论文标题
调查用户激进化：一种用于识别意见细粒度的时间变化的新型数据集
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion
论文作者
论文摘要
人们对社交媒体用户的细粒度转变的建模能力越来越需要，因为人们对潜在的两极分化社会影响的担忧增加了。但是，缺乏适合该任务的公开可用数据集提出了一个重大挑战。在本文中，我们介绍了一个创新的注释数据集，用于建模微妙的意见波动并检测细粒度的立场。该数据集包括随着时间的时间和整个对话线程，每个用户都包含足够数量的立场极性和强度标签，从而使长期和短期内可检测到的微妙意见波动。所有帖子均由非专家注释，并且专家还注释了数据的很大一部分。我们为招募合适的非专家提供了一种策略。我们对通知协议的分析表明，从非专家的多数投票获得的结果注释与专家的注释相当。我们在短期和长期水平上提供了立场演变的分析，对具有动态态度和坚果态度的用户之间语言使用的比较以及细粒度的立场检测基线。
There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle opinion fluctuations and detecting fine-grained stances. The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term. All posts are annotated by non-experts and a significant portion of the data is also annotated by experts. We provide a strategy for recruiting suitable non-experts. Our analysis of the inter-annotator agreements shows that the resulting annotations obtained from the majority vote of the non-experts are of comparable quality to the annotations of the experts. We provide analyses of the stance evolution in short term and long term levels, a comparison of language usage between users with vacillating and resolute attitudes, and fine-grained stance detection baselines.
