Protectbot: A Chatbot to Protect Children on Gaming Platforms

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats

How Technology Impacts and Compares to Humans in Socially Consequential Arenas

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.Comment: Doctoral thesis proposal. arXiv admin note: substantial text overlap with arXiv:2110.08396, arXiv:2108.12508, arXiv:2006.1262

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Social World Sensing via Social Image Analysis from Social Media

Social imagery, the visuals shared by users via various platforms and applications, may be analyzed to elicit something of massmind (and individual) thinking. This work involves the exploration of seven topics from various subject areas (global public health, environmentalism, human rights, political expression, and human predation) through social imagery and data from social media. The coding techniques involve manual coding, the integration of multiple social data streams, computational text analysis, data visualizations, and other combinations of approaches.https://newprairiepress.org/ebooks/1037/thumbnail.jp

Self-disclosure model for classifying & predicting text-based online disclosure

Les médias sociaux et les sites de réseaux sociaux sont devenus des babillards numériques pour les internautes à cause de leur évolution accélérée. Comme ces sites encouragent les consommateurs à exposer des informations personnelles via des profils et des publications, l'utilisation accrue des médias sociaux a généré des problèmes d’invasion de la vie privée. Des chercheurs ont fait de nombreux efforts pour détecter l'auto-divulgation en utilisant des techniques d'extraction d'informations. Des recherches récentes sur l'apprentissage automatique et les méthodes de traitement du langage naturel montrent que la compréhension du sens contextuel des mots peut entraîner une meilleure précision que les méthodes d'extraction de données traditionnelles. Comme mentionné précédemment, les utilisateurs ignorent souvent la quantité d'informations personnelles publiées dans les forums en ligne. Il est donc nécessaire de détecter les diverses divulgations en langage naturel et de leur donner le choix de tester la possibilité de divulgation avant de publier. Pour ce faire, ce travail propose le « SD_ELECTRA », un modèle de langage spécifique au contexte. Ce type de modèle détecte les divulgations d'intérêts, de données personnelles, d'éducation et de travail, de relations, de personnalité, de résidence, de voyage et d'accueil dans les données des médias sociaux. L'objectif est de créer un modèle linguistique spécifique au contexte sur une plate-forme de médias sociaux qui fonctionne mieux que les modèles linguistiques généraux. De plus, les récents progrès des modèles de transformateurs ont ouvert la voie à la formation de modèles de langage à partir de zéro et à des scores plus élevés. Les résultats expérimentaux montrent que SD_ELECTRA a surpassé le modèle de base dans toutes les métriques considérées pour la méthode de classification de texte standard. En outre, les résultats montrent également que l'entraînement d'un modèle de langage avec un corpus spécifique au contexte de préentraînement plus petit sur un seul GPU peut améliorer les performances. Une application Web illustrative est conçue pour permettre aux utilisateurs de tester les possibilités de divulgation dans leurs publications sur les réseaux sociaux. En conséquence, en utilisant l'efficacité du modèle suggéré, les utilisateurs pourraient obtenir un apprentissage en temps réel sur l'auto-divulgation.Social media and social networking sites have evolved into digital billboards for internet users due to their rapid expansion. As these sites encourage consumers to expose personal information via profiles and postings, increased use of social media has generated privacy concerns. There have been notable efforts from researchers to detect self-disclosure using Information extraction (IE) techniques. Recent research on machine learning and natural language processing methods shows that understanding the contextual meaning of the words can result in better accuracy than traditional data extraction methods. Driven by the facts mentioned earlier, users are often ignorant of the quantity of personal information published in online forums, there is a need to detect various disclosures in natural language and give them a choice to test the possibility of disclosure before posting. For this purpose, this work proposes "SD_ELECTRA," a context-specific language model to detect Interest, Personal, Education and Work, Relationship, Personality, Residence, Travel plan, and Hospitality disclosures in social media data. The goal is to create a context-specific language model on a social media platform that performs better than the general language models. Moreover, recent advancements in transformer models paved the way to train language models from scratch and achieve higher scores. Experimental results show that SD_ELECTRA has outperformed the base model in all considered metrics for the standard text classification method. In addition, the results also show that training a language model with a smaller pre-training context-specific corpus on a single GPU can improve its performance. An illustrative web application designed allows users to test the disclosure possibilities in their social media posts. As a result, by utilizing the efficiency of the suggested model, users would be able to get real-time learning on self-disclosure

Novel Natural Language Processing Models for Medical Terms and Symptoms Detection in Twitter

This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug’s legalization and the sentiment analysis of drug consumption on Twitter. We collected 7.5 million data from August 2015 to March 2016. This work leveraged a longstanding, multidisciplinary collaboration between researchers at the Population & Center for Interventions, Treatment, and Addictions Research (CITAR) in the Boonshoft School of Medicine and the Department of Computer Science and Engineering. In addition, we aimed to develop and deploy an innovative prediction analysis algorithm for eDrugTrends, capable of semi-automated processing of Twitter data to identify emerging trends in cannabis and synthetic cannabinoid use in the U.S. In addition, the study included aim four, a use case study defined by tweets content analyzing PLWH, medication patterns, and identifying keyword trends via Twitter-based, user-generated content. This case study leveraged a multidisciplinary collaboration between researchers at the Departments of Family Medicine and Population and Public Health Sciences at Wright State University’s Boonshoft School of Medicine and the Department of Computer Science and Engineering. We collected 65K data from February 2022 to July 2022 with the U.S.-based HIV knowledge domain recruited via the Twitter API streaming platform. For knowledge discovery, domain knowledge plays a significant role in powering many intelligent frameworks, such as data analysis, information retrieval, and pattern recognition. Recent NLP and semantic web advances have contributed to extending the domain knowledge of medical terms. These techniques required a bag of seeds for medical knowledge discovery. Various initiate seeds create irrelevant data to the noise and negatively impact the prediction analysis performance. The methodology of aim one, PatRDis classifier, applied for noisy and ambiguous issues, and aim two, DsOn Ontology model, applied for semantic parsing and enriching the online medical to classify the data for HIV care medications engagement and symptom detection from Twitter. By applying the methodology of aims 2 and 3, we solved the challenges of ambiguity and explored more than 1500 cannabis and cannabinoid slang terms. Sentiments measured preceding the election, such as states with high levels of positive sentiment preceding the election who were engaged in enhancing their legalization status. we also used the same dataset for prediction analysis for marijuana legalization and consumption trend analysis (Ohio public polling data). In Aim 4, we applied three experiments, ensemble-learning, the RNN-LSM, the NNBERT-CNN models, and five techniques to determine the tweets associated with medication adherence and HIV symptoms. The long short-term memory (LSTM) model and the CNN for sentence classification produce accurate results and have been recently used in NLP tasks. CNN models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. We propose attention-based RNN, MLP, and CNN deep learning models that capitalize on the advantages of LSTM and BERT techniques with an additional attention mechanism. We trained the model using NNBERT to evaluate the proposed model\u27s performance. The test results showed that the proposed models produce more accurate classification results, and BERT obtained higher recall and F1 scores than MLP or LSTM models. In addition, We developed an intelligent tool capable of automated processing of Twitter data to identify emerging trends in HIV disease, HIV symptoms, and medication adherence

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The New Teacher of Ideas: A Study on Social Media, Political Influencers, and Generation Z

The current study focuses on members of Generation Z and where they learn about politics and receive political news. Different than previous generations, Generation Z receives news on social media more than on traditional media platforms (Murmuration, 2023). Politicians must quickly begin to understand how to engage and connect with the next generation of voters on these social media platforms. For these reasons, this study surveyed members of Generation Z at The University of Mississippi. Current University of Mississippi students are members of Generation Z and reflect common trends among this Generation, such as constant usage of social media. This research is important in helping political campaigns increase engagement among the youngest group of voters. A candidate or party who can increase participation among Generation Z could potentially win future elections

Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject