We present a transformer-based model for credibility assessment, CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), fine-tuned for Reddit submissions focusing on political discourse. We adopt a semi-supervised training approach for CREDiBERT, leveraging the community structure of Reddit. By encoding submission content using CREDiBERT and integrating it with a classification neural network, we improve the credibility assessment for Reddit submission by 3% in F1 score compared to existing methods. Additionally, we introduce a new version of the post-to-post network in Reddit that efficiently encodes user interactions to enhance the credibility assessment task by 8% in the F1 score. We demonstrate CREDiBERT's applicability by evaluating the susceptibility of Reddit communities to different topics and assessing the credibility score of unseen sources.

Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites is the formation of low-credibility communities, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities prone to uncredible or highly biased news within a social news website. We employ a user embedding pipeline that detects user communities based on their stances toward posts and news sources. We then project each community onto a credibility-bias space and analyze the distributional characteristics of each projected community to identify those that have a high risk of adopting beliefs with low credibility or high bias. This approach also enables the prediction of individual users' susceptibility to low-credibility content based on their community affiliation. Our results show that latent space clusters effectively indicate the credibility and bias levels of their users, with significant variance observed across clusters---a 34% difference in the users' susceptibility to low-credibility content and a 8.3% difference in the users' susceptibility to high political bias.

Exposure notification applications are designed to help trace disease spreading by alerting exposed individuals to get tested. However, false alarms can cause users to become hesitant to respond, making the applications ineffective. To address the shortcomings of slow manual contact tracing, costly lockdowns, and unreliable exposure notification applications, better disease mitigation strategies are needed. In this paper, we propose a new disease mitigation paradigm where people can reduce infection spreading while maintaining some mobility (i.e., Quarantine in Motion). Our approach utilizes Graph Neural Networks (GNNs) to predict disease hotspots such as restaurants, shops and parks, and Multi-Agent Reinforcement Learning (MARL) to collaboratively manage human mobility to reduce disease transmission. As proof of concept, we simulate an infection using real-world mobility data from New York City (over 200,000 devices) and Austin (over 36,000 devices) and train 10,000 agents from each city to manage disease dynamics. Through simulation, we show that a trained population suppresses their reproduction rate below 1, thereby mitigating the outbreak.

Exposure notification applications are developed to increase the scale and speed of disease contact tracing. Indeed, by taking advantage of Bluetooth technology, they track the infected population's mobility and then inform close contacts to get tested. In this paper, we ask whether these applications can extend from reactive to preemptive risk management tools? To this end, we propose a new framework that utilizes graph neural networks (GNN) and real-world Foursquare mobility data to predict high risk locations on an hourly basis. As a proof of concept, we then simulate a risk-informed Foursquare population of over 36,000 people in Austin TX after the peak of an outbreak. We find that even after 50% of the population has been infected with COVID-19, they can still maintain their mobility, while reducing the new infections by 13%. Consequently, these results are a first step towards achieving what we call Quarantine in Motion.

With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks in the past, existing approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network science based method to first build and then prune the dynamic contact networks for recurring interactions; these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare's Points of Interest (POI) smart phone geolocation data from over 1.3 million devices to better approximate the COVID-19 infection curves for two major (yet very different) US cities, (i.e., Austin and New York City), while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the mesoscale that can help both policy makers and individuals better understand their estimated state of health and help the pandemic mitigation efforts.

With the explosion in Big Data, it is often forgotten that much of the data nowadays is generated at the edge. Specifically, a major source of data is users' endpoint devices like phones, smart watches, etc., that are connected to the internet, also known as the Internet-of-Things (IoT). This "edge of data" faces several new challenges related to hardware-constraints, privacy-aware learning, and distributed learning (both training as well as inference). So what systems and machine learning algorithms can we use to generate or exploit data at the edge? Can network science help us solve machine learning (ML) problems? Can IoT-devices help people who live with some form of disability and many others benefit from health monitoring?

Microblogging disseminates realtime information through dynamic user interactions. While it is intuitive that such interactions may generate patterns, it is difficult to identify and characterize them in satisfactory detail. In this paper, we propose using a combination of dynamic graphs and time-series to study the dynamics and collective behaviors in microblogging. To enable automatic pattern identification, a distance metric is developed to incorporate the heterogeneous aspects of the dynamical interactions. We demonstrate the effectiveness of the proposed approach using a month long Twitter dataset and show that the new representation and distance metric are both essential for discovering the patterns of collective microblogging, such as propagation of breaking news, advertisement, social movement, and interest group formation.