通过社会语言学建模，精准预测用户在不同领域的影响力。
Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling
- 结合人口统计与人格特征，用社区认可度定义影响力。
- 在八个领域中均显著提升排名评估指标表现。
- 适合社交网络分析、舆情监测与虚假信息防范场景。
随着人类互动日益在线化，识别用户影响力成为研究热点。有影响力的用户能左右他人观点以达成目标，因此预测其影响力有助于理解社交网络、预测趋势、防范错误信息传播。然而，影响力建立在具体情境或领域之上，且用户交流受限于文本，使得预测极具挑战。本文将用户影响力定义为社区认可度的函数，并提出一种模型，融合人口统计与人格数据，在八个不同领域中均显著优于基线方法，持续提升 RankDCG 评分。
原文摘要 · Abstract (English)
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.
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