Identifying Policy Agenda Sub-Topics in Political Tweets based on Community Detection
Identifying Policy Agenda Sub-Topics in Political Tweets based on Community Detection
复制标题
基于社区检测识别政治推文中的政策议程子主题
DOI：
10.1145/3110025.3116208
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Peterson, David A.
中科院分区：
文献类型：
--
作者：
Iyer, Rohit;Wong, Johnny;Tavanapong, Wallapak;Peterson, David A.
twitter在政治领域的爆炸性使用为追踪联邦和州一级的政治对话提供了新的途径。州和联邦政府机构使用推文向公民提供有关未来和当前政策的信息。它也被政治候选人用来表达他们对政策变化的看法，法律和竞选立法机构选举，最近的例子是2016年美国总统选举。在本文中，我们使用监督学习，文本语义相似性和社区检测技术，发现积极讨论的政策议程子主题在一定时间内的政治推文。具体来说，我们的目标推文有关的国家代表在美国发布的主要政策议程，试图辨别他们使用他们的Twitter帐户解决的主要政策子主题。使用我们的方法，我们展示了我们如何实现高精度的主题召回和顺序召回，通过比较我们提出的方法与子主题注释领域专家完成的输出。
The explosive use of twitter in the political landscape presents new avenues for tracking political conversations at federal and state level. Tweets are used by state and federal government bodies to present citizens with information about future and present policies. It is also used by political candidates to express their views on policy changes, laws and to campaign for legislative body elections, the most recent example being the 2016 US presidential elections. In this paper, we use supervised learning, textual semantic similarity and community detection techniques to find actively discussed policy agenda sub-topics among political tweets within a certain time period. Specifically, we target tweets pertaining to major policy agendas published by state representatives in US, to try and discern the major policy sub-topics that they address using their twitter accounts. Using our method, we demonstrate how we achieve a high accuracy in terms of Topic Recall and Order Recall, by comparing the output of our proposed method with sub-topic annotations done by domain experts.
密码书
DOI：
10.1080/10724117.2006.11974652
发表时间：
2006
期刊：
Math Horizons
影响因子：
--
作者：
N. S. Clair
通讯作者：
N. S. Clair
影响因子：
22.7
作者：
MILLER, GA
通讯作者：
MILLER, GA