基于社会媒体的城市人群感知：基于Transformer和循环神经网络的实证研究
Urban Crowdsensing using Social Media: An Empirical Study on Transformer and Recurrent Neural Networks
摘要：
城市规划的一个重要方面是了解不同位置的人群水平，这通常需要使用物理传感器。这样的传感器在大规模实施时可能是昂贵且耗时的。为了解决这个问题，我们利用公开的社交媒体数据集，并将其作为两个城市感知问题的基础，即事件检测和人群水平预测。这项工作的一个主要贡献是我们从Twitter和Flickr收集的数据集，以及地面实况事件。我们用两种初步的监督学习方法证明了这个数据集的有用性：首先，一系列神经网络模型来确定社交媒体帖子是否与事件相关，其次是一个回归模型，使用社交媒体帖子计数来预测实际人群水平。我们讨论这些任务的初步结果，并强调一些挑战。
英文摘要：
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.
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