Conditional Density Estimation of Tweet Location: A Feature-Dependent Approach
Twitter-based public health surveillance systems have achieved many successes. Underlying this success, much useful information has been associated with tweets such as temporal and spatial information. For fine-grained investigation of disease propagation, this information is attributed a more important role. Unlike temporal information that is always available, spatial information is less available because of privacy concerns. To extend the availability of spatial information, many geographic identification systems have been developed. However, almost no origin of the user location can be identified, even if a human reads the tweet contents. This study estimates the geographic origin of tweets with reliability using a density estimation approach. Our method reveals how the model interprets the origin of user location according to the spread of estimated density.
Social Media Geographic Mapping Disease Outbreak
Hayate ISO Shoko WAKAMIYA Eiji Aramaki
Nara Institute of Science and Technology (NAIST),Japan
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
408-411
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）