Keyword expansion techniques for mining social movement data on social media
Keyword expansion techniques for mining social movement data on social media
复制标题
DOI：
10.1140/epjds/s13688-022-00343-9
复制
发表时间：
2022-05-21
期刊：
影响因子：
3.6
通讯作者：
Budak，Ceren
中科院分区：
文献类型：
--
作者：
Bozarth，Lia;Budak，Ceren
政治和社会科学家一直广泛依赖关键字，如标签，从社交媒体网站，特别是Twitter挖掘社会运动数据。然而，先前的工作表明，不具有代表性的关键字集可能会导致有缺陷的研究结论。许多关键词扩展方法已被提出来增加关键词的全面性，但这些方法一直缺乏系统的评价。我们的论文填补了这一空白。我们评估了五种不同的关键字扩展技术（或管道）在两个不同的活动水平上的五个代表性的社会运动。我们的研究结果指导那些旨在使用社交媒体关键字搜索来挖掘数据的研究人员。例如，我们表明，当运动处于正常活动期时，基于词嵌入的方法显着优于其他更复杂和更新的方法。这些方法的计算强度也较低。更重要的是，我们还观察到，当这些运动处于离线的高峰动员期时，没有一个管道可以识别出略多于一半的运动相关推文。然而，当使用多于一个管道时，覆盖率可以显著增加。即使在随机选择管线时也是如此。
Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random.