Nowadays, multiple phenomena have promoted an impact on society, constituting in some cases, not only a contribution of benefits but also of risks. Among them, the fake news phenomenon is considered one of the most burning phenomena today due to the risk it poses to society. In view of this situation, the research community has carried out numerous studies that seek to address this issue from a multidisciplinary perspective. Based on this, the objective of this work was to analyze the productivity and, therefore, the impact of this topic in the research community. To this end, this work advocated a scientometric-type methodology, through scientometric laws, impact indicators, and scientific evolution of 640 publications of the web of science (WOS). The results showed the impact of the fake news discipline today, which is considered an emerging issue that is of interest to many knowledge disciplines around the world. Likewise, the results showed that the publications not only have a focus on analyzing the veracity or not of the news, but that it begins to vertebrate a new line of an investigation directed to the informational education and towards the prevention of the consumption of this type of news through the internet.

https://www.mdpi.com/2076-0760/9/5/73/pdf

The Impact of Term Fake News on the Scientific Community. Scientific Performance and Mapping in Web of Science

PurposeArtificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery.Design/methodology/approachThis study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries.FindingsThis study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework.Research limitations/implicationsIn research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation.Practical implicationsIn practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution.Social implicationsThe enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community.Originality/valueAI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

Editorial: 40th anniversary: contemporary library research

Abstract Purpose In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albeit some single-class ones have been recently proposed. Therefore, the only way to classify users in Ethereum are online services (e.g., Etherscan), where users are classified after a request from them. However, the fraction of users thus classified is low. To address this issue, we present an automatic approach for a multi-class classification of Ethereum users based on their past behavior. 

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Defining user spectra to classify Ethereum users based on their behavior

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This paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots,Descriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017 Four scopes considered to classify the articles as technology, service, user and resource It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes,A review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies In this researches, librarian robots mostly designed for detection and replacing books on the shelf Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research Our conclusion is that we need to develop research in the area of smart resources,This study has a new approach to the literature review in this area We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific

Intelligent libraries: a review on expert systems, artificial intelligence, and robot

Finding the most suitable co-author is one of the most important ways to conduct effective research in scientific fields. Data science has contributed to achieving this possibility significantly. The present study aims at designing a mathematical model of co-author recommender system in bioinformatics using graph mining techniques and big data applications. The present study employed an applied-developmental research method and a mixed-methods research design. The research population consisted of all scientific products in bioinformatics in the PubMed database. To achieve the research objectives, the most appropriate effective features in choosing a co-author were selected, prioritized, and weighted by experts. Then, they were weighted using graph mining techniques and big data applications. Finally, the mathematical co-author recommender system model in bioinformatics was presented. Data analysis instruments included Expert Choice, Excel, Spark, Scala and Python programming languages in a big data server. The research was conducted in four steps: (1) identifying and prioritizing the criteria effective in choosing a co-author using AHP; (2) determining the correlation degree of articles based on the criteria obtained from step 1 using algorithms and big data applications; (3) developing a mathematical co-author recommender system model; and (4) evaluating the developed mathematical model. Findings showed that the journal titles and citations criteria have the highest weight while the abstract has the lowest weight in the mathematical co-author recommender system model. The accuracy of the proposed model was 72.26. It was concluded that using content-based features and expert opinions have high potentials in recommending the most appropriate co-authors. It is expected that the proposed co-author recommender system model can provide appropriate recommendations for choosing co-authors on various fields in different contexts of scientific information. The most important innovation of this model is the use of a combination of expert opinions and systemic weights, which can accelerate the finding of co-authors and consequently saving time and achieving a greater quality of scientific products.

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Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applications

Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.

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Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

This research was carried out using the bibliometric method to thematically analyze the articles on IoT in the Web of Science with Hierarchical Agglomerative Clustering approach. First, the descriptors of the related articles published from 2002 to 2016 were extracted from WoS, by conducting a keyword search using the “Internet of Things” keyword. Data analysis and clustering were carried out in SPSS, UCINET, and PreMap. The analysis results revealed that the scientific literature published on IoT during the period had grown exponentially, with an approximately 48% growth rate in the last two years of the study period (i.e. 2015 and 2016). After analyzing the themes of the documents, the resulting concepts were classified into twelve clusters. The twelve main clusters included: Privacy and Security, Authentication and Identification, Computing, Standards and Protocols, IoT as a component, Big Data, Architecture, Applied New Techniques in IoT, Application, Connection and Communication Tools, Wireless Network Protocols, and Wireless Sensor Networks.

Asefeh Asemi

Papers

Intelligent libraries: a review on expert systems, artificial intelligence, and robot

Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applications

Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

A Thematic Analysis of the Articles on the Internet of Things in the Web of Science With HAC Approach