领袖特征：通过社会语言学建模预测用户影响力水平
Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling
AI总结：
本文通过社会语言学建模预测用户影响力水平，利用社区背书定义影响力，并通过人口统计和性格数据提升预测效果，跨八个领域均提升RankDCG分数。
摘要：
识别用户影响力水平在在线互动中备受关注，因为有影响力用户能够影响他人观点以达成目标。因此，预测用户影响力有助于理解社交网络、预测趋势、防止虚假信息等。然而，预测用户影响力具有挑战性，因为影响力概念因情境或领域而异，且用户交流仅限于文本。本文将用户影响力水平定义为社区背书函数，并开发出显著优于基线的模型，通过利用人口统计和性格数据，该方法在八个不同领域中持续提升RankDCG分数。
英文摘要：
Recognition of a user's influence level has attracted much attention as human interactions move online. Influential users have the ability to sway others' opinions to achieve some goals. As a result, predicting users' level of influence can help to understand social networks, forecast trends, prevent misinformation, etc. However, predicting user influence is a challenging problem because the concept of influence is specific to a situation or a domain, and user communications are limited to text. In this work, we define user influence level as a function of community endorsement and develop a model that significantly outperforms the baseline by leveraging demographic and personality data. This approach consistently improves RankDCG scores across eight different domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
