The expansion of social media has unlocked a real-time barometer of public opinion. This paper introduces a novel framework to analyze sentiment shifts in social network comment sections, a reflection of the broader public discourse over time. Leveraging a pre-trained uncased RoBERTa model, we predict emotional scores from user comments, mapping these to key sentiment trends such as Approval, Toxicity, Obscenity, Threat, Hate, Offensive, and Neutral. Our methodology employs machine learning techniques to train a dataset that connects emotional scores with these trends, generating trend probability scores. We utilize a bottom-up recursive algorithm to aggregate emotional scores within comment threads, enabling the prediction of trend scores using three distinct aggregation methods. The results demonstrate that our emotional prediction model achieves an AUC of 0.92, and XGBoost stands out with an F1 score exceeding 0.40. Our research elucidates the temporal evolution of online public sentiment, enhancing the understanding of digital social dynamics and offering insights for strategic online interaction, intervention, and content moderation.

Gender identification from social media content can play a crucial role in detecting and mitigating the risks posed by counterfeit accounts. Authentic gender representation can foster a safer and more diverse online environment. While research has been conducted on gender identification in languages such as English, Russian, and Arabic, there remains a gap in studies targeting Bangla and its related languages. This paper introduces a stylometric feature approach to discern the gender of authors from Bangla texts. Utilizing a dataset of 5,000 posts sourced from various Facebook groups, we trained seven traditional machine learning models. Among these, the Random Forest (RF) model notably excelled, achieving an accuracy of 73.37% and an F1-Score of 79.25%, thus setting a promising benchmark in gender identification from Bangla texts.

Identity attacks, such as impersonation, identity theft, and fraudulent account creation, pose significant threats to the security and trustworthiness of Online Social Networks (OSNs). In this paper, we propose a robust and secure framework to verify user identities without compromising their privacy by developing a multi-layered framework leveraging zero-knowledge proof (ZKP) and Hyperledger Fabric private blockchain. We introduce a blockchain-based government identity provider system, coupled with a zero-knowledge proof-based signup process for social networks. Our prototype authenticates user identities in multiple layers, effectively mitigating fraudulent, cloned, and multiple account creations. Our experiments with n (n = 50) users showed a 100% success rate for our system, highlighting its effectiveness compared to other OSNs.

The proliferation of social media offers a real-time reflection of public sentiments. Sentiment analysis on such platforms yields crucial insights for sectors like market research, politics, business strategy, and public health. In this study, we introduce an innovative framework to examine evolving sentiments in social media comments and understand their wider implications. Utilizing a pre-trained BERT base uncase model, we estimate emotional values from comments and align them with various sentiment trends such as Approval, Toxicity, and Neutral, among others. By leveraging machine learning, we train on a distinctive dataset, correlating emotional values with sentiment trends to generate trend likelihood scores. Through a bottom-up methodology, we compile emotional ratings across comment threads to forecast overarching sentiment scores. Our results reveal that the BERT base uncase model excels in emotional prediction, achieving an AUC of 0.91. Meanwhile, Decision Tree models stand out, registering an F1 score above 0.40 on a macro average basis.