微博动态的测量与总结Measuring and Summarizing Movement in Microblog Postings |
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课程网址: | https://videolectures.net/videos/icwsm2013_ruiz_microblog_posting... |
主讲教师: | Eduardo J. Ruiz |
开课单位: | 信息不详。欢迎您在右侧留言补充。 |
开课时间: | 2014-04-03 |
课程语种: | 英语 |
中文简介: | 每天,用户在推特和脸书等流行社交网络平台上发布数亿条微博帖子。当综合考虑时,微博帖子已被证明表现出反映具有全球意义的事件的时间模式。在这篇论文中,我们提出了识别和量化空间模式的技术:例如,一个在某一天在一个城市流行的标签可能会在第二天在另一个城市变得流行。检测这些模式是具有挑战性的,因为数据是嘈杂的,柱子没有物理移动,也就是说,它们不是像太空飞行器那样的连续轨迹。其次,我们引入了一个多粒度摘要模型来描述标签在两个时间段之间的移动。为了可解释性,我们寻求地图上遵循自然或行政边界的空间变化的表示,如城市和州。我们使用定量方法和用户调查来比较各种运动指标。我们通过分析损失和覆盖函数来评估我们的运动摘要方案。我们的结果表明,可以可靠地自动检测相关的空间变化,并生成准确表示这些变化的简单摘要。 |
课程简介: | Every day, users publish hundreds of millions of microblog postings in popular social-networking platforms such as Twitter and Facebook. When considered in aggregation, microblog postings have been shown to exhibit temporal patterns that reflect events of global significance. In this paper, we propose techniques to identify and quantify spatial patterns: for instance, a hashtag that is popular in one city on a given day, may become popular in a different city on the next day. Detecting these patterns is challenging given that the data are noisy and posts are not physically moving, i.e., they are not continuous trajectories in space like vehicles. Second, we introduce a multi-granular summarization model to describe the movement of a hashtag between two time periods. For interpretability, we seek representations of spatial changes that follow natural or administrative boundaries on a map, such as cities and states. We compare various movement measures using quantitative approaches and user surveys. We evaluate our movement summarization schemes by analytical loss and coverage functions. Our results show that it is possible to reliably detect relevant spatial changes automatically, and to produce simple summaries that represent accurately these changes. |
关 键 词: | 社交网络平台; 全球意义; 识别和量化空间模式 |
课程来源: | 视频讲座网 |
数据采集: | 2025-04-24:yuhongrui |
最后编审: | 2025-04-24:yuhongrui |
阅读次数: | 2 |