Cyberbullying detection: Current trends and future directions

As we see the rapid growth of Web 2.0; online social networks-OSNs and online communications which provides platforms to connect each other all over the world and express the opinion and interests. Online users are generating big amount of data every day. As result, OSNs are providing opportunities for cybercrime and cyberbullying activities. Cyberbullying is online harassing, humiliating or insulting an online user through sending text messages of threatening or harassing using online tool of communication. This research paper provides the comprehensive overview of cyberbullying that occurs usually on OSNs websites and provides current approaches to tackle cyberbullying on OSNs. It also highlights the issues and challenges in cyberbullying detection system and outline the future direction for research in this area. The topic discussed in this paper start with introduction of OSNs, cyberbullying, types of cyberbullying, and data accessibility is reviewed. Lastly, issues and challenges concerning cyberbullying detection are highlighted

Cyberbullying severity detection: A machine learning approach.

With widespread usage of online social networks and its popularity, social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. In this study, we have proposed a cyberbullying detection framework to generate features from Twitter content by leveraging a pointwise mutual information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation. Extracted features were applied with Naïve Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine algorithms. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa, classifier accuracy and f-measure metrics, as well as in a binary setting. These results indicate that our proposed framework provides a feasible solution to detect cyberbullying behavior and its severity in online social networks. Finally, we compared the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection

Cyberbullying detection in Roman Urdu language using lexicon based approach

Nowadays, online social networks (OSNs) have become integral part of our daily life and online users of social media are massively growing. The increasing use of OSNs by users leads to large amount of user communication data. This study focuses on OSNs users who communicate in Roman Urdu (Urdu language written in English alphabets). Pakistan alone has over 44 million OSNs users who communicate in Roman Urdu. In this paper, we addressed the issue of cyberbullying behavior on Twitter platform, where users use Roman Urdu as medium of their communication. To the best of our knowledge, this is the first study addressing cyberbullying behavior in Roman Urdu. To address this issue, we developed supervised machine learning method and proposed a lexicon-based model with set of features derived from Twitter. An evaluation model shows that the developed model attained results with area under receiver operating characteristics curve (AUC) of 0.986 and f-measure of 0.984. These results indicate that the proposed lexicon-based method gives feasible solution for detecting cyberbullying behavior in Roman Urdu in OSNs. Finally, we compared results achieved with our proposed lexicon-based method and the results obtained from other well-known models. The comparison results show the significance of our proposed model

RNA-RBP interactions recognition using multi-label learning and feature attention allocation

Abstract In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures the complex characteristics of RNA and recognizes corresponding RBPs through its dual-module architecture. The first module employs convolutional neural networks (CNNs) for intricate feature extraction from RNA sequences, enabling the model to discern nuanced patterns and attributes. The second module is a multi-view multi-label classification system incorporating a feature attention mechanism. The second module is a multi-view multi-label classification system that utilizes a feature attention mechanism. This mechanism is designed to intricately analyze and distinguish between common and unique deep features derived from the diverse RNA characteristics. To evaluate the model's efficacy, extensive experiments were conducted on a comprehensive RNA-RBP interaction dataset. The results emphasize substantial improvements in the model's ability to predict RNA-RBP interactions compared to existing methodologies. This advancement emphasizes the model's potential in contributing to the understanding of RNA-mediated biological processes and disease etiology

Adaption of Distance Learning to Continue the Academic Year Amid COVID-19 Lockdown

This work investigates the use of distance learning in saving students' academic year amid COVID-19 lockdown. It assesses the adoption of distance learning using various online application tools that have gained widespread attention during the coronavirus infectious disease 2019 (COVID-19) pandemic. Distance learning thrives as a legitimate alternative to classroom instructions, as major cities around the globe are locked down amid the COVID-19 pandemic. To save the academic year, educational institutions have reacted to the situation impulsively and adopted distance learning platforms using online resources. This study surveyed random undergraduate students to identify the impact of trust in formal and informal information sources, awareness and the readiness to adopt distance learning. In this study, we have hypothesized that adopting distance learning is an outcome of situational awareness and readiness, which is achieved by the trust in the information sources related to distance learning. The findings indicate that trust in information sources such as institute and media information or interpersonal communication related to distance learning programs is correlated with awareness (β=0.423, t=12.296, p=0.000) and contribute to readiness (β=0.593, t=28.762, p=0.001). The structural model path coefficient indicates that readiness strongly influences the adoption of distance learning (β=0.660, t=12.798, p=0.000) amid the COVID-19 pandemic. Our proposed model recorded a predictive relevance (Q2) of 0.377 for awareness, 0.559 for readiness, and 0.309 for the adoption of distance learning, which explains how well the model and its parameter estimates reconstruct the values. This study concludes with implications for further research in this area