Social media analysis is a fast growing research area aimed at extracting useful information from social media platforms. This paper presents a methodology, called IOM-NN (Iterative Opinion Mining using Neural Networks), for discovering the polarization of social media users during election campaigns characterized by the competition of political factions. The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users. Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules. Such rules are then used to determine the polarization of people towards a faction. The methodology has been assessed on two case studies that analyze the polarization of a large number of Twitter users during the 2018 Italian general election and 2016 US presidential election. The achieved results are very close to the real ones and more accurate than the average of the opinion polls, revealing the high accuracy and effectiveness of the proposed approach. Moreover, our approach has been compared to the most relevant techniques used in the literature (sentiment analysis with NLP, adaptive sentiment analysis, emoji- and hashtag- based polarization) by achieving the best accuracy in estimating the polarization of social media users.

Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Big Data analysis refers to advanced and efficient data mining and machine learning techniques applied to large amount of data. Research work and results in the area of Big Data analysis are continuously rising, and more and more new and efficient architectures, programming models, systems, and data mining algorithms are proposed. Taking into account the most popular programming models for Big Data analysis (MapReduce, Directed Acyclic Graph, Message Passing, Bulk Synchronous Parallel, Workflow and SQL-like), we analysed the features of the main systems implementing them. Such systems are compared using four classification criteria (i.e. level of abstraction, type of parallelism, infrastructure scale and classes of applications) for helping developers and users to identify and select the best solution according to their skills, hardware availability, productivity and application needs. GRAPHICAL ABSTRACT This figure is a word cloud highlighting the most popular words related to Big Data analysis.

Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.