EviDense：一种基于图的高影响事件发现方法
EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions
摘要：
尽管近年来研究界做出了重大努力，但从社交媒体自动获取有关高影响力事件的有价值信息仍然具有挑战性。我们提出了EviDense，一种基于图形的方法，用于在社交媒体中查找高影响力事件（如灾难事件）。我们在工作中面临的挑战之一是为每个事件提供一个简洁的基于关键字的描述，包含最相关的信息，例如发生了什么，位置以及时间范围。我们使用众包平台，在19个月内发布的大量推文上评估我们的方法。我们的评估表明，我们的方法优于国家的最先进的方法相同的问题，在具有更高的精度，更低的重复次数，并提出了一个基于关键字的描述，是简洁和信息。我们进一步改善我们的算法的结果，纳入来自主流媒体的新闻。这项工作的初步版本在ICWSM 2018上以4页的简短论文形式提交。
英文摘要：
Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging. We present EviDense, a graph-based approach for finding high-impact events (such as disaster events) in social media. One of the challenges we address in our work is to provide for each event a succinct keyword-based description, containing the most relevant information about it, such as what happened, the location, as well as its timeframe. We evaluate our approach on a large collection of tweets posted over a period of 19 months, using a crowdsourcing platform. Our evaluation shows that our method outperforms state-of-the-art approaches for the same problem, in terms of having higher precision, lower number of duplicates, and presenting a keyword-based description that is succinct and informative. We further improve the results of our algorithm by incorporating news from mainstream media. A preliminary version of this work was presented as a 4-pages short paper at ICWSM 2018.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
