Detecting depression from user-generated content on social media platforms has garnered significant attention due to its potential for the early identification and monitoring of mental health issues. This paper presents a comprehensive approach for depression detection from user tweets using machine learning techniques. The study utilizes a dataset of 632,000 tweets and employs data preprocessing, feature selection, and model training with logistic regression, Bernoulli Naive Bayes, random forests, DistilBERT, SqueezeBERT, DeBERTA, and RoBERTa models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results indicate that the RoBERTa model achieves the highest accuracy ratio of 0.981 and the highest mean accuracy of 0.97 (across 10 cross-validation folds) in detecting depression from tweets. This research demonstrates the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for mental health analysis. The findings offer valuable insights into the potential for early detection and monitoring of depression using online platforms, contributing to the growing field of mental health analysis based on user-generated content.

Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions.

Depression detection through social media analysis has emerged as a promising approach for early intervention and mental health support. This study evaluates the performance of various machine learning and transformer models in identifying depressive content from tweets on X. Utilizing the Sentiment140 and the Suicide-Watch dataset, we built several models which include logistic regression, Bernoulli Naive Bayes, Random Forest, and transformer models such as RoBERTa, DeBERTa, DistilBERT, and SqueezeBERT to detect this content. Our findings indicate that transformer models outperform traditional machine learning algorithms, with RoBERTa and DeBERTa, when predicting depression and suicide rates. This performance is attributed to the transformers’ ability to capture contextual nuances in language. On the other hand, logistic regression models outperform transformers in another dataset with more accurate information. This is attributed to the traditional model’s ability to understand simple patterns especially when the classes are straighforward. We employed a comprehensive cross-validation approach to ensure robustness, with transformers demonstrating higher stability and reliability across splits. Despite limitations like dataset scope and computational constraints, the findings contribute significantly to mental health monitoring and suggest promising directions for future research and real-world applications in early depression detection and mental health screening tools. The various models used performed outstandingly.

Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model’s performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception.