Special Issue "News Research in Social Networks and Social Media"

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 8949

Special Issue Editors

Dr. Carlos A. Iglesias
E-Mail Website
Guest Editor
Universidad Politécnica de Madrid, Madrid, Spain
Interests: multiagent systems; social computing; linked data; natural language processing; affect technology; machine learning
Dr. J. Fernando Sánchez-Rada
E-Mail Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: agents; social simulation; machine learning; social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the news industry has undergone a big transformation as a consequence of its online diffusion. According to a recent survey, social media has become an important source of news for adults around the world. Nevertheless, this survey also revealed that phenomena such as fake news have had a serious impact on the credibility of online news. Additionally, scientific and technical progress has enabled the increasing adoption of data-driven news generation. Lastly, online news provides a rich dataset for understanding the characteristics of news media channels, such as political leaning and tone, as well as news consumption and sharing dynamics.

This Special Issue aims to provide an overview of the application of intelligent techniques in the news domain. The key areas of this Special Issue include, but are not limited to:

  • Fake news detection (both explainable and multi-modal);
  • News analytics and classification;
  • News similarity and clustering;
  • News provenance;
  • Automated news generation;
  • News event and topic evolution;
  • News framing research;
  • News personalization;
  • News emotion prediction and detection;
  • News sharing, diffusion, and consumption.

Dr. Carlos A. Iglesias
Dr. J. Fernando Sánchez-Rada
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 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 network analysis
  • News detection
  • Social computing
  • Natural language processing
  • Social context
  • Sentiment analysis
  • Social media.

Published Papers (3 papers)

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Research

Article
Automated Classification of Fake News Spreaders to Break the Misinformation Chain
Information 2021, 12(6), 248; https://doi.org/10.3390/info12060248 - 15 Jun 2021
Cited by 3 | Viewed by 1451
Abstract
In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety [...] Read more.
In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%. Full article
(This article belongs to the Special Issue News Research in Social Networks and Social Media)
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Article
Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes
Information 2021, 12(5), 204; https://doi.org/10.3390/info12050204 - 11 May 2021
Cited by 32 | Viewed by 5226
Abstract
A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and [...] Read more.
A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines. Full article
(This article belongs to the Special Issue News Research in Social Networks and Social Media)
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Article
A Big Data Reference Architecture for Emergency Management
Information 2020, 11(12), 569; https://doi.org/10.3390/info11120569 - 04 Dec 2020
Cited by 2 | Viewed by 1339
Abstract
Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely [...] Read more.
Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely improve the decision-making process. On the other hand, digital humanitarianism has shown to be very beneficial for providing support during emergencies. Despite this, the full potential of combining automatic big data processing and digital humanitarianism approaches has not been fully realized, though there is an initial body of research. This paper aims to provide a reference architecture for emergency management that instantiates the NIST Big Data Reference Architecture to provide a common language and enable the comparison of solutions for solving similar problems. Full article
(This article belongs to the Special Issue News Research in Social Networks and Social Media)
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