Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. However, high dimensionality and extensive feature sets of Twitter data present remarkable challenges for ED classification. To overcome these hurdles, we introduce a novel method, an informed branch and bound search technique known as ED-Filter. This strategy significantly improves the drawbacks of conventional feature selection algorithms such as filters and wrappers. ED-Filter iteratively identifies an optimal set of promising features that maximize the eating disorder classification accuracy. In order to adapt to the dynamic nature of Twitter ED data, we enhance the ED-Filter with a hybrid greedy-based deep learning algorithm. This algorithm swiftly identifies sub-optimal features to accommodate the ever-evolving data landscape. Experimental results on Twitter eating disorder data affirm the effectiveness and efficiency of ED-Filter. The method demonstrates significant improvements in classification accuracy and proves its value in eating disorder detection on social media platforms.

This research investigates the performance of various machine learning algorithms (CNN, LSTM, VADER, and RoBERTa) for sentiment analysis of Twitter data related to imported food items in Trinidad and Tobago. The study addresses three primary research questions: the comparative accuracy and efficiency of the algorithms, the optimal configurations for each model, and the potential applications of the optimized models in a live system for monitoring public sentiment and its impact on the import bill. The dataset comprises tweets from 2018 to 2024, divided into imbalanced, balanced, and temporal subsets to assess the impact of data balancing and the COVID-19 pandemic on sentiment trends. Ten experiments were conducted to evaluate the models under various configurations. Results indicated that VADER outperformed the other models in both multi-class and binary sentiment classifications. The study highlights significant changes in sentiment trends pre- and post-COVID-19, with implications for import policies.

Large language models (LLMs) have gained significant attention due to their ability to mimic human language. Identifying texts generated by LLMs is crucial for understanding their capabilities and mitigating potential consequences. This paper analyzes datasets of varying text lengths: small, medium, and large. We compare the performance of machine learning algorithms on four datasets: (1) small (tweets from Election, FIFA, and Game of Thrones), (2) medium (Wikipedia introductions and PubMed abstracts), and (3) large (OpenAI web text dataset). Our results indicate that LLMs with very large parameters (such as the XL-1542 variant of GPT2 with 1542 million parameters) were harder (74%) to detect using traditional machine learning methods. However, detecting texts of varying lengths from LLMs with smaller parameters (762 million or less) can be done with high accuracy (96% and above). We examine the characteristics of human and machine-generated texts across multiple dimensions, including linguistics, personality, sentiment, bias, and morality. Our findings indicate that machine-generated texts generally have higher readability and closely mimic human moral judgments but differ in personality traits. SVM and Voting Classifier (VC) models consistently achieve high performance across most datasets, while Decision Tree (DT) models show the lowest performance. Model performance drops when dealing with rephrased texts, particularly shorter texts like tweets. This study underscores the challenges and importance of detecting LLM-generated texts and suggests directions for future research to improve detection methods and understand the nuanced capabilities of LLMs.

In an attempt to mimic the complex paths through which unreliable content spreads between search engines and social media, we explore the impact of incorporating both webgraph and large-scale social media contexts into website credibility classification and discovery systems. We further explore the usage of what we define as \textit{dredge words} on social media -- terms or phrases for which unreliable domains rank highly. Through comprehensive graph neural network ablations, we demonstrate that curriculum-based heterogeneous graph models that leverage context from both webgraphs and social media data outperform homogeneous and single-mode approaches. We further demonstrate that the incorporation of dredge words into our model strongly associates unreliable websites with social media and online commerce platforms. Finally, we show our heterogeneous model greatly outperforms competing systems in the top-k identification of unlabeled unreliable websites. We demonstrate the strong unreliability signals present in the diverse paths that users follow to uncover unreliable content, and we release a novel dataset of dredge words.

Cultural variation exists between nations (e.g., the United States vs. China), but also within regions (e.g., California vs. Texas, Los Angeles vs. San Francisco). Measuring this regional cultural variation can illuminate how and why people think and behave differently. Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. In this work, we introduce a new research problem for the NLP community: How do we measure variation in cultural constructs across regions using language? We then provide a scalable solution: building knowledge-guided lexica to model cultural variation, encouraging future work at the intersection of NLP and cultural understanding. We also highlight modern LLMs' failure to measure cultural variation or generate culturally varied language.

Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems. In this study, we present a new framework to surveil public health, focusing on mental health (MH) outcomes. We hypothesize that locally posted tweets are indicative of local MH outcomes and collect tweets posted from 765 neighborhoods (census block groups) in the USA. We pair these tweets from each neighborhood with the corresponding MH outcome reported by the Center for Disease Control (CDC) to create a benchmark dataset, LocalTweets. With LocalTweets, we present the first population-level evaluation task for Twitter-based MH surveillance systems. We then develop an efficient and effective method, LocalHealth, for predicting MH outcomes based on LocalTweets. When used with GPT3.5, LocalHealth achieves the highest F1-score and accuracy of 0.7429 and 79.78\%, respectively, a 59\% improvement in F1-score over the GPT3.5 in zero-shot setting. We also utilize LocalHealth to extrapolate CDC's estimates to proxy unreported neighborhoods, achieving an F1-score of 0.7291. Our work suggests that Twitter data can be effectively leveraged to simulate neighborhood-level MH outcomes.

New York City (NYC) topped the global chart for the worst air pollution in June 2023, owing to the wildfire smoke drifting in from Canada. This unprecedented situation caused significant travel disruptions and shifts in traditional activity patterns of NYC residents. This study utilized large-scale social media data to study different crisis activity concerns (i.e., evacuation, staying indoors, shopping, and recreational activities among others) in the emergence of the 2023 Canadian wildfire smoke in NYC. In this regard, one week (June 02 through June 09, 2023) geotagged Twitter data from NYC were retrieved and used in the analysis. The tweets were processed using advanced text classification techniques and later integrated with national databases such as Social Security Administration data, Census, and American Community Survey. Finally, a model has been developed to make community inferences of different activity concerns in a major wildfire. The findings suggest, during wildfires, females are less likely to engage in discussions about evacuation, trips for medical, social, or recreational purposes, and commuting for work, likely influenced by workplaces maintaining operations despite poor air quality. There were also racial disparities in these discussions, with Asians being more likely than Hispanics to discuss evacuation and work commute, and African Americans being less likely to discuss social and recreational activities. Additionally, individuals from low-income neighborhoods and non-higher education students expressed fewer concerns about evacuation. This study provides valuable insights for policymakers, emergency planners, and public health officials, aiding them in formulating targeted communication strategies and equitable emergency response plans.

More recently, (Fu et al., 2023) natural language processing (NLP) tasks using creates a meta-model responsible for predicting the latest generative pretrained models such as the accuracy of the LLM model using the model's GPT (OpenAI, 2023; Ouyang et al., 2022), PaLM confidence scores as features. Methods from the (Chowdhery et al., 2022), and many others (Touvron computer vision (CV) domain to assess unlabeled et al., 2023; Bai et al., 2022; Penedo et al., data more generally have, for example, proposed 2023; Taori et al., 2023). This new generation of the average threshold confidence method that learns models opens up many new possibilities including a threshold over the model's confidence, predicting competitive performance in zero-shot and few-shot accuracy as the fraction of unlabeled examples settings for tasks that have typically been modeled exceeding that threshold (Garg et al., 2022), or iteratively using a supervised setting (OpenAI, 2023). More learn an ensemble of models to identify established language models (BERT (Devlin et al., misclassified data points and perform self-training 2019), RoBERTa (Liu et al., 2019), XLM-Roberta to improve the ensemble with the identified points (Conneau et al., 2020b), etc.) provide a strong balance (Chen et al., 2021). However, the metrics and hyperparameters of inference cost and task performance for in previous works are specifically for such systems. This broad class of large language classification tasks and cannot be easily extended models (LLMs) used for complex supervised NLP to more complex tasks.

Disaster Management is one of the most promising research areas because of its significant economic, environmental and social repercussions. This research focuses on analyzing different types of data (pre and post satellite images and twitter data) related to disaster management for in-depth analysis of location-wise emergency requirements. This research has been divided into two stages, namely, satellite image analysis and twitter data analysis followed by integration using location. The first stage involves pre and post disaster satellite image analysis of the location using multi-class land cover segmentation technique based on U-Net architecture. The second stage focuses on mapping the region with essential information about the disaster situation and immediate requirements for relief operations. The severely affected regions are demarcated and twitter data is extracted using keywords respective to that location. The extraction of situational information from a large corpus of raw tweets adopts Content Word based Tweet Summarization (COWTS) technique. An integration of these modules using real-time location-based mapping and frequency analysis technique gathers multi-dimensional information in the advent of disaster occurrence such as the Kerala and Mississippi floods that were analyzed and validated as test cases. The novelty of this research lies in the application of segmented satellite images for disaster relief using highlighted land cover changes and integration of twitter data by mapping these region-specific filters for obtaining a complete overview of the disaster.

Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.