{"title":"与灾害相关的媒体文章中的词汇关联——一项统计分析","authors":"M. Pirnau","doi":"10.1109/SPED.2017.7990427","DOIUrl":null,"url":null,"abstract":"The paper aims to analyze the frequency of the posts in case of earthquakes and of the word associations included in such Social Media (SM) posts. Since important posts are shared by users in SM, the purpose was to identify the variation of a number of posts having unique content that occurred over a period of time in Social Media for a particular topic. The present study uses messages generated by the Twitter platform, which had been posted before and after the occurrence of the earthquakes in the areas with important seismic activity, such as Vrancea (24th September 2016), Ussita (30th October 2016), New Zealand (13th November 2016) and Papua (23rd January 2017). For the analysis of the contents of the tweets, the A-priori algorithm was used to extract words associations from these posts, keywords that draw attention to the analyzed earthquake situation.","PeriodicalId":345314,"journal":{"name":"2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Word associations in media posts related to disasters — A statistical analysis\",\"authors\":\"M. Pirnau\",\"doi\":\"10.1109/SPED.2017.7990427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper aims to analyze the frequency of the posts in case of earthquakes and of the word associations included in such Social Media (SM) posts. Since important posts are shared by users in SM, the purpose was to identify the variation of a number of posts having unique content that occurred over a period of time in Social Media for a particular topic. The present study uses messages generated by the Twitter platform, which had been posted before and after the occurrence of the earthquakes in the areas with important seismic activity, such as Vrancea (24th September 2016), Ussita (30th October 2016), New Zealand (13th November 2016) and Papua (23rd January 2017). For the analysis of the contents of the tweets, the A-priori algorithm was used to extract words associations from these posts, keywords that draw attention to the analyzed earthquake situation.\",\"PeriodicalId\":345314,\"journal\":{\"name\":\"2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPED.2017.7990427\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPED.2017.7990427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Word associations in media posts related to disasters — A statistical analysis
The paper aims to analyze the frequency of the posts in case of earthquakes and of the word associations included in such Social Media (SM) posts. Since important posts are shared by users in SM, the purpose was to identify the variation of a number of posts having unique content that occurred over a period of time in Social Media for a particular topic. The present study uses messages generated by the Twitter platform, which had been posted before and after the occurrence of the earthquakes in the areas with important seismic activity, such as Vrancea (24th September 2016), Ussita (30th October 2016), New Zealand (13th November 2016) and Papua (23rd January 2017). For the analysis of the contents of the tweets, the A-priori algorithm was used to extract words associations from these posts, keywords that draw attention to the analyzed earthquake situation.
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