Special Issue "Decentralization and New Technologies for Social Media"

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

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 9646

Special Issue Editor

Dr. Barbara Guidi
E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Interests: distributed systems; peer-to-peer networks; distributed online social network; social network analysis; blockchain and cryptocurrencies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, Online Social Media (OSM) represent the main communication channel in the real life of people.  Social Media are virtual worlds where people can communicate and share their personal information. However, the more importance they have, the more issues related to their usage arise. Privacy issues, like the famouse Cambridge Analytica Scandal, raise several questions concerning the storage of private data into these centralized social platforms. During the last few years, several proposals to overcome the privacy issues have been proposed, as well as the introduction of new paradigms. Decentralization, blockchain and other new important technologies have been considered in order to overcome the privacy issues of corrent OSM. However, OSM issues are more general and concern also fake news, (mis)information, and censorship. In particular, Decentralized Social Networks (DSONs) and Blockchain-based Social Media (BOSMs) represent today a valid alternative to the current OSMs.

The goal of this Special Issue is to collect research contributions, applications, analyses, methodologies, or strategies that strengthen or face the management of Social Media issues, and the application of decentralized solutions and technologies in order to overcome the current limitations of social media. We hope that this Special Issue will contribute to raising awareness about new proposals and the impact of new technologies on the next generation of social media. Potential topics include, but are not limited to, the following:

  • Social media analysis
  • Decentralized solutions for social media
  • Blockchain social media
  • Blockchain technology applied to social media
  • AI for social media
  • Social media mining
  • Privacy in social media
  • Trust and reputation in social media
  • Mobile networks for social applications
  • Fake news and misinformation

Dr. Barbara Guidi
Guest Editor

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 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.

Keywords

  • Social media
  • Social networks
  • Decentralization
  • Decentralized social media
  • Blockchain
  • Social media mining
  • AI for social media
  • Privacy and trust
  • Fake news

Published Papers (5 papers)

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Research

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Article
Extraction and Analysis of Social Networks Data to Detect Traffic Accidents
Information 2022, 13(1), 26; https://doi.org/10.3390/info13010026 - 10 Jan 2022
Cited by 2 | Viewed by 688
Abstract
Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with [...] Read more.
Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods. Full article
(This article belongs to the Special Issue Decentralization and New Technologies for Social Media)
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Article
CNMF: A Community-Based Fake News Mitigation Framework
Information 2021, 12(9), 376; https://doi.org/10.3390/info12090376 - 16 Sep 2021
Cited by 3 | Viewed by 722
Abstract
Fake news propagation in online social networks (OSN) is one of the critical societal threats nowadays directing attention to fake news mitigation and intervention techniques. One of the typical mitigation techniques focus on initiating news mitigation campaigns targeting a specific set of users [...] Read more.
Fake news propagation in online social networks (OSN) is one of the critical societal threats nowadays directing attention to fake news mitigation and intervention techniques. One of the typical mitigation techniques focus on initiating news mitigation campaigns targeting a specific set of users when the infected set of users is known or targeting the entire network when the infected set of users is unknown. The contemporary mitigation techniques assume the campaign users’ acceptance to share a mitigation news (MN); however, in reality, user behavior is different. This paper focuses on devising a generic mitigation framework, where the social crowd can be employed to combat the influence of fake news in OSNs when the infected set of users is undefined. The framework is composed of three major phases: facts discovery, facts searching and, community recommendation. Mitigation news circulation is accomplished by recruiting a set of social crowd users (news propagators) who are likely to accept posting the mitigation news article. We propose a set of features that identify prospect OSN audiences and news propagators. Moreover, we inspect the variant properties of the news circulation process, such as incentivizing news propagators, determining the required number of news propagators, and the adaptivity of the MN circulation process. The paper pinpoints the significance of facts searching and news propagator’s behavior features introduced in the experimental results. Full article
(This article belongs to the Special Issue Decentralization and New Technologies for Social Media)
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Article
A Data-Driven Framework for Coding the Intent and Extent of Political Tweeting, Disinformation, and Extremism
Information 2021, 12(4), 148; https://doi.org/10.3390/info12040148 - 31 Mar 2021
Cited by 3 | Viewed by 1163
Abstract
Disinformation campaigns on online social networks (OSNs) in recent years have underscored democracy’s vulnerability to such operations and the importance of identifying such operations and dissecting their methods, intents, and source. This paper is another milestone in a line of research on political [...] Read more.
Disinformation campaigns on online social networks (OSNs) in recent years have underscored democracy’s vulnerability to such operations and the importance of identifying such operations and dissecting their methods, intents, and source. This paper is another milestone in a line of research on political disinformation, propaganda, and extremism on OSNs. A total of 40,000 original Tweets (not re-Tweets or Replies) related to the U.S. 2020 presidential election are collected. The intent, focus, and political affiliation of these political Tweets are determined through multiple discussions and revisions. There are three political affiliations: rightist, leftist, and neutral. A total of 171 different classes of intent or focus are defined for Tweets. A total of 25% of Tweets were left out while defining these classes of intent. The purpose is to assure that the defined classes would be able to cover the intent and focus of unseen Tweets (Tweets that were not used to determine and define these classes) and no new classes would be required. This paper provides these classes, their definition and size, and example Tweets from them. If any information is included in a Tweet, its factuality is verified through valid news sources and articles. If any opinion is included in a Tweet, it is determined that whether or not it is extreme, through multiple discussions and revisions. This paper provides analytics with regard to the political affiliation and intent of Tweets. The results show that disinformation and extreme opinions are more common among rightists Tweets than leftist Tweets. Additionally, Coronavirus pandemic is the topic of almost half of the Tweets, where 25.43% of Tweets express their unhappiness with how Republicans have handled this pandemic. Full article
(This article belongs to the Special Issue Decentralization and New Technologies for Social Media)
Article
Pluralism of News and Social Plurality in the Colombian Local Media
Information 2021, 12(3), 131; https://doi.org/10.3390/info12030131 - 18 Mar 2021
Cited by 1 | Viewed by 1124
Abstract
Information on the management of local administrations and the actions of the political leaders who govern them is essential for citizens to exercise their political rights. It is therefore necessary for these administrations to provide quality information that the media can use as [...] Read more.
Information on the management of local administrations and the actions of the political leaders who govern them is essential for citizens to exercise their political rights. It is therefore necessary for these administrations to provide quality information that the media can use as sources for their news stories. At the same time, these media outlets have to compare and report while taking into account the plurality of their audiences. However, in local settings, collusion exists between political power and media owners that restricts the plurality of news, favoring the dominant political interests and hiding the demands, interests and protagonism of other social actors. We study this problem in the Caribbean Region of Colombia. We analyze the information that the town halls of the main cities in the region provide to the media and how the largest print newspapers and main regional television news broadcasters report on local politics. We compare these news stories to establish whether there is a plurality of news reports. In addition, we analyze the key elements of the news items disseminated by private media outlets to establish whether they report a limited vision of reality: the topics covered, the protagonists referred to in headlines and news stories, and the sources against which the news and images are compared. The results reveal shortcomings that result in similar information between public information and private media content, thus limiting the plurality of news reports and the social protagonism of other social agents. Ultimately, this hinders quality journalism that satisfies the interests of citizens. Full article
(This article belongs to the Special Issue Decentralization and New Technologies for Social Media)

Review

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Review
Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges
Information 2021, 12(1), 38; https://doi.org/10.3390/info12010038 - 18 Jan 2021
Cited by 20 | Viewed by 4936
Abstract
The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake [...] Read more.
The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities. Full article
(This article belongs to the Special Issue Decentralization and New Technologies for Social Media)
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