Understanding post-wildfire vegetation loss is critical for developing effective ecological recovery strategies and is often challenging due to the extended time and effort required to capture the evolving ecosystem features. Recent works in this area have not fully explored all the contributing factors, their modalities, and interactions with each other. Furthermore, most research in this domain is limited by a lack of interpretability in predictive modeling, making it less useful in real-world settings. In this work, we propose a novel end-to-end ML pipeline called MVeLMA (Multimodal Vegetation Loss Modeling Architecture) to predict county-wise vegetation loss from fire events. MVeLMA uses a multimodal feature integration pipeline and a stacked ensemble-based architecture to capture different modalities while also incorporating uncertainty estimation through probabilistic modeling. Through comprehensive experiments, we show that our model outperforms several state-of-the-art (SOTA) and baseline models in predicting post-wildfire vegetation loss. Furthermore, we generate vegetation loss confidence maps to identify high-risk counties, thereby helping targeted recovery efforts. The findings of this work have the potential to inform future disaster relief planning, ecological policy development, and wildlife recovery management.

Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain.

Can we trust Large Language Models (LLMs) to accurately predict scam? This paper investigates the vulnerabilities of LLMs when facing adversarial scam messages for the task of scam detection. We addressed this issue by creating a comprehensive dataset with fine-grained labels of scam messages, including both original and adversarial scam messages. The dataset extended traditional binary classes for the scam detection task into more nuanced scam types. Our analysis showed how adversarial examples took advantage of vulnerabilities of a LLM, leading to high misclassification rate. We evaluated the performance of LLMs on these adversarial scam messages and proposed strategies to improve their robustness.

This paper presents an innovative framework developed to identify, analyze, and generate memorable experiences in the hotel industry. People prefer memorable experiences over traditional services or products in today's ever-changing consumer world. As a result, the hospitality industry has shifted its focus toward creating unique and unforgettable experiences rather than just providing essential services. Despite the inherent subjectivity and difficulties in quantifying experiences, the quest to capture and understand these critical elements in the hospitality context has persisted. However, traditional methods have proven inadequate due to their reliance on objective surveys or limited social media data, resulting in a lack of diversity and potential bias. Our framework addresses these issues, offering a holistic solution that effectively identifies and extracts memorable experiences from online customer reviews, discerns trends on a monthly or yearly basis, and utilizes a local LLM to generate potential, unexplored experiences. As the first successfully deployed, fast, and accurate product of its kind in the industry, This framework significantly contributes to the hotel industry's efforts to enhance services and create compelling, personalized experiences for its customers.

This paper introduces HyperMAD, a novel Hypergraph Convolutional Network model designed for the multiclass classification of mental health advice in Arabic tweets. The model distinguishes between misleading and valid advice, further categorizing each tweet into specific classes of advice. HyperMAD leverages high-order relations between words in short texts, captured through the definition of four types of hyperedges that represent local and global contexts as well as semantic similarity. Extensive experiments demonstrate the effectiveness of HyperMAD, with results outperforming those from existing baselines. The study also includes an ablation study to investigate the significance and contribution of each hyperedge type. The paper presents a case study analyzing the accuracy and types of Arabic mental health advice on Twitter, revealing that about 9% of the advice in response to mental health expressions on Twitter was accurate in general. The paper concludes with the hope that the application of HyperMAD can be utilized in flagging misleading responses on social media, providing the correct resources for those who choose to share their mental health struggles online.

Mental health conditions affect millions of people today. While existing work on predicting mental health conditions from social media text focuses largely on depression and similar conditions, other less prominent disorders like bipolar tend not to receive in-depth analysis. Furthermore, these works tend not to analyze or model the correlated nature of these different disorders and conditions. To account for the coexistence and correlation of multiple mental health conditions, this paper introduces DeMHeM, a novel multitask framework designed for the descriptive classification of bipolar and related mental health topics on online platforms like Reddit. By treating each mental health category as a separate task, DeMHeM leverages both the shared latent and task-specific semantic feature space by integrating sentence-level and topic-level embeddings. It further incorporates Focal Loss for joint learning, inter-task parameter sharing, and regularization decay to optimize the prediction for the naturally skewed imbalanced dataset. Hence, the model distinguishes between different mental health categories and also models the correlation among them by categorizing each post into potentially multiple mental health categories. Next, we focus on a more insightful analysis by leveraging the predicted outcome of the model to study how the discussions differ based on the type and coexistence of different mental disorders. We analyze the entirety of the “r/bipolar” subreddit by applying our trained model to predict a category and then implementing keyword extraction techniques on each predicted combination of mental health conditions to understand the specific nuances in the discussion of bipolar disorder. Our results show that DeMHeM surpassed the baseline models and can be used to understand the multi-faceted discussion of mental health topics for a given community.

The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model.

One of the strongest indicators of a mental health crisis is how people interact with each other or express them-selves. Hence, social media is an ideal source to extract user-level information about the language used to express personal feelings. In the wake of the ever-increasing mental health crisis in the United States, it is imperative to analyze the general well-being of a population and investigate how their public social media posts can be used to detect different underlying mental health conditions. For that purpose, we propose a study that collects posts from “reddits” related to different mental health topics to detect the type of the post and the nature of the mental health issues that correlate to the post. The task of detecting mental health related issues indicates the mental health conditions connected to the posts. To achieve this, we develop a multi-task learning model that leverages, for each post, both the latent embedding space of words and topics for prediction with a message passing mechanism enabling the sharing of information for related tasks. We train the model through an active learning approach in order to tackle the lack of standardized fine-grained label data for this specific task.

Drug overdose deaths are a dreadful crisis that leads to substantial societal impairments. Its harmful impact directly affects families and communities. To assist policymakers in mitigating this crisis, it is crucial to study the societal, economic, and criminal contributing factors linked to the crisis. Unfortunately, current data-driven works assume a singular factor, such as poverty being the cause and disregarding other realistic causes. Besides, recent works exhibited a lack of explainable models and spatial analysis of the crisis. Thus, DOD-Explainer links the gap by developing a realistic framework that predicts highly impacted counties of drug overdose deaths from crime and socioeconomic data. DOD-Explainer overcomes the challenge of data scarcity by proposing three data augmentation methods. Then, an algorithm is proposed to provide realistic explanations of the leading causes of the crisis. The results show that our application achieves the best predictive accuracy from several models, accurately identifies the most/least impacted counties by the crisis, and reveals the most contributing factors of drug overdoses.

Social media platforms have become an easy method of communication for many users. Content posted on social media can influence those who are exposed to it, and users who posted that content are referred to as influencers. Identifying influencers has many applications in marketing, politics, and even health awareness. While research identifying influential users across multiple fields has been studied extensively, users’ influence varies in different topics. Recent studies in topic-specific influence have shown that identifying influencers on the topic-level is more effective. However, most of the existing influencer detection approaches focus only on influential user identification and do not consider that some content can be influential regardless of who published it. This paper investigates the problem of detecting topic-specific influential users and tweets in Twitter datasets. We introduce HyperTwitter, a framework that uses a Twitter sub-graph consisting of users, tweets, and interactions as input. HyperTwitter generates a hypergraph with hyperedges of two types: networks and topic edges, then measures the topic distribution for both users and tweets. With this distribution and the constructed hypergraph, we create a local, topic-based influence ranking for each user and tweet. We conduct extensive experiments with two Twitter datasets and show that the proposed framework outperforms existing baselines significantly.

Ever since the COVID-19 outbreak, various works have focused on using multitude of different static and dynamic features to aid the prediction of disease forecasting models. However, in the absence of historical pandemic data these models will not be able to give any meaningful insight about the areas which are most likely to be affected based on preexisting conditions. Furthermore, the black box nature of neural networks often becomes an impediment for the concerned authorities to derive any meaning from. In this paper, we propose a novel explainable Graph Neural Network (GNN) framework called Graph-COVID-19-Explainer (GC-Explainer) that gives explainable prediction for the severity of the spread during initial outbreak. We utilize a comprehensive set of static population characteristics to use as node features of Graph where each node corresponds to a geographical region. Unlike post-hoc methods of GNN explanations, we propose a framework for learning important features during the training of the model. We further apply our model on real-world early pandemic data to show the validity of our approach. Through GC-Explainer, we show that static features along with spatial dependency among regions can be used to explain the varied degree of severity in outbreak during the early part of the pandemic and provide a framework to identify the at-risk areas for any infectious disease outbreak, especially when historical data is not available.

Modeling the spatiotemporal nature of the spread of infectious diseases can provide useful intuition in understanding the time-varying aspect of the disease spread and the underlying complex spatial dependency observed in people's mobility patterns. Besides, the county level multiple related time series information can be leveraged to make a forecast on an individual time series. Adding to this challenge is the fact that real-time data often deviates from the unimodal Gaussian distribution assumption and may show some complex mixed patterns. Motivated by this, we develop a deep learning-based time-series model for probabilistic forecasting called Auto-regressive Mixed Density Dynamic Diffusion Network (ARM3Dnet), which considers both people's mobility and disease spread as a diffusion process on a dynamic directed graph. The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the realtime data while learning from multiple related time series. We show that our model, when trained with the best combination of dynamic covariate features and mixture components, can outperform both traditional statistical and deep learning models in forecasting the number of Covid-19 deaths and cases at the county level in the United States.

Existing COVID-19 prediction models focus on studying the dynamic nature of the virus spread by using pandemic-related temporal data. In this paper, we present a work that exclusively uses comprehensive socioeconomic factors to predict the high risk areas of COVID-19 infection based on fine-grained static spatial analysis. Moreover, the most and least influential socioeconomic factors on COVID-19 spread are identified. This paper uses a uniquely built dataset by combining local states’ cumulative COVID-19 statistics and their associated socioeconomic features on the zip code level. Further, the work solves the lack of data by augmentation. To evaluate the work, four case studies are conducted on Florida, Illinois, Minnesota, and Virginia. Experimental results show that the study provides accurate predictions with respect to ground truth data. By identifying high risk areas and socioeconomic factors, policymakers can use this study to take necessary measures to help disadvantaged communities.