Near Real-Time Social Distance Estimation in London

Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information

Access to accurate, granular, and up-to-date poverty data is essential for humanitarian organizations to identify vulnerable areas for poverty alleviation efforts. Recent works have shown success in combining computer vision and satellite imagery for poverty estimation; however, the cost of acquiring high-resolution images coupled with black box models can be a barrier to adoption for many development organizations. In this study, we present a interpretable and cost-efficient approach to poverty estimation using machine learning and readily accessible data sources including social media data, …

面向不确定性的社交媒体虚假/误导性信息 prevalence 估计

Uncertainty-Aware Estimation of Mis/Disinformation Prevalence on Social Media

本研究提出了一种面向不确定性的社交媒体虚假信息 prevalence 估计方法，通过量化样本、标注和数据检索不确定性，提高分析的稳健性。

点赞与碎片：考察人们对在 TikTok 上花费时间的感知

Likes and Fragments: Examining Perceptions of Time Spent on TikTok

本文通过对比255名用户自我报告与服务器记录的TikTok使用时间，研究用户人口统计特征和平台参与度如何影响其时间感知与估计准确性，为数字媒体时间估计研究识别了新的影响因素。

Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic Cameras

Displaying near-real-time traffic information is a useful feature of digital navigation maps. However, most commercial providers rely on privacy-compromising measures such as deriving location information from cellphones to estimate traffic. The lack of an open-source traffic estimation method using open data platforms is a bottleneck for building sophisticated navigation services on top of OpenStreetMap (OSM). We propose a deep learning-based Complex Event Processing (CEP) method that relies on publicly available video camera streams for traffic estimation. The proposed framework performs nea…