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首页> 外文会议>2017 ACM/IEEE Joint Conference on Digital Libraries >Local Memory Project: Providing Tools to Build Collections of Stories for Local Events from Local Sources
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Local Memory Project: Providing Tools to Build Collections of Stories for Local Events from Local Sources

机译：本地记忆项目：提供工具以构建来自本地来源的本地事件的故事集

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The national (non-local) news media has different priorities than the local news media. If one seeks to build a collection of stories about local events, the national news media may be insufficient, with the exception of local news which "bubbles" up to the national news media. If we rely exclusively on national media, or build collections exclusively on their reports, we could be late to the important milestones which precipitate major local events, thus, run the risk of losing important stories due to link rot and content drift. Consequently, it is important to consult local sources affected by local events. Our goal is to provide a suite of tools (beginning with two) under the umbrella of the Local Memory Project (LMP) to help users and small communities discover, collect, build, archive, and share collections of stories for important local events by leveraging local news sources. The first service (Geo) returns a list of local news sources (newspaper, TV and radio stations) in order of proximity to a user-supplied zip code. The second service (Local Stories Collection Generator) discovers, collects and archives a collection of news stories about a story or event represented by a user-supplied query and zip code pair. We evaluated 20 pairs of collections, Local (generated by our system) and non-Local, by measuring archival coverage, tweet index rate, temporal range, precision, and sub-collection overlap. Our experimental results showed Local and non-Local collections with archive rates of 0.63 and 0.83, respectively, and tweet index rates of 0.59 and 0.80, respectively. Local collections produced older stories than non-Local collections, at a higher precision (relevance) of 0.84 compared to a non-Local precision of 0.72. These results indicate that Local collections are less exposed, thus less popular than their non-Local counterpart.
机译：国家（非本地）新闻媒体的优先级与本地新闻媒体的优先级不同。如果要收集有关本地事件的故事，国家新闻媒体可能是不够的，除了本地新闻会“冒充”国家新闻媒体。如果我们仅依靠国家媒体，或仅根据他们的报道来建立馆藏，我们可能迟到了引发重大本地事件的重要里程碑，因此冒着因链接腐烂和内容漂移而丢失重要故事的风险。因此，重要的是咨询受当地事件影响的当地资源。我们的目标是在本地内存项目（LMP）的保护下提供一套工具（从两个开始），以帮助用户和小型社区利用重要的本地事件来发现，收集，构建，存档和共享故事的集合。当地新闻来源。第一个服务（Geo）按与用户提供的邮政编码接近的顺序返回本地新闻来源（报纸，电视和广播电台）的列表。第二项服务（本地故事收集生成器）发现，收集和存档有关由用户提供的查询和邮政编码对表示的故事或事件的新闻故事的集合。通过测量档案覆盖率，tweet索引率，时间范围，精度和子集合重叠，我们评估了20对集合（本地（由我们的系统生成）和非本地）。我们的实验结果显示，本地和非本地集合的存档率分别为0.63和0.83，推特索引率分别为0.59和0.80。与非本地收藏相比，本地收藏产生的故事比非本地收藏的故事更老，其精确度（相关性）为0.84，而非本地收藏的精确度为0.72。这些结果表明，本地收藏的曝光率较低，因此与非本地收藏相比，人气较低。

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