Examinando por Autor "Ribeiro-Soriano, Domingo"

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Assessing behavioral data science privacy issues in government artificial intelligence deployment
(Elsevier, 2022) Saura, Jose Ramon; Ribeiro-Soriano, Domingo; Palacios-Marqués, Daniel
In today’s global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens’ behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens’ privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens’ concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens’ privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens’ privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.
Exploring the boundaries of open innovation: Evidence from social media mining
(Elsevier, 2022) Saura, Jose Ramon; Palacios-Marqués, Daniel; Ribeiro-Soriano, Domingo
Technological development of the last several decades has driven open innovation towards organizational, business, social, and economic change. Open innovation has emerged as the main driver of change in a business sector that needs to be flexible and resilient, rapidly adapting to change through innovation. In this context, the present study aimed to explore the limits of open innovation by extracting evidence from user-generated content (UGC) on Twitter using social media mining. To this end, in terms of the methodology, we first applied machine learning Sentiment Analysis algorithm texted using Support Vector Classifier, Multinomial Naïve Bayes, Logistic Regression, and Random Forest Classifier to divide the sample of n = 586.348 tweets into three groups expressing the following three sentiments: positive, negative, and neutral. Then, we used a mathematical topic modeling algorithm known as Latent Dirichlet allocation to analyze the tweet databases. Finally, Python was used to develop textual analysis techniques under the theoretical framework of Computer-Aided Text Analysis and Natural Language Processing. The results revealed that, in the tweets dataset, there were eight topics. Of these topics, two contained tweets expressing negative sentiments (Culture and Business Models/Management), three topics contained tweets expressing positive sentiments (Communities, Creative projects and Ideas), and three topics contained tweets expressing neutral sentiments (Entrepreneurship, Teams and Technology). These topics are discussed in the context of limitations, risks, and characteristics of open innovation according to the UGC on Twitter. The paper concludes with the formulation of 20 limits of open innovation and 27 research questions for further research on open innovation, as well as a discussion of theoretical and practical implications of the study. UGCUser-Generated ContentUGDUser-Generated DataCATAComputer-Aided Text AnalysisNLPNatural Language ProcessingLDALatent Dirichlet AllocationSVCSupport Vector ClassifierMNBMultinomial Naïve BayesLRLogistic RegressionRFCRandom Forest Classifier
Leveraging SMEs technologies adoption in the Covid-19 pandemic: a case study on Twitter-based user-generated content
(Springer, 2023) Saura, Jose Ramon; Palacios-Marqués, Daniel; Ribeiro-Soriano, Domingo
The COVID-19 pandemic has caused many entrepreneurs and small and medium enterprises (SMEs) to adapt their business models and business strategies to the consequences caused by the pandemic. In order to identify the main innovations and technologies adopted by SMEs in the pandemic, in the present study, we used a database of 56,941 tweets related to the coronavirus to identify those that contained the hashtag #SMEs. The fnal sample was analyzed using several data-mining techniques such as sentiment analysis, topic modeling and textual analysis. The theoretical perspectives adopted in the present study were Computer-Aided Text Analysis, User-Generated Content and Natural Language Processing. The results of our analysis helped us to identify 15 topics (7 positive: Free support against Covid-19, Webinars tools, Time Optimizer and efciency, Business solutions tools, Advisors tools, Software for process support and Back-up tools; 4 negative: Government support, Payment systems, Cybersecurity problems and Customers solutions in Cloud, and and 4 neutral: Social media and e-commerce, Specialized startups software, CRMs and Finance and Big data analysis tools). The results of the present study suggest that SMEs have used a variety of digital tools and strategies to adapt to the changing market conditions brought on by the pandemic, and have been proactive in adopting new technologies to continue to operate and reach customers in a connected era. Future research should be directed towards understanding the long-term efects of these technologies and strategies on entrepreneurial growth and value creation, as well as the sustainability of SMEs in the new era based on data-driven decisions.
Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks
(Springer, 2023) Saura, Jose Ramon; Palacios-Marqués, Daniel; Ribeiro-Soriano, Domingo
In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these afect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifer (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifer (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identifed: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.
Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research
(Elsevier, 2021) Ramon Saura, Jose; Ribeiro-Soriano, Domingo; Palacios-Marqués, Daniel
The new business challenges in the B2B sector are determined by connected ecosystems, where data-driven decision making is crucial for successful strategies. At the same time, the use of digital marketing as a communication and sales channel has led to the need and use of Customer Relationship Management (CRM) systems to correctly manage company information. The understanding of B2B traditional Marketing strategies that use CRMs that work with Artificial Intelligence (AI) has been studied, however, research focused on the understanding and application of these technologies in B2B digital marketing is scarce. To cover this gap in the literature, this study develops a literature review on the main academic contributions in this area. To visualize the outcomes of the literature review, the results are then analyzed using a statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. The research results classify the types of CRMs and their typologies and explore the main techniques and uses of AI-based CRMs in B2B digital marketing. In addition, a discussion, directions and propositions for future research are presented.
Setting Privacy “by Default” in Social IoT: Theorizing the Challenges and Directions in Big Data Research
(Elsevier, 2021) Saura, José Ramón; Ribeiro-Soriano, Domingo; Palacios-Marqués, Daniel
The social Internet of Things (SIoT) shares large amounts of data that are then processed by other Internet of Thing (IoT) devices, which results in the generation, collection, and treatment of databases to be analyzed afterwards with Big Data techniques. This paradigm has given rise to users' concerns about their privacy, particularly with regard to whether users have to use a smart handling (self-establishment and self-management) in order to correctly install the SIoT, ensuring the privacy of the SIot-generated content and data. In this context, the present study aims to identify and explore the main perspectives that define user privacy in the SIoT; our ultimate goal is to accumulate new knowledge on the adoption and use of the concept of privacy “by default” in the scientific literature. To this end, we undertake a literature review of the main contributions on the topic of privacy in SIoT and Big Data processing. Based on the results, we formulate the following five areas of application of SIoT, including 29 key points relative to the concept of privacy “by default”: (i) SIoT data collection and privacy; (ii) SIoT security; (iii) threats for SIoT devices; (iv) SIoT devices mandatory functions; and (v) SIoT and Big Data processing and analytics. In addition, we outline six research propositions and discuss six challenges for the SIoT industry. The results are theorized for the future development of research on SIoT privacy by “default” and Big Data processing.
Using data mining techniques to explore security issues in smart living environments in Twitter
(Elsevier, 2021) Saura, Jose Ramon; Palacios-Marqués, Daniel; Ribeiro-Soriano, Domingo
In present-day in consumers’ homes, there are millions of Internet-connected devices that are known to jointly represent the Internet of Things (IoT). The development of the IoT industry has led to the emergence of connected devices and home assistants that create smart living environments. However, the continuously generated data accumulated by these connected devices create security issues and raise user’s privacy concerns. The present study aims to explore the main security issues in smart living environments using data mining techniques. To this end, we applied a three-sentence data mining analysis of 938,258 tweets collected from Twitter under the user-generated data (UGD) framework. First, sentiment analysis was applied using Textblob which was tested with support vector classifier, multinomial naïve bayes, logistic regression, and random forest classifier; as a result, the analyzed tweets were divided into those expressing positive, negative, and neutral sentiment. Next, a Latent Dirichlet Allocation (LDA) algorithm was applied to divide the sample into topics related to security issues in smart living environments. Finally, the insights were extracted by applying a textual analysis process in Python validated with the analysis of frequency and weighted percentage variables and calculating the statistical measure known as mutual information (MI) to analyze the identified n-grams (unigrams and bigrams). As a result of the research 10 topics were identified in which we found that the main security issues are malware, cybersecurity attacks, data storing vulnerabilities, the use of testing software in IoT, and possible leaks due to the lack of user experience. We discussed different circumstances and causes that may affect user security and privacy when using IoT devices and emphasized the importance of UGC in the processing of personal data of IoT device users.