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.

翻译：随着人类互动向线上转移，用户影响力等级的识别已引起广泛关注。具有影响力的用户能够通过左右他人观点以实现特定目标。因此，预测用户的影响力等级有助于理解社交网络、预测趋势、遏制虚假信息传播等。然而，用户影响力预测是一个具有挑战性的问题，因为影响力的概念往往因情境或领域而异，且用户交流通常仅限于文本形式。在本研究中，我们将用户影响力等级定义为社群认可度的函数，并构建了一个通过融合人口统计学与人格特征数据显著超越基线性能的预测模型。该方法在八个不同领域中均能持续提升RankDCG评分。