Creating Full Individual-level Location Timelines from Sparse Social Media Data

TL;DR

This paper introduces a stochastic framework called Intermediate Location Computing (ILC) that accurately reconstructs full human location timelines from sparse social media data by leveraging prior mobility knowledge, outperforming RNN baselines especially in low-data scenarios.

Contribution

The paper presents ILC, a novel method that improves full timeline inference from sparse social media data using prior knowledge, outperforming existing RNN-based approaches.

Findings

ILC achieves up to 77.2% accuracy in predicting missing locations.

ILC outperforms RNN models, especially with fewer users or sparser data.

ILC requires less data to achieve comparable performance to RNNs.

Abstract

In many domain applications, a continuous timeline of human locations is critical; for example for understanding possible locations where a disease may spread, or the flow of traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich, they are expensive, often not publicly available or garnered only in select locations, restricting their wide use. Conversely, geo-located social media data are publicly and freely available, but present challenges especially for full timeline inference due to their sparse nature. We propose a stochastic framework, Intermediate Location Computing (ILC) which uses prior knowledge about human mobility patterns to predict every missing location from an individual's social media timeline. We compare ILC with a state-of-the-art RNN baseline as well as methods that are optimized for next-location prediction only. For three major cities, ILC predicts the top 1 location for all missing locations in a timeline, at 1 and 2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all compared methods). Specifically, ILC also outperforms the RNN in settings of low data; both cases of very small number of users (under 50), as well as settings with more users, but with sparser timelines. In general, the RNN model needs a higher number of users to achieve the same performance as ILC. Overall, this work illustrates the tradeoff between prior knowledge of heuristics and more data, for an important societal problem of filling in entire timelines using freely available, but sparse social media data.