Special Issue "Advanced Analysis Technologies for Social Media"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 September 2021).

Special Issue Editors

Dr. Barbara Guidi
E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, 56127 Pisa PI, Italy
Interests: decentralized social media; P2P networks; social media analysis; blockchain social media
Special Issues and Collections in MDPI journals
Dr. Carlos A. Iglesias
E-Mail Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: multiagent systems; social computing; linked data; natural language processing; affect technology; machine learning
Special Issues and Collections in MDPI journals
Dr. Giulio Rossetti
E-Mail Website
Guest Editor
Knowledge Discovery and Data mining Laboratory, Information Science and Technologies Institute, Italian National Research Council, 56124 Pisa PI, Italy
Interests: complex networks; dynamics on and off networks; polarization and mixing patterns; agent-based modeling
Dr. Kevin Koidl
E-Mail Website
Guest Editor
Trinity College Dublin, Dublin, Ireland
Interests: social media; AI; trust

Special Issue Information

Dear Colleagues,

Interest in social media has only increased with time. Social media today represent the main channel to communicate and to share personal information. Social media analysis usually combines content-based and network-based analysis. While content-based approaches analyze media using media analysis techniques, network-based approaches analyze static and dynamic network properties with the aim of detecting influencers for marketing purposes. Network-based analysis represents a fundamental process in order to understand the dynamics of these platforms. New techniques and technologies have been proposed in order to enrich the social media analytics field. In particular, decentralized approaches have been proposed in order to face privacy issues, and AI has been applied in order to improve analysis over large sets of data. The main goal of this Special Issue is to collect research contributions, applications, analyses, methodologies, or strategies that strengthen or face the knowledge of social media thanks to advanced analyses or new technologies, such as P2P networks or blockchain. We hope that this Special Issue will contribute to raising awareness about new proposals and the impact of new technologies on social media. Potential topics include, but are not limited to, the following:

  • Social media analysis;
  • Decentralized approaches for social media;
  • Blockchain social media: analysis and applications;
  • AI for social media;
  • Social media mining;
  • Privacy in social media;
  • Fake news and misinformation.

Dr. Barbara Guidi
Dr. Carlos A. Iglesias
Dr. Giulio Rossetti
Dr. Kevin Koidl
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2000 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

  • social media analysis
  • AI for social media
  • decentralized solution for social media
  • fake news
  • trust and reputation in social media
  • blockchain for social media

Published Papers (5 papers)

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Research

Article
An Overview of Blockchain Online Social Media from the Technical Point of View
by
Appl. Sci. 2021, 11(21), 9880; https://doi.org/10.3390/app11219880 (registering DOI) - 22 Oct 2021
Viewed by 150
Abstract
Social media is becoming one of the dominant ways to communicate. Before social media, people were extremely limited in their means to interact with others, and they were limited largely to the people that they knew in person. However, this impact on people [...] Read more.
Social media is becoming one of the dominant ways to communicate. Before social media, people were extremely limited in their means to interact with others, and they were limited largely to the people that they knew in person. However, this impact on people in real life has damaged privacy. Alternative solutions have been proposed in order to overcome current social media issues. In this direction, blockchain is one of the most promising, and several blockchain-based social media have been proposed. In this paper, we analyze blockchain online social media from the technical point of view in order to understand the current trend of social DApps and to describe which characteristics are important in a blockchain-based social media scenario. We analyze real data by exploiting one of the most well-known DApps sites, and we compare current technologies in order to highlight which ones can be better applied to a real social scenario, such as Facebook. Full article
(This article belongs to the Special Issue Advanced Analysis Technologies for Social Media)
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Article
Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors
Appl. Sci. 2021, 11(15), 6726; https://doi.org/10.3390/app11156726 - 22 Jul 2021
Viewed by 366
Abstract
Social media rumor precise governance is conducive to better coping with the difficulties of rumor monitoring within massive information and improving rumor governance effectiveness. This paper proposes a conceptual framework of social media rumor precise governance system based on literature mining. Accordingly, insightful [...] Read more.
Social media rumor precise governance is conducive to better coping with the difficulties of rumor monitoring within massive information and improving rumor governance effectiveness. This paper proposes a conceptual framework of social media rumor precise governance system based on literature mining. Accordingly, insightful directions for achieving social media rumor precise governance are introduced, which includes (1) rational understanding of social media rumors, especially large-scale spreading false rumors and recurring false rumors; (2) clear classification of rumor spreaders/believers/refuters/unbelievers; (3) scientific evaluation of rumor governance effectiveness and capabilities. For the above three directions, advanced analysis technologies applications are then summarized. This paper is beneficial to clarify and promote the promising thought of social media rumor precise governance and create impacts on the technologies’ applications in this area. Full article
(This article belongs to the Special Issue Advanced Analysis Technologies for Social Media)
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Article
Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
Appl. Sci. 2021, 11(12), 5390; https://doi.org/10.3390/app11125390 - 10 Jun 2021
Viewed by 900
Abstract
In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach [...] Read more.
In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users’ ideology on the controversy; (iii) the construction of users’ debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump’s presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time. Full article
(This article belongs to the Special Issue Advanced Analysis Technologies for Social Media)
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Article
Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text
Appl. Sci. 2021, 11(11), 4768; https://doi.org/10.3390/app11114768 - 22 May 2021
Viewed by 584
Abstract
With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific [...] Read more.
With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text. Full article
(This article belongs to the Special Issue Advanced Analysis Technologies for Social Media)
Article
An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks
Appl. Sci. 2021, 11(6), 2530; https://doi.org/10.3390/app11062530 - 12 Mar 2021
Cited by 2 | Viewed by 464
Abstract
Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data [...] Read more.
Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems. Full article
(This article belongs to the Special Issue Advanced Analysis Technologies for Social Media)
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