Twitter is one of the most prominent online media that acts as a global network for sharing sensitive real-time information like earthquake alerts, political news, product review, personality identification, criminal detection etc. along with regular usage, which is why knowing the location of a user in twitter gets at most important even though they do not tend to disclose it. In this paper, we propose a technique to detect the name of the locations for the twitter users. This technique involves a hypergraph-based map-reduce concept to represent the user tweets with their locations. The Helly property of the hypergraph was used to remove less potential words and the Impact Factor measure (IF) was introduced to calculate the score of each location for a particular user. The algorithm (HIF) was implemented in a big data environment provided by Hadoop and found to give an average accuracy of 78% which is well ahead of the existing methodologies. This method gives appreciable results, with high values of precision and recall for all locations.

中文翻译：

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使用全球数据通信推特中的超图影响因子进行用户位置预测

Twitter 是最著名的在线媒体之一，它充当全球网络，用于共享敏感的实时信息，如地震警报、政治新闻、产品评论、人格识别、犯罪侦查等，同时经常使用，这就是为什么要知道位置推特上的用户最重要，即使他们不倾向于透露它。在本文中，我们提出了一种技术来检测 twitter 用户的位置名称。该技术涉及基于超图的 map-reduce 概念来表示用户推文及其位置。超图的 Helly 属性用于删除较少的潜在单词，并引入影响因子测量 (IF) 来计算特定用户的每个位置的分数。该算法 (HIF) 在 Hadoop 提供的大数据环境中实施，发现其平均准确率为 78%，远远领先于现有方法。这种方法给出了可观的结果，所有位置的精度和召回率都很高。