Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for intelligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experimental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing understanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence.

社交媒体网络 (SMN) 越来越多地用于知识工作者和相关资产的专业管理。然而，影响这些网络中行为趋势和活动水平的因素尚不清楚。尽管社会和认知理论可以帮助解释传统社交网络中的人类行为，但它们在 SMN 中的应用尚未得到验证。传统的社交网络建模技术可能无法准确预测现实世界的 SMN 活动。这项研究开发了一个用于 SMN 中的情报提取的时间图框架。基于该框架开发了基于理论的数据驱动模型（从众模型（COM）、新近度至上模型（REM）、趋势交互模型（TIM）、周期交互模型（PIM））以捕获用户行为的各个方面：从众效应，新近性，首要性，周期性，和动态趋势。这些模型捕获活动历史并动态组合定价信息以提高预测准确性。本文使用 83,536 个关于加密货币的 GitHub 软件存储库的数据，报告了比较模型在预测 SMN 活动随时间变化方面的性能的实验结果。实验结果表明，捕获人类认知处理的新近度/首要效应的模型 (REM) 在与参与度、贡献、影响力和受欢迎程度有关的 9 个（共 18 个）度量中优于其他模型。首要性在预测参与度、贡献和受欢迎程度方面起着主导作用，而新近度在预测影响力方面起着关键作用。发现短期趋势（使用 TIM 建模）在预测用户贡献方面产生明显更好的性能。在大多数方面，这些模型的表现也优于集成机器学习 (IML) 模型。总体而言，REM 和 TIM 建模的效果被发现比 COM、PIM 和 IML 建模的效果更显着。该研究有助于加强对 SMN 行为的理解，开发新模型来模拟和预测 SMN 活动，并为信息系统从业者设计新的工件来管理知识资产和提取 SMN 智能。