311. Social media bot detection research : review of literatureBlaž Rodič, 2025, professional article Abstract: This study presents a review of research on social media bot detection. Social media bots are used by political and criminal actors for mass distribution of political messages, as well as rumors, conspiracy theories, and other forms of false information. Through the spread of disinformation, bots are eroding the public trust in political and media institutions and integrity of social media. We have examined recent research publication in the field of social media bot detection, including several previous reviews of bot detection research, and identified the methods used in bot detection and issues encountered by researchers. Our review was conducted through a search of 5 main bibliographical databases, which has produced a total of 534 research papers and other publications. This collection was then filtered with exclusion and inclusion criteria to isolate the most pertinent documents, resulting in a focused selection of 49 documents that were analyzed for this review. In the first part of the paper we introduce the phenomenon of fake news within social networks, its connection with social media bot activity, and conclude the introduction with issues caused or exacerbated by bots. In the main part of this paper we first present the results of statistical analysis of the reviewed documents and then introduce the field of social media bot research, followed by an overview of the issues of social media bot detection identified in the reviewed literature, including the evolution of bot concealment techniques and the methodological issues presented in some of the bot detection studies. We then proceed with an overview of the methods and results from the reviewed research papers, structured according to the main methodology used in the examined studies. Our review concludes with examination of the recent trends in social media bot development and related bot detection research.
Keywords: social media, social networks, social media bots, Twitter bots, bot detection, misinformation, disinformation, fake news
Published in ReVIS: 14.04.2025; Views: 286; Downloads: 1
 Link to file |
312. Bridging perceived and actual data quality : automating the framework for governance reliabilityTomaž Podobnikar, 2025, original scientific article Abstract: The discrepancy between perceived and actual data quality, shaped by stakeholders’ interpretations of technical specifications, poses significant challenges in governance, impacting decision-making and stakeholder trust. To address this, we introduce an automated data quality management (DQM) framework, implemented through the NRPvalid toolkit, as a standalone solution incorporating over 100 assessment tools. This framework strengthens data quality evaluation and stakeholder collaboration by systematically bridging subjective perceptions with objective quality metrics. Unlike traditional producer–user models, it accounts for complex, multi-stakeholder interactions to improve data governance. Applied to planned land use (PLU) data, the framework significantly reduces discrepancy, as quantified by error score metrics, and directly enhances building permit issuance by streamlining interactions among administrative units, municipalities, and investors. By evaluating, refining, and seamlessly integrating spatial data into the enterprise spatial information system, this scalable, automated solution supports constant data quality improvement. The DQM and its toolkit have been widely adopted, promoting transparent, reliable, and efficient geospatial data governance.
Keywords: perceived and actual data quality, data quality management, DQM, quality assurance/quality control, QA/QC, spatial data quality, data quality standards, data governance, planned land use, automation, uncertainty management, geospatial
Published in ReVIS: 14.04.2025; Views: 287; Downloads: 1
 Full text (9,08 MB) |
313. A framework for bridging perceived and actual quality through automation : strengthening data reliability and governanceTomaž Podobnikar, 2025, other component parts Abstract: Following spatial data capture, stakeholders often invest significant resources to meet technical specifications. This challenge arises largely from varying interpretations of established standards, resulting in data that fails to meet the requirements for ingestion into the enterprise geospatial ecosystem. A key issue lies in the discrepancy between perceived data quality – how stakeholders understand or interpret the performance of the data, which is aligned with technical specifications – and actual data quality, which reflects objective performance when properly measured. The proposed data quality management (DQM) framework addresses this discrepancy by focusing on key aspects of spatial data quality, with an automated program playing a central role in bridging this divide. The framework enhances stakeholder communication and significantly improves the reliability of data governance by providing a comprehensive evaluation of data quality. This evaluation with the outputs combining error presentation through statistics, georeferenced files, and visualization enables rapid interpretation and error resolution. When applied to planned land use (PLU) data, this solution improved efficiency, enhanced overall data quality, and ensured seamless integration into the enterprise Spatial information system. This resulted in a higher level of maturity in data quality management.
Keywords: quality assurance/quality control (QA/QC), continuous process improvement, spatial data quality, data steward, data governance, planned land use data, perceived vs. actual data quality, geospatial, data quality management (DQM), uncertainty management
Published in ReVIS: 14.04.2025; Views: 329; Downloads: 2
Full text (2,66 MB) |
314. |
315. |
316. |
317. |
318. |
319. Exploring the potential of BCI in education : an experiment in musical trainingRaffaella Folgieri, Claudio Lucchiari, Sergej Gričar, Tea Baldigara, Marisa Gil, 2025, original scientific article Keywords: education, BCI, feedback, music
Published in ReVIS: 10.04.2025; Views: 290; Downloads: 2
Full text (1,95 MB)
This document has many files! More... |
320. |
