Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction
Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction
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DOI：
10.1145/3132847.3133020
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发表时间：
2017-11
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影响因子：
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通讯作者：
Rui Dong;Yizhou Sun;Lu Wang-;Yupeng Gu;Yuan Zhong
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文献类型：
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作者：
Rui Dong;Yizhou Sun;Lu Wang-;Yupeng Gu;Yuan Zhong
社交媒体网站已经成为网络用户就枪支管制和堕胎等争议性问题发表意见的热门渠道。了解用户的立场和他们的论点是决策过程和公众审议的关键任务。现有的方法依赖于大量的人工注释来预测感兴趣的问题的立场，这是昂贵的，难以扩展到新的问题。在这项工作中，我们提出了一个弱引导的用户姿态建模框架，该框架同时考虑两种类型的信息：你说什么（通过基于姿态的内容生成模型）和你如何行为（通过基于社交互动的图正则化）。我们实验了两种类型的社交媒体数据：新闻评论和论坛帖子。我们的模型在对新闻评论上未见过的用户进行基于立场的链接预测方面，一致优于基于逻辑回归的监督方法。与最先进的监督系统相比，我们的方法也为论坛用户实现了更好或相当的姿态预测性能。同时，为对立立场的用户学习单独的单词分布。这可能有助于更好地理解和解释有争议问题的相互矛盾的论点。
Social media websites have become a popular outlet for online users to express their opinions on controversial issues, such as gun control and abortion. Understanding users' stances and their arguments is a critical task for policy-making process and public deliberation. Existing methods rely on large amounts of human annotation for predicting stance on issues of interest, which is expensive and hard to scale to new problems. In this work, we present a weakly-guided user stance modeling framework which simultaneously considers two types of information: what do you say (via stance-based content generative model) and how do you behave (via social interaction-based graph regularization). We experiment with two types of social media data: news comments and discussion forum posts. Our model uniformly outperforms a logistic regression-based supervised method on stance-based link prediction for unseen users on news comments. Our method also achieves better or comparable stance prediction performance for discussion forum users, when compared with state-of-the-art supervised systems. Meanwhile, separate word distributions are learned for users of opposite stances. This potentially helps with better understanding and interpretation of conflicting arguments for controversial issues.