{"title":"使用维基百科探索长时间运行的新闻故事","authors":"Jaspreet Singh, Abhijith Anand, Vinay Setty, Avishek Anand","doi":"10.1145/2786451.2786489","DOIUrl":null,"url":null,"abstract":"A significant portion of today's news articles are part of long running stories. To better understand the context of these stories journalists, social scientists and other scholars use news collections to find temporal and topical insights. However these insights are devoid of user impressions, derived from click-through data and query logs, and are only reliable if the collection is complete and consistent. In this work we introduce the notion of combining user impressions from Wikipedia with news collection based insights for long running news story exploration and outline promising new research directions. We also demonstrate our initial attempts with a prototype system called NewsEX.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"50 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Exploring Long Running News Stories using Wikipedia\",\"authors\":\"Jaspreet Singh, Abhijith Anand, Vinay Setty, Avishek Anand\",\"doi\":\"10.1145/2786451.2786489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A significant portion of today's news articles are part of long running stories. To better understand the context of these stories journalists, social scientists and other scholars use news collections to find temporal and topical insights. However these insights are devoid of user impressions, derived from click-through data and query logs, and are only reliable if the collection is complete and consistent. In this work we introduce the notion of combining user impressions from Wikipedia with news collection based insights for long running news story exploration and outline promising new research directions. We also demonstrate our initial attempts with a prototype system called NewsEX.\",\"PeriodicalId\":93136,\"journal\":{\"name\":\"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference\",\"volume\":\"50 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2786451.2786489\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2786451.2786489","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploring Long Running News Stories using Wikipedia
A significant portion of today's news articles are part of long running stories. To better understand the context of these stories journalists, social scientists and other scholars use news collections to find temporal and topical insights. However these insights are devoid of user impressions, derived from click-through data and query logs, and are only reliable if the collection is complete and consistent. In this work we introduce the notion of combining user impressions from Wikipedia with news collection based insights for long running news story exploration and outline promising new research directions. We also demonstrate our initial attempts with a prototype system called NewsEX.
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