Social media platforms have transformed the world into a global village by providing a unique platform for unrestricted communication and opinion sharing. However, this freedom is used to spread misinformation and disrupt societal harmony. To combat misinformation and fake news on social media platforms, multifarious AI applications have been developed to detect such content in various languages and data modalities, including text, images, and videos. To establish a distinctive platform that fosters the rapid development of AI-driven fake news detectors, researchers have published several review articles in recent years. However, many of these articles are outdated, and lack comprehensiveness, particularly regarding recent trends, public datasets, representation learning methods, and classifiers details. The limited scope of articles hindered their ability to provide in-depth information on predictors’ language specificity, data modality focus, and state-of-the-art performance across diverse languages. This paper offers a unique platform with detailed information on these aspects to address this gap. It offers a detailed roadmap for understanding the scope, strengths, and limitations of existing review articles on the subject. To support the development of new benchmark datasets and more accurate predictors, it conducts an in-depth analysis of the definitions and perspectives of fake news within the context of existing literature. This analysis provides a more comprehensive definition and basic concepts of fake news. In addition, in the last 8 years, 310 fake news detection articles have been thoroughly investigated.Within the landscape of these articles, it presents details of languages, datasets, used data modalities, predictor architecture designs, and their performance metrics.

In today’s digital era, the escalating phenomenon of cyberbullying is a pervasive and growing concern. With the increasing prevalence of social media platforms, such as Twitter, online abusive behavior has become a significant issue that often leads to unpleasant experiences for users. Manual detection of abnormal and bullying behavior within the realm of social media is inherently not scalable. Moreover, most existing studies on cyberbullying detection have been predominantly conducted in English and very limited work has been done on Urdu (a widely used language in Asia). This paper presents an approach for detecting cyberbullying in Roman Urdu tweets and identifying abuser profiles on Twitter. Firstly, we develop a text corpus of Roman Urdu tweets with user profile data. Subsequently, we employ Gated Recurrent Unit (GRU) model coupled with the application of word2vec technique for word embedding to develop a cyberbullying detection model. Furthermore, we present temporal abusive tweet probability analysis method to provide a nuanced analysis of the number of bullying and non-bullying tweets sent by individuals within a specific time interval. To evaluate the performance, we compare the GRU-based approach with other machine learning models. The results show that the GRU model with lexical normalization gives the best results with an accuracy of 97% and F1-measure of 97%.

The Internet has been vulnerable to several attacks as it has expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). The three main types of attacks most frequently reported in the current period are viruses, DoS attacks, and DDoS attacks. Advanced DDoS and DoS attacks are too complex for traditional security solutions, such as intrusion detection systems and firewalls, to detect. The combination of machine learning methods with AI-based machine learning has led to the introduction of several novel attack detection systems. Due to their remarkable performance, machine learning models, in particular, have been essential in identifying DDoS attacks. However, there is a considerable gap in the work on real-time detection of such attacks. This study uses Mininet with the POX Controller to simulate an environment to detect DDoS attacks in real-time settings. The CICDDoS2019 dataset identifies and classifies such attacks in the simulated environment. In addition, a virtual software-defined network (SDN) is used to collect network information from the surrounding area. When an attack occurs, the pre-trained models are used to analyze the traffic and predict the attack in real-time. The performance of the proposed methodology is evaluated based on two metrics: accuracy and detection time. The results reveal that the proposed model achieves an accuracy of 99% within 1 s of the detection time.