{"title":"LOOM:在Twitter数据中展示幂律的动态","authors":"Maryanne Doyle, Mark T. Keane","doi":"10.1109/iV.2017.50","DOIUrl":null,"url":null,"abstract":"LOOM is advanced as a new visualisation for changes in ranks and trends in power-law data that is changing dynamically over time. A comparison between LOOM and existing methods for visualising such data (e.g., time-series graphs, typical analytics dashboards). Several exemplar data sets are shown, using LOOM, drawn from the tracking of news stories on Twitter. The basis for the LOOM visualisation is elaborated and it is shown how it avoids the pitfalls arising in other line-graph representations.","PeriodicalId":410876,"journal":{"name":"2017 21st International Conference Information Visualisation (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LOOM: Showing the Dynamics of Power Laws in Twitter Data\",\"authors\":\"Maryanne Doyle, Mark T. Keane\",\"doi\":\"10.1109/iV.2017.50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"LOOM is advanced as a new visualisation for changes in ranks and trends in power-law data that is changing dynamically over time. A comparison between LOOM and existing methods for visualising such data (e.g., time-series graphs, typical analytics dashboards). Several exemplar data sets are shown, using LOOM, drawn from the tracking of news stories on Twitter. The basis for the LOOM visualisation is elaborated and it is shown how it avoids the pitfalls arising in other line-graph representations.\",\"PeriodicalId\":410876,\"journal\":{\"name\":\"2017 21st International Conference Information Visualisation (IV)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 21st International Conference Information Visualisation (IV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iV.2017.50\",\"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 21st International Conference Information Visualisation (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iV.2017.50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LOOM: Showing the Dynamics of Power Laws in Twitter Data
LOOM is advanced as a new visualisation for changes in ranks and trends in power-law data that is changing dynamically over time. A comparison between LOOM and existing methods for visualising such data (e.g., time-series graphs, typical analytics dashboards). Several exemplar data sets are shown, using LOOM, drawn from the tracking of news stories on Twitter. The basis for the LOOM visualisation is elaborated and it is shown how it avoids the pitfalls arising in other line-graph representations.
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