Special Issue "Social Media Analysis"

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Electrical Engineering".

Deadline for manuscript submissions: closed (20 July 2021).

Special Issue Editors

Prof. Dr. Camelia Delcea
E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: cybernetics; social networks; sentiment analysis; opinion mining; grey systems theory; operations research; strategic management; computational intelligence; business analysis; agent-based modelling
Special Issues and Collections in MDPI journals
Prof. Dr. Liviu-Adrian Cotfas
E-Mail Website
Guest Editor
Bucharest University of Economic Studies, Bucharest, Romania
Interests: social media analysis; natural language processing; semantic web; machine learning; deep learning; sentiment analysis; opinion mining; agent-based modelling; recommender systems
Special Issues and Collections in MDPI journals
Dr. Gabriella Ferruzzi
E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Interests: social networks; social influence; education; entrepreneurial studies; organizational studies; innovation policies; opinion mining; strategical behavior

Special Issue Information

Dear Colleagues,

Given the ever-increasing number of social media messages posted daily by millions of users, social media has become in the last few years one of the most relevant sources for understanding the public’s opinion on various subjects. Uncovering opinions using social media analysis from such a large number of users in near real-time can prove invaluable in many domains, ranging from marketing, to political elections, to emergency situations, and even to discovering security risks and predicting the evolution of the crypto currencies. While advanced techniques relying on machine learning and deep learning are currently revolutionizing the accuracy of the natural language processing algorithms used to analyze the content social media messages, big data approaches uncover hidden insights from the huge volume of messages published every day. Moreover, semantic web technologies offer invaluable possibilities, by organizing data using classes and relations.

The Special Issue invites original research papers that address the design and development of advanced social media analysis techniques and platforms, relying on recent developments such as machine learning, deep learning, and semantic web. Additionally, the authors are encouraged to submit papers addressing the state-of-the-art or case studies featuring social media and social networks analysis in the new economic context, in relation to education, sustainability, the gender gap and diversity, marketing strategies, strategic management, decision-making, etc., by employing different approaches and quantitative and/or qualitative social network analysis methods.

Prof. Dr. Camelia Delcea
Prof. Dr. Liviu-Adrian Cotfas
Dr. Gabriella Ferruzzi
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. Designs is an international peer-reviewed open access quarterly 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.

Keywords

  • Fake news detection and influencers
  • Contagion in social networks
  • Social media monitoring and dashboards
  • Qualitative/quantitative methods for social analysis
  • Natural language processing
  • Sentiment and emotion analysis
  • Machine learning and deep learning
  • Big data in social analysis
  • Semantic web
  • Case studies

Published Papers (1 paper)

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Review

Review
Using NLP for Fact Checking: A Survey
Designs 2021, 5(3), 42; https://doi.org/10.3390/designs5030042 - 14 Jul 2021
Viewed by 265
Abstract
In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide [...] Read more.
In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use. Full article
(This article belongs to the Special Issue Social Media Analysis)
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