论文标题
使用社交媒体的城市众包：关于变压器和经常性神经网络的实证研究
Urban Crowdsensing using Social Media: An Empirical Study on Transformer and Recurrent Neural Networks
论文作者
论文摘要
城市规划的一个重要方面是了解各个位置的人群水平，这通常需要使用物理传感器。这样的传感器可能是昂贵的，而且很耗时，可以大规模实施。为了解决这个问题，我们利用公开可用的社交媒体数据集并将其用作两个城市传感问题的基础，即事件检测和人群级别的预测。这项工作的一项主要贡献是我们从Twitter和Flickr收集的数据集以及地面真相事件。我们通过两种初步的监督学习方法证明了该数据集的有用性：首先，一系列神经网络模型，以确定社交媒体帖子是否与事件相关，其次是使用社交媒体帖子的回归模型来预测实际人群的水平。我们讨论了这些任务的初步结果，并突出了一些挑战。
An important aspect of urban planning is understanding crowd levels at various locations, which typically require the use of physical sensors. Such sensors are potentially costly and time consuming to implement on a large scale. To address this issue, we utilize publicly available social media datasets and use them as the basis for two urban sensing problems, namely event detection and crowd level prediction. One main contribution of this work is our collected dataset from Twitter and Flickr, alongside ground truth events. We demonstrate the usefulness of this dataset with two preliminary supervised learning approaches: firstly, a series of neural network models to determine if a social media post is related to an event and secondly a regression model using social media post counts to predict actual crowd levels. We discuss preliminary results from these tasks and highlight some challenges.
