论文标题
在推文中检测立场：基于网络的签名方法
Detecting Stance in Tweets : A Signed Network based Approach
论文作者
论文摘要
确定与政治事件相关的用户立场有多种应用，例如确定个人立场，塑造公众舆论，确定政府措施的普及以及许多其他措施。在社交媒体平台上进行的大量政治讨论，例如Twitter，为开发自动化机制提供了机会，以识别单个立场，然后随后扩展到大量用户。但是，诸如短文和推文词汇中的巨大差异之类的问题非常困难。现有的立场检测算法需要特定事件的培训数据或带注释的Twitter手柄，因此很难适应新事件。在本文中，我们提出了一个基于符号网络的框架，该框架使用外部信息源，例如新闻文章来创建有关新闻事件的相关实体网络，然后随后使用该网络来检测任何推文对活动的立场。与现有的立场检测方法相比，与10个事件相关的5,000个推文的验证表明，F1得分的平均得分超过6.5％。
Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require either event specific training data or annotated twitter handles and therefore, are difficult to adapt to new events. In this paper, we propose a sign network based framework that use external information sources, like news articles to create a signed network of relevant entities with respect to a news event and subsequently use the same to detect stance of any tweet towards the event. Validation on 5,000 tweets related to 10 events indicates that the proposed approach can ensure over 6.5% increase in average F1 score compared to the existing stance detection approaches.
