Characteristics of Similar-Context Trending Hashtags in Twitter: A Case Study
Lecture Notes in Computer Science, 2020
Twitter is a popular social networking platform that is widely used in discussing and spreading i... more Twitter is a popular social networking platform that is widely used in discussing and spreading information on global events. Twitter trending hashtags have been one of the topics for researcher to study and analyze. Understanding the posting behavior patterns as the information flows increase by rapid events can help in predicting future events or detection manipulation. In this paper, we investigate similar-context trending hashtags to characterize general behavior of specific-trend and generic-trend within same context. We demonstrate an analysis to study and compare such trends based on spatial, temporal, content, and user activity. We found that the characteristics of similar-context trends can be used to predict future generic trends with analogous spatiotemporal, content, and user features. Our results show that more than 70% users participate in location-based hashtag belongs to the location of the hashtag. Generic trends aim to have more influence in users to participate than specific trends with geographical context. The retweet ratio in specific trends is higher than generic trends with more than 79%.
The last few years have revealed that social bots in social networks have become more sophisticat... more The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and aver-age_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).
Smart Online Exam Proctoring Assist for Cheating Detection
Springer eBooks, 2022
Online exams are the most preferred mode of exams in online learning environment. This mode of ex... more Online exams are the most preferred mode of exams in online learning environment. This mode of exam has been even more prevalent and a necessity in the event of a forced closure of face-to-face teaching such as the recent Covid-19 pandemic. Naturally, conducting online exams poses much greater challenge to preserving academic integrity compared to conducting on-site face-to-face exams. As there is no human proctor for policing the examinee on site, the chances of cheating are high. Various online exam proctoring tools are being used by educational institutes worldwide, which offer different solutions to reduce the chances of cheating. The most common technique followed by these tools is recording of video and audio of the examinee during the whole duration of exam. These videos can be analyzed later by human examiner to detect possible cheating case. However, viewing hours of exam videos for each student can be impractical for a large class and thus detecting cheating would be next to impossible. Although some AI-based tools are being used by some proctoring software to raise flags, they are not always very useful. In this paper we propose a cheating detection technique that analyzes an exam video to extract four types of event data, which are then fed to a pre-trained classification model for detecting cheating activity. We formulate the cheating detection problem as a multivariate time-series classification problem by transforming each video into a multivariate time-series representing the time-varying event data extracted from each frame of the video. We have developed a real dataset of cheating videos and conduct extensive experiments with varying video lengths, different deep learning and traditional machine learning models and feature sets, achieving prediction accuracy as high as 97.7%.
Let’s Have a Chat! A Conversation with ChatGPT: Technology, Applications, and Limitations
Artificial Intelligence and Applications
The advent of AI-empowered chatbots capable of constructing human-like sentences and articulating... more The advent of AI-empowered chatbots capable of constructing human-like sentences and articulating cohesive essays has captivated global interest. This paper provides a historical perspective on chatbots, focusing on the technology underpinning the Chat Generative Pre-trained Transformer, better known as ChatGPT. We underscore the potential utility of ChatGPT across a multitude of fields, including healthcare, education, and research. To the best of our knowledge, this is the first review that not only highlights the applications of ChatGPT in multiple domains, but also analyzes its performance on examinations across various disciplines. Despite its promising capabilities, ChatGPT raises numerous ethical and privacy concerns that are meticulously explored in this paper. Acknowledging the current limitations of ChatGPT is crucial in understanding its potential for growth. We also ask ChatGPT to provide its point of view and present its responses to several questions we attempt to answer.
A simple supervised learning model can predict a class from trained data based on the previous le... more A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be applied to supervised learning using a well-trained dataset that covers different data points and has no imbalance issues. This task is challenging when it integrates a semi-supervised learning approach with a dynamic data stream, such as social network data. In this paper, we propose a stream-based evolving bot detection (SEBD) framework for Twitter that uses a deep graph neural network. Our SEBD framework was designed based on multi-view graph attention networks using fellowship links and profile features. It integrates Apache Kafka to enable the Twitter API stream and predict the account type after processing. We used a probably approximately correct (PAC) learning framework to evaluate SEBD’s results. Our o...
Social bots are automated social media accounts governed by software and controlled by humans at ... more Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.
With the continuous progress of renewable energy technology and the large-scale construction of m... more With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find ...
Twitter, as a popular social network, has been targeted by different bot attacks. Detecting socia... more Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. We called the framework ‘Bot-MGAT’, which stands for bot multi-view graph attention network. The framework used both labeled and unlabeled data. We used profile features to reduce the overheads of the feature engineering. We executed our experiments on a recent benchmark dataset that included representative sam...
Metaverse has emerged as a novel technology with the objective to merge the physical world into t... more Metaverse has emerged as a novel technology with the objective to merge the physical world into the virtual world. This technology has seen a lot of interest and investment in recent times from prominent organizations including Facebook which has changed its company name to Meta with the goal of being the leader in developing this technology. Although people in general are excited about the prospects of metaverse due to potential use cases such as virtual meetings and virtual learning environments, there are also concerns due to potential negative consequences. For instance, people are concerned about their data privacy as well as spending a lot of their time on the metaverse leading to negative impacts in real life. Therefore, this research aims to further investigate the public sentiments regarding metaverse on social media. A total of 86565 metaverse-related tweets were used to perform lexicon-based sentiment analysis. Furthermore, various machine and deep learning models with va...
With the wide application of advanced communication and information technology, false data inject... more With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has...
Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible ... more Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are stored on blockchain networks and represent a digital certificate of ownership that cannot be forged. NFTs can be incorporated into a smart contract which allows the owner to benefit from a future sale percentage. While digital art producers can benefit immensely with NFTs, their production is time consuming. Therefore, this paper explores the possibility of using generative adversarial networks (GANs) for automatic generation of digital arts. GANs are deep learning architectures that are widely and effectively used for synthesis of audio, images, and video contents. However, their application to NFT arts have been limited. In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. Results from the qualitative case study indicate that the generated artworks are comparable to the r...
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