  Inferring locations from user texts on social media platforms is a
non-trivial and challenging problem relating to public safety. We propose a
novel non-uniform grid-based approach for location inference from Twitter
messages using Quadtree spatial partitions. The proposed algorithm uses natural
language processing (NLP) for semantic understanding and incorporates Cosine
similarity and Jaccard similarity measures for feature vector extraction and
dimensionality reduction. We chose Twitter as our experimental social media
platform due to its popularity and effectiveness for the dissemination of news
and stories about recent events happening around the world. Our approach is the
first of its kind to make location inference from tweets using Quadtree spatial
partitions and NLP, in hybrid word-vector representations. The proposed
algorithm achieved significant classification accuracy and outperformed
state-of-the-art grid-based content-only location inference methods by up to
24% in correctly predicting tweet locations within a 161km radius and by 300km
in median error distance on benchmark datasets.
