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Convergence of Time-Series Analytics and Social Media Intelligence
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Convergence of Time-Series Analytics and Social Media Intelligence

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Applications".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2915

Editors

Prof. Dr. Tatiana Atanasova

E-Mail Website
Guest Editor
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl.2, 1113 Sofia, Bulgaria
Interests: knowledge-based systems; complex control systems; learning structures; information society technologies
Dr. Francesco Camastra

E-Mail Website
Guest Editor
Department of Science and Technology, University of Naples Parthenope, 80133 Napoli, Italy
Interests: machine learning; kernel methods; lustering; intrinsic dimension estimation; gesture recognition; handwriting recognition; time series prediction; dimensionality reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The dynamics of all spheres of life of modern society are reflected in the rapid penetration of artificial intelligence (AI) into all forms of digitized systems and services. This includes organizational, social, economic, and governmental fields, revolutionizing the way in which information is processed and decisions are made. Time series in numerical or symbolic form provide information that can be analysed via artificial intelligence and machine learning (ML) algorithms to generate and test predictive models. By conducting systematic research and experiments, it is possible to optimize and improve the performances of these predictive models. This involves exploring new approaches for studying time-series data, as well as developing methodologies for their application in various practical contexts. The focus is on testing the capabilities of predictive models in real-world scenarios, while also considering the risks associated with cybersecurity.

This Special Issue accepts interdisciplinary articles that address synergy between methods for time-series analysis, AI, and ML to foster innovation, efficiency, and sustainability, having been launched in response to the need to develop intelligent, adaptive systems.

Prof. Dr. Tatiana Atanasova
Dr. Francesco Camastra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • time-series analysis
  • anomaly detection
  • sentiment analysis
  • classification algorithms
  • machine learning
  • social media intelligence
  • cybersecurity

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Published Papers (2 papers)

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Research

15 pages, 362 KB  
Article
Time-Series and Social-Media Threat Analytics over a Deployed Cyber-Threat Knowledge Graph
by Kalin Kopanov, Kristina Dineva, Ivaylo Keremidarski, Velizar Varbanov, Vitalii Toderian, Petrica Butusina and Andrei Ionut Damian
Information 2026, 17(8), 794; https://doi.org/10.3390/info17080794 - 19 Aug 2026
Viewed by 243
Abstract
Security teams decide which vulnerabilities to patch first, which alerts to trust, and whether social media warns of new threats earlier than the official feeds. We answer these questions by directly measuring EdgeGuard, a deployed cyber-threat knowledge graph that merges eleven public threat [...] Read more.
Security teams decide which vulnerabilities to patch first, which alerts to trust, and whether social media warns of new threats earlier than the official feeds. We answer these questions by directly measuring EdgeGuard, a deployed cyber-threat knowledge graph that merges eleven public threat feeds into one Neo4j database via MISP (an open threat-sharing platform) and the STIX 2.1 exchange format, recording for every entry which feed reported it and when. These records let the graph be read as a time series. Read this way, it shows that half of the vulnerabilities known to have been exploited were listed as exploited within five days of their publication (352 cases), and that a large ingestion spike in early 2026 came from a single feed rather than a real attack wave. Benchmarked against 10,000 threat-related social-media posts, the graph already held 96% of the actionable vulnerabilities the posts discussed and reported them at least as quickly, while most posts carried no actionable signal and social media led only in early warning of active exploitation. A crowd-sourced community layer additionally supplies the only intelligence tagged by industry sector. The deployed graph is thus a clean, timely, and comprehensive base, and live social ingestion a small, targeted enhancement. Full article
(This article belongs to the Special Issue Convergence of Time-Series Analytics and Social Media Intelligence)
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20 pages, 526 KB  
Article
Profile-Free Behavioral Characterization of Bot-like Activity in a Political Reply Ecosystem on X: A Case Study
by Kalin Kopanov and Tatiana Atanasova
Information 2026, 17(8), 727; https://doi.org/10.3390/info17080727 - 28 Jul 2026
Viewed by 1862
Abstract
Coordinated and bot-like activity on social media is usually studied with supervised detectors that need rich account data such as profiles, timelines, and follower networks, which is increasingly hard to obtain. We ask what can be established about a single account’s reply ecosystem [...] Read more.
Coordinated and bot-like activity on social media is usually studied with supervised detectors that need rich account data such as profiles, timelines, and follower networks, which is increasingly hard to obtain. We ask what can be established about a single account’s reply ecosystem from its publicly visible posts and replies alone, with no profiles, timelines, or follower data. In a case study of the reply ecosystem of an official political party account (23,953 replies by 1985 accounts, December 2025 to January 2026), we compute profile-free behavioral features covering text duplication, character-level entropy, timing regularity, reply latency, and post coverage, complemented by a co-commenting network analysis, and group active accounts with unsupervised density-based clustering. The clustering, combined with two transparent labeling rules, separates three behavioral tiers: templated amplifiers defined by text reuse, persistent responders with human-like text but extreme volume and coverage, and an organic remainder. The two non-organic tiers comprise 5.4% of accounts, yet produce 53.5% of all comments, a composition that is stable under resampling and threshold sensitivity analysis, with a failure mode that is only conservative, since over-strict settings leave a tier unassigned rather than reshaping it. The platform’s own spam flags, never used as input, rise steadily from organic accounts to templated amplifiers, consistent with the behavioral grouping. Full article
(This article belongs to the Special Issue Convergence of Time-Series Analytics and Social Media Intelligence)
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