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Due to the advancement of technology, and the promotion of smartphones, using social media got more and more popular. Nowadays, it has become an undeniable part of people's lives. So, they will create a flow of information by the content they share every single moment. Analyzing this information helps us to have a better understanding of users, their needs, their tendencies and classify them into different groups based on their behavior. These behaviors are various and due to some extracted features, it is possible to categorize the users into different categories. In this paper, we are going to focus on Twitter users and the AusOpen Tennis championship event as a case study. We define the attributions describing each class and then extract data and identify features that are more correlated to each type of user and then label user type based on the reasoning model. The results contain 4 groups of users; Verified accounts, Influencers, Regular profiles, and Fake profiles.

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Maintaining a healthy cyber society is a great challenge due to the users’ freedom of expression and behavior. This can be solved by monitoring and analyzing the users’ behavior and taking proper actions. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using graph analysis methods. Then, the users’ behavioral patterns are analyzed by applying metadata analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then, in the abnormal behavior detection and filtering component, the interesting profiles are selected for further examinations. Finally, in the contextual analysis component, the contents are analyzed using natural language processing techniques; a binary text classification model (SVM (Support Vector Machine) + TF-IDF (Term Frequency—Inverse Document Frequency) with 88.89% accuracy) is used to detect if a tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN (Feed-Forward Neural Network) with 80% accuracy), because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (the police) with suggestions to control hate speech or terrorist propaganda.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. However, first, it is necessary to know how and from which aspects to compare users. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official TwitterAPI. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for the comparison of the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

These days by a high increase in the amount of computation and big data gathering and analysis, everybody needs more resources. Buying more computational and storage resources are so expensive. However, cloud computing solved this problem by providing a "pay as you go" plans therefor, users will only pay for resources that they used. However, using this technology has its challenges. One of them is resource management, which is focusing on the methodologies of dedicating resources to the users with the minimum of waste. In this paper, we propose a novel energy-aware resource management technique, using the concepts of both joint VM and container consolidation approach and deep Q-Learning algorithm for green computing in cloud data centers in order to minimize the waste of resources, migration rate, and energy.