549 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
A Transformer-based Framework for POI-level Social Post Geolocation
POI-level geo-information of social posts is critical to many location-based
applications and services. However, the multi-modality, complexity and diverse
nature of social media data and their platforms limit the performance of
inferring such fine-grained locations and their subsequent applications. To
address this issue, we present a transformer-based general framework, which
builds upon pre-trained language models and considers non-textual data, for
social post geolocation at the POI level. To this end, inputs are categorized
to handle different social data, and an optimal combination strategy is
provided for feature representations. Moreover, a uniform representation of
hierarchy is proposed to learn temporal information, and a concatenated version
of encodings is employed to capture feature-wise positions better. Experimental
results on various social datasets demonstrate that three variants of our
proposed framework outperform multiple state-of-art baselines by a large margin
in terms of accuracy and distance error metrics.Comment: Full papers are 12 pages in length plus additional 4 pages for
references (turns to 18 pages in total after submitting to arxiv). One figure
and 5 tables are contained. This paper was submitted to ECIR 2023 for revie
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
Accurate estimation of user location is important for many online services.
Previous neural network based methods largely ignore the hierarchical structure
among locations. In this paper, we propose a hierarchical location prediction
neural network for Twitter user geolocation. Our model first predicts the home
country for a user, then uses the country result to guide the city-level
prediction. In addition, we employ a character-aware word embedding layer to
overcome the noisy information in tweets. With the feature fusion layer, our
model can accommodate various feature combinations and achieves
state-of-the-art results over three commonly used benchmarks under different
feature settings. It not only improves the prediction accuracy but also greatly
reduces the mean error distance.Comment: Accepted by EMNLP 201
- …