Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learning.

BACKGROUND: The exponential growth of location-based social media (LBSM) data has ushered in novel prospects for investigating the urban food environment in health geography research. However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. To enhance the analysis of the urban food environment using LBSM data, it is crucial to develop a more comprehensive list of food-related words. Within the context, this study delves into the exploration of expanding food-related words along with their associated energy densities. METHODS: This study addresses the aforementioned research gap by introducing a novel methodology for expanding the food-related word dictionary and predicting energy densities. Seed words are generated from official and crowdsourced food composition databases, and new food words are discovered by clustering food words within the word embedding space using the Gaussian mixture model. Machine learning models are employed to predict the energy density classifications of these food words based on their feature vectors. To ensure a thorough exploration of the prediction problem, ten widely used machine learning models are evaluated. RESULTS: The approach successfully expands the food-related word dictionary and accurately predicts food energy density (reaching 91.62%.). Through a comparison of the newly expanded dictionary with the initial seed words and an analysis of Yelp reviews in the city of Toronto, we observe significant improvements in identifying food words and gaining a deeper understanding of the food environment. CONCLUSIONS: This study proposes a novel method to expand food-related vocabulary and predict the food energy density based on machine learning and word embedding. This method makes a valuable contribution to building a more comprehensive list of food words that can be used in geography and public health studies by mining geotagged social media data.

SocialSift: Target Query Discovery on Online Social Media With Deep Reinforcement Learning.

Among the prohibitively large volume of posts (e.g., tweets in Twitter) on online social networks (OSNs), how to design effective queries to explore the ones of interest is a pressing problem. There are two main challenges to address the problem. First, given public application programming interfaces (APIs) for querying posts related to keywords from an extremely large vocabulary, how to infer the keywords relevant to our target interest using as few queries as possible? Second, how to deal with the agnostics of OSN's API? i.e., as different social networks typically have different running mechanisms, even with some randomness in returning results, how to build the knowledge of the API returns w.r.t. target interests from scratches? To address the above two challenges, we propose a target query discovery framework based on a deep reinforcement learning approach, named SocialSift. SocialSift intelligently interacts with OSNs' keyword-based API and develops its own knowledge in searching the optimal queries w.r.t. the target interests as well as OSN APIs. Specifically, to address the first challenge, we are inspired by the human searching experience, and recognize learning to query with context awareness to reduce the searching space, by qualifying keywords from returned results and keeping the tracks of the query trial history, or say contexts. As for addressing the second challenge, we treat OSNs' APIs as black boxes and probabilistically quantify query-interest pairs guided by rewards, which is a well-curated indicator w.r.t. target interests. Empirical results on three popular OSNs: Twitter, Reddit, and Amazon demonstrate our SocialSift significantly outperforms the state-of-the-art baselines by 12% in retrieving target posts.

Active Surveillance via Group Sparse Bayesian Learning.

The key to the effective control of a diffusion system lies in how accurately we could predict its unfolding dynamics based on the observation of its current state. However, in the real-world applications, it is often infeasible to conduct a timely and yet comprehensive observation due to resource constraints. In view of such a practical challenge, the goal of this work is to develop a novel computational method for performing active observations, termed active surveillance, with limited resources. Specifically, we aim to predict the dynamics of a large spatio-temporal diffusion system based on the observations of some of its components. Towards this end, we introduce a novel measure, the γ value, that enables us to identify the key components by means of modeling a sentinel network with a row sparsity structure. Having obtained a theoretical understanding of the γ value, we design a backward-selection sentinel network mining algorithm (SNMA) for deriving the sentinel network via group sparse Bayesian learning. In order to be practically useful, we further address the issue of scalability in the computation of SNMA, and moreover, extend SNMA to the case of a non-linear dynamical system that could involve complex diffusion mechanisms. We show the effectiveness of SNMA by validating it using both synthetic datasets and five real-world datasets. The experimental results are appealing, which demonstrate that SNMA readily outperforms the state-of-the-art methods.