Public conversations on Twitter comprise many pertinent topics including
disasters, protests, politics, propaganda, sports, climate change,
epidemics/pandemic outbreaks, etc., that can have both regional and global
aspects. Spatial discourse analysis rely on geographical data. However, today
less than 1% of tweets are geotagged; in both cases--point location or bounding
place information. A major issue with tweets is that Twitter users can be at
location A and exchange conversations specific to location B, which we call the
Location A/B problem. The problem is considered solved if location entities can
be classified as either origin locations (Location As) or non-origin locations
(Location Bs). In this work, we propose a simple yet effective framework--the
True Origin Model--to address the problem that uses machine-level natural
language understanding to identify tweets that conceivably contain their origin
location information. The model achieves promising accuracy at country (80%),
state (67%), city (58%), county (56%) and district (64%) levels with support
from a Location Extraction Model as basic as the CoNLL-2003-based RoBERTa. We
employ a tweet contexualizer (locBERT) which is one of the core components of
the proposed model, to investigate multiple tweets' distributions for
understanding Twitter users' tweeting behavior in terms of mentioning origin
and non-origin locations. We also highlight a major concern with the currently
regarded gold standard test set (ground truth) methodology, introduce a new
data set, and identify further research avenues for advancing the area.

本文提出了一个简单而有效的模型，即真实来源模型，来解决 twitter 上的 “位置 A / B 问题”，该模型使用机器级自然语言理解来识别可能含有原始位置信息的推文，以达到国家，省市，乡镇和地区等级的有希望的准确性，并研究了多个推文的分布，以了解 Twitter 用户在提及原始和非原始位置方面的行为。