论文标题
对预测社交媒体时间的基准的实验评估
Experimental Evaluation of Baselines for Forecasting Social Media Timeseries
论文作者
论文摘要
在许多情况下，从了解趋势（例如哪些主题可能会吸引更多用户）到确定异常行为，例如协调的信息操作或PumpnDump努力，可以预测社交媒体活动在许多情况下都可以使用。为了评估一种新的预测方法，重要的是要拥有以评估绩效提高的基准。我们通过实验评估了几个社交媒体数据集中的四个基线的预测活动的性能，它们记录了与在两个不同平台Twitter和Twitter和YouTube上同时进行的三种不同地理政治环境有关的讨论。实验是在小时的时间段内进行的。我们的评估确定了对于特定指标最准确的基准，因此为社交媒体建模中的未来工作提供了指导。
Forecasting social media activity can be of practical use in many scenarios, from understanding trends, such as which topics are likely to engage more users in the coming week, to identifying unusual behavior, such as coordinated information operations or PumpNDump efforts. To evaluate a new approach to forecasting, it is important to have baselines against which to assess performance gains. We experimentally evaluate the performance of four baselines for forecasting activity in several social media datasets that record discussions related to three different geo-political contexts synchronously taking place on two different platforms, Twitter and YouTube. Experiments are done over hourly time periods. Our evaluation identifies the baselines which are most accurate for particular metrics and thus provide guidance for future work in social media modeling.
