Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

Citation

BibTex format

@inproceedings{Lever:2022:10.1145/3529190.3534735,
author = {Lever, J and Arcucci, R and Cai, J},
doi = {10.1145/3529190.3534735},
pages = {455--462},
title = {Social Data Assimilation of Human Sensor Networks for Wildfires},
url = {http://dx.doi.org/10.1145/3529190.3534735},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - We present an implementation of a human sensor network in the context of wildfires. A human sensor network can be thought of as a socially nuanced abstraction of a physical sensing model, where social media users are considered noisy remote sensors with variable reliability and location. This allows real-time social modelling of physical events. We apply this concept to data collected from Twitter & Reddit in the context of California wildfires, performing sentimental & topical analysis over the period of a wildfire season to extract themes, sentiments and discussions. We assimilate this social media data in a predictive model trained by machine learning approaches for time series. Both Long Short Term Memory (LSTM) & AutoRegressive Integrated Moving Average (ARIMA) models are employed. We assimilate the human sensor networks, to overcome the limitations & biases exhibited by individual social media platform demographics. We implement Optimal Interpolation and Ensemble Kalman Filter architectures on our models & data. Finally we compare and evaluate performance, and discuss how these implementations could benefit current wildfire models.
AU - Lever,J
AU - Arcucci,R
AU - Cai,J
DO - 10.1145/3529190.3534735
EP - 462
PY - 2022///
SP - 455
TI - Social Data Assimilation of Human Sensor Networks for Wildfires
UR - http://dx.doi.org/10.1145/3529190.3534735
ER -