Special Issue "Social and Semantic Trends: Tools and Applications"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Prof. Dr. Evaggelos Spyrou
Website
Guest Editor
Department of Informatics and Telecommunications, University of Thessaly, Greece
Interests: semantic multimedia analysis indexing and retrieval; feature extraction and modeling; visual context modeling; human activity recognition
Special Issues and Collections in MDPI journals
Prof. Dr. Phivos Mylonas
Website
Guest Editor
Department of Informatics, Ionion University, Kerkira, Greece
Interests: knowledge-assisted multimedia content analysis; multimedia information retrieval; user modeling; user profiling; context representation and analysis; human-computer interaction
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Our times may be described by the massive exploitation of social information and semantics in our everyday life. Social data mining could be described as one of the most important and challenging tasks of our era. As this observation applies both to the research community, which is faced with enormous challenges deriving from big data management, as well as new emerging disciplines in terms, for instance, of social information handling and related social tools and applications, it is becoming evident that new approaches have to be introduced and invented in order to efficiently handle the huge amounts of such data.

The aim of the SMAP workshop series, and by association of the proposed Special Issue, is formed around two main axes. The first axis focuses on the data, information and knowledge extraction, pre-processing, manipulation, and analysis, whereas the second axis focuses on the utilization of the aforementioned outcomes towards efficient tools and application production—the ultimate task being, of course, the facilitation of respective human actions associated to the associated computational tasks in order to make the everyday life of the involved individuals easier.

This Special Issue aims to bring together interdisciplinary approaches that focus on the application of innovative, as well as existing social and semantics methodologies. Since typical computational data are typically dominated by semantic heterogeneity and are quite dynamic in nature, computer science researchers are obliged and encouraged to develop new suitable algorithms, tools, and applications to efficiently tackle them, whereas existing ones need to be adapted to the individual special characteristics using traditional methodologies derived from the fields of Artificial Intelligence or Machine Learning. Thus, the current Special Issue is fully open to all who want to contribute by submitting a relevant research manuscript.

In addition to the Open Call, selected papers presented at SMAP 2020 are invited to be submitted as extended versions to this Special Issue of the journal Information. The workshop paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a workshop paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Each submission to this journal issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Information and collected together on this Special Issue website.

Dr. Evaggelos Spyrou
Dr. Phivos Mylonas
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 1400 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.

Published Papers (4 papers)

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Research

Open AccessArticle
Tool to Retrieve Less-Filtered Information from the Internet
Information 2021, 12(2), 65; https://doi.org/10.3390/info12020065 - 04 Feb 2021
Abstract
While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered [...] Read more.
While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume. Full article
(This article belongs to the Special Issue Social and Semantic Trends: Tools and Applications)
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Open AccessArticle
CrowdHeritage: Crowdsourcing for Improving the Quality of Cultural Heritage Metadata
Information 2021, 12(2), 64; https://doi.org/10.3390/info12020064 - 04 Feb 2021
Abstract
The lack of granular and rich descriptive metadata highly affects the discoverability and usability of cultural heritage collections aggregated and served through digital platforms, such as Europeana, thus compromising the user experience. In this context, metadata enrichment services through automated analysis and feature [...] Read more.
The lack of granular and rich descriptive metadata highly affects the discoverability and usability of cultural heritage collections aggregated and served through digital platforms, such as Europeana, thus compromising the user experience. In this context, metadata enrichment services through automated analysis and feature extraction along with crowdsourcing annotation services can offer a great opportunity for improving the metadata quality of digital cultural content in a scalable way, while at the same time engaging different user communities and raising awareness about cultural heritage assets. To address this need, we propose the CrowdHeritage open end-to-end enrichment and crowdsourcing ecosystem, which supports an end-to-end workflow for the improvement of cultural heritage metadata by employing crowdsourcing and by combining machine and human intelligence to serve the particular requirements of the cultural heritage domain. The proposed solution repurposes, extends, and combines in an innovative way general-purpose state-of-the-art AI tools, semantic technologies, and aggregation mechanisms with a novel crowdsourcing platform, so as to support seamless enrichment workflows for improving the quality of CH metadata in a scalable, cost-effective, and amusing way. Full article
(This article belongs to the Special Issue Social and Semantic Trends: Tools and Applications)
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Open AccessArticle
Latent Twitter Image Information for Social Analytics
Information 2021, 12(2), 49; https://doi.org/10.3390/info12020049 - 21 Jan 2021
Abstract
The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting [...] Read more.
The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting latent information from Twitter images, by leveraging the Google Cloud Vision API platform, aiming at enriching social analytics with semantics and hidden textual information. As validated by our experiments, social analytics can be further enriched by considering the combination of user-generated content, latent concepts, and textual data extracted from social images, along with linked data. Moreover, we employed word embedding techniques for investigating the usage of latent semantic information towards the identification of similar Twitter images, thereby showcasing that hidden textual information can improve such information retrieval tasks. Finally, we offer an open enhanced version of the annotated dataset described in this study with the aim of further adoption by the research community. Full article
(This article belongs to the Special Issue Social and Semantic Trends: Tools and Applications)
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Open AccessArticle
AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones
Information 2021, 12(1), 43; https://doi.org/10.3390/info12010043 - 19 Jan 2021
Abstract
To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified [...] Read more.
To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones. Full article
(This article belongs to the Special Issue Social and Semantic Trends: Tools and Applications)
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