumans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence\nwas used to check the results.\n\n\n\nWe were able to gain insight into human emotions and the main motivations behind\nsuicide attempts using the topics we extracted. Expert feedback proved that LDA results were better\nthan PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human\nfeelings were changed positively and negatively during that period, what supporting and awareness\nmethods people used, and what they preferred. The required measures can then be taken by those\nresponsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident\nin the future.\n","PeriodicalId":10815,"journal":{"name":"Coronaviruses","volume":" 67","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of Suicide-related Tweets During the COVID-19 Pandemic\",\"authors\":\"K.D.S. Balasooriya, R. Rupasingha, B. Kumara\",\"doi\":\"10.2174/0126667975296097240321060634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nThe COVID-19 virus started in 2019 and badly affected the different sectors\\nof many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden\\nanger, relationships, family disputes, and psychological distress increased, and individuals were\\nstalled from carrying out their lifestyle in a normal way, and some individuals were even motivated\\nto commit suicide.\\n\\n\\n\\nIt is important to reduce the number of suicides and identify the reasons for this situation.\\nThrough this research, the focus is on identifying the main topics discussed relevant to suicides during\\nthe COVID-19 pandemic.\\n\\n\\n\\nIndividuals use Twitter, a social media platform, to share their ideas freely and publically.\\nWe collected 9750 primary data through Twitter API (Application Programming Interface). After\\npreprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we\\napplied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA)\\ntopic modeling algorithms to identify topics.\\n\\n\\n\\nBased on the LDA results, we extracted ten different topics under the three themes, such as\\nthe impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance\\nMap, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence\\nscore and perplexing value are used to measure how interpretable the extracted topics are to\\nhumans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence\\nwas used to check the results.\\n\\n\\n\\nWe were able to gain insight into human emotions and the main motivations behind\\nsuicide attempts using the topics we extracted. Expert feedback proved that LDA results were better\\nthan PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human\\nfeelings were changed positively and negatively during that period, what supporting and awareness\\nmethods people used, and what they preferred. The required measures can then be taken by those\\nresponsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident\\nin the future.\\n\",\"PeriodicalId\":10815,\"journal\":{\"name\":\"Coronaviruses\",\"volume\":\" 67\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Coronaviruses\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0126667975296097240321060634\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Coronaviruses","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0126667975296097240321060634","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.