In recent years, Reddit has attracted the interest of many researchers due to its popularity all over the world. In this article, we aim at providing a contribution to the knowledge of this social ...network by investigating three of its aspects, interesting from the scientific viewpoint, and, at the same time, by analysing a large number of applications. In particular, we first propose a definition and an analysis of several stereotypes of both subreddits and authors. This analysis is coupled with the definition of three possible orthogonal taxonomies that help us to classify stereotypes in an appropriate way. Then, we investigate the possible existence of author assortativity in this social medium; specifically, we focus on co-posters, that is, authors who submitted posts on the same subreddit.
The COVID-19 outbreak impacted almost all the aspects of ordinary life. In this context, social networks quickly started playing the role of a sounding board for the content produced by people. ...Studying how dramatic events affect the way people interact with each other and react to poorly known situations is recognized as a relevant research task. Since automatically identifying country-based COVID-19 social posts on generalized social networks, like Twitter and Facebook, is a difficult task, in this work we concentrate on Reddit megathreads, which provide a unique opportunity to study focused reactions of people by both topic and country. We analyze specific reactions and we compare them with a “normal” period, not affected by the pandemic; in particular, we consider structural variations in social posting behavior, emotional reactions under the Plutchik model of basic emotions, and emotional reactions under unconventional emotions, such as skepticism, particularly relevant in the COVID-19 context.
The analysis of people’s comments in social platforms is a widely investigated topic because comments are the place where people show their spontaneity most clearly. In this article, we present a ...network-based data structure and a related approach to represent and manage the underlying semantics of a set of comments. Our approach is based on the extraction of text patterns that take into account not only the frequency, but also the utility of the analysed comments. Our data structure and approach are ‘multidimensional’ and ‘holistic’, in the sense that they can simultaneously handle content semantics from multiple perspectives. They are also easily extensible, because additional content semantics perspectives can be easily added to them. Furthermore, our approach is able to evaluate the semantic similarity of two sets of comments. In this article, we also illustrate the results of several tests we conducted on Reddit comments, even if our approach can be applied to any social platform. Finally, we provide an overview of some possible applications of this research.
Alignment of Microarray Data Cauteruccio, Francesco
Methods in molecular biology (Clifton, N.J.),
2022, Volume:
2401
Journal Article
The aim in microarray data analysis is to discover patterns of gene expression and to identify similar genes. Simply comparing new gene sequences to known DNA sequences often does not reveal the ...function of a new gene; thus, more sophisticated techniques are in order. Nowadays, data mining techniques, and in particular the clustering process, play an important role in bioinformatics. To analyze vast amounts of data can be difficult; thus, a way to cluster similar data is needed. This chapter is devoted to illustrate the general data mining approach used in microarray data analysis, combining clustering, alignment and similarity, and to highlight a novel similarity measure capable of capturing hidden correlations between data.
The concept of scope was introduced in Social Network Analysis to assess the authoritativeness and convincing ability of a user toward other users on one or more social platforms. It has been studied ...in the past in some specific contexts, for example to assess the ability of a user to spread information on Twitter. In this paper, we propose a new investigation on scope, as we want to assess the scope of the sentiment of a user on a topic. We also propose a multi-dimensional definition of scope. In fact, besides the traditional spatial scope, we introduce the temporal one, which has never been addressed in the literature, and propose a model that allows the concept of scope to be extended to further dimensions in the future. Furthermore, we propose an approach and a related set of parameters for measuring the scope of the sentiment of a user on a topic in a social network. Finally, we illustrate the results of an experimental campaign we conducted to evaluate the proposed framework on a dataset derived from Reddit. The main novelties of this paper are: (i) a multi-dimensional view of scope; (ii) the introduction of the concept of sentiment scope; (iii) the definition of a general framework capable of analyzing the sentiment scope related to any subject on any social network.
Sentiment analysis (SA), also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone behind a piece of text. It involves analyzing ...the text to identify whether it expresses a positive, negative, or neutral sentiment. SA can be applied to various types of text data such as social media posts, customer reviews, news articles, and more. This experiment is based on the Internet Movie Database (IMDB) dataset, which comprises movie reviews and the positive or negative labels related to them. Our research experiment’s objective is to identify the model with the best accuracy and the most generality. Text preprocessing is the first and most critical phase in an NLP system since it significantly impacts the overall accuracy of the classification algorithms. The experiment implements unsupervised sentiment classification algorithms including Valence Aware Dictionary and sentiment Reasoner (VADER) and TextBlob. We also examine the supervised sentiment classifications methods such as Naïve Bayes (Bernoulli NB and Multinomial NB). The Term Frequency-Inverse Document Frequency (TFIDF) model is used to feature selection and extractions. The combination of Multinomial NB and TFIDF achieves the highest accuracy, 87.63%, for both classification reports based on our experiment result.
Wash trading is considered a highly inopportune and illegal behavior in regulated markets. Instead, it is practiced in unregulated markets, such as cryptocurrency or NFT (Non-Fungible Tokens) ...markets. Regarding the latter, in the past many researchers have been interested in this phenomenon from an “ex-ante” perspective, aiming to identify and classify wash trading activities before or at the exact time they happen. In this paper, we want to investigate the phenomenon of wash trading in the NFT market from a completely different perspective, namely “ex-post”. Our ultimate goal is to analyze wash trading activities in the past to understand whether the game is worth the candle, i.e., whether these illicit activities actually lead to a significant profit for their perpetrators. To the best of our knowledge, this is the first paper in the literature that attempts to answer this question in a “structured” way. The efforts to answer this question have enabled us to make some additional contributions to the literature in this research area. They are: (i) a framework to support future “ex-post” analyses of the NFT wash trading phenomenon; (ii) a new dataset on wash trading transactions involving NFTs that can support further future investigations of this phenomenon; (iii) a set of insights of the NFT wash trading phenomenon extracted at the end of an experimental campaign.
Electronic sports (eSports) is competitive video gaming that is coordinated and managed by sporting organizations. While traditional sports have thrived on spectatorship and the intense emotional ...experiences of fans, there has been limited attention directed towards the emotional aspect of eSports spectatorship. In this paper, we introduce a first contribution to this field, presenting an in-depth, mixed-study investigation of eSports spectatorship and emotional experiences during the 2020 League of Legends’ World Championship. Our investigation is based on an extensive dataset comprising over 40,500 comments from 3,100 spectators posted during the event on the social media platform Reddit. We provide both computational and qualitative analyses of spectators’ experience during the event. The former employs social network-based models and techniques, while the latter includes thematic analysis. Our findings reveal that spectators supporting the same team tend to engage in cohesive discussions, while interactions among those supporting different teams are less prominent. Additionally, we explore various factors that trigger spectators’ emotions during the event, including interactions among fan groups and the local context. The methodology underpinning our investigation is general and enables the study of eSports spectatorship from a heterogeneous perspective.
•A mixed-methods methodology for investigating eSports spectatorship is presented.•Social interactions and emotional dimensions are investigated through network-based and thematic analyses.•Social media discussions of a real eSport major event are collected and analyzed.•Distinctive features of eSports spectator behavior have a considerable impact on spectators’ presence and interaction.•Spectators’ emotional work is highlighted as supporting the spectator ecosystem in the eSports context.
The increasing use of wireless communication and IoT devices has raised concerns about security, particularly with regard to attacks on the Routing Protocol for Low-Power and Lossy Networks (RPL), ...such as the wormhole attack. In this study, the authors have used the trust concept called PCC-RPL (Parental Change Control RPL) over communicating nodes on IoT networks which prevents unsolicited parent changes by utilizing the trust concept. The aim of this study is to make the RPL protocol more secure by using a Subjective Logic Framework-based trust model to detect and mitigate a wormhole attack. The study evaluates the trust-based designed framework known as SLF-RPL (Subjective Logical Framework-Routing Protocol for Low-Power and Lossy Networks) over various key parameters, i.e., low energy consumption, packet loss ratio and attack detection rate. The achieved results were conducted using a Contiki OS-based Cooja Network simulator with 30, 60, and 90 nodes with respect to a 1:10 malicious node ratio and compared with the existing PCC-RPL protocol. The results show that the proposed SLF-RPL framework demonstrates higher efficiency (0.0504 J to 0.0728 J out of 1 J) than PCC-RPL (0.065 J to 0.0963 J out of 1 J) in terms of energy consumption at the node level, a decreased packet loss ratio of 16% at the node level, and an increased attack detection rate at network level from 0.42 to 0.55 in comparison with PCC-RPL.
PDF
