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Automatic Disinformation Detection on Social Media Platforms
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Automatic Disinformation Detection on Social Media Platforms

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 18603

Special Issue Editors

Dr. Gianni Costa

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networks (ICAR) of the National Research Council of Italy (CNR), 87036 Rende, Italy
Interests: data mining; machine learning; recommender systems; social network analysis; text mining; semi-structured data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Ortale

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking (ICAR), National Research Council (CNR), 87036 Rende, Italy
Interests: machine intelligence; machine learning; knowledge discovery; (intelligent) information systems; knowledge-based systems; recommender systems; text analysis; community question answering; (social) network/media analysis; decision support; behavioral analysis; semistructured data analysis; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, people increasingly tend not to access and consume news through traditional media but using social media platforms, thanks to the low cost, ease of access, and speed of information dissemination that social media can ensure. This change has modified the way people inform themselves and form their opinions but, at the same time, has exposed them to the large-scale proliferation of disinformation in online newspapers and social networks. Disinformation, in all its forms, has serious negative repercussions on fields as diverse as economics, politics, and health, and its propagation finds even more fertile ground in times of crisis such the one we are experiencing due to the COVID-19 pandemic. Despite recent efforts undertaken by the scientific community to devise appropriate countermeasures, identifying misinformation in social media and mitigating its spread are still open problems.

Therefore, this Special Issue aims to collect innovative research papers, both theoretical and experimental, from different areas such as machine learning, social network analysis, data mining, and natural language processing, on using social media data for automatic online disinformation detection and mitigation. Extensions of previously published works are also welcome, provided that they contain at least 30% new material. 

Topics of interest include, but are not limited to, the following:

  • Detection of online disinformation, hoaxes and fake news;
  • Social media data analysis;
  • Roles, trust and reputation in social media;
  • New open datasets and knowledge base to help predicting disinformation in social media;
  • Identification and analysis of propaganda in online disinformation campaigns;
  • Automatic claim verification;
  • Bots and troll detection on social media;
  • Fact-checking in social media;
  • Disinformation propagation modeling;
  • Rumors detection on social media.

Dr. Gianni Costa
Dr. Riccardo Ortale
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. Data 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 1600 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

  • Natural language processing
  • Data mining
  • Machine learning
  • Social network analysis
  • Deep learning

Published Papers (4 papers)

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Research

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18 pages, 296 KiB  
Communication
The Missing Case of Disinformation from the Cybersecurity Risk Continuum: A Comparative Assessment of Disinformation with Other Cyber Threats
by Kevin Matthe Caramancion, Yueqi Li, Elisabeth Dubois and Ellie Seoe Jung
Data 2022, 7(4), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/data7040049 - 12 Apr 2022
Cited by 12 | Viewed by 7561
Abstract
This study examines the phenomenon of disinformation as a threat in the realm of cybersecurity. We have analyzed multiple authoritative cybersecurity standards, manuals, handbooks, and literary works. We present the unanimous meaning and construct of the term cyber threat. Our results reveal that [...] Read more.
This study examines the phenomenon of disinformation as a threat in the realm of cybersecurity. We have analyzed multiple authoritative cybersecurity standards, manuals, handbooks, and literary works. We present the unanimous meaning and construct of the term cyber threat. Our results reveal that although their definitions are mostly consistent, most of them lack the inclusion of disinformation in their list/glossary of cyber threats. We then proceeded to dissect the phenomenon of disinformation through the lens of cyber threat epistemology; it displays the presence of the necessary elements required (i.e., threat agent, attack vector, target, impact, defense) for its appropriate classification. To conjunct this, we have also included an in-depth comparative analysis of disinformation and its similar nature and characteristics with the prevailing and existing cyber threats. We, therefore, argue for its recommendation as an official and actual cyber threat. The significance of this paper, beyond the taxonomical correction it recommends, rests in the hope that it influences future policies and regulations in combatting disinformation and its propaganda. Full article
(This article belongs to the Special Issue Automatic Disinformation Detection on Social Media Platforms)
13 pages, 1644 KiB  
Article
Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus
by Noha Alnazzawi, Najlaa Alsaedi, Fahad Alharbi and Najla Alaswad
Data 2022, 7(4), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/data7040044 - 07 Apr 2022
Cited by 9 | Viewed by 4476
Abstract
Nowadays, an increasing portion of our lives is spent interacting online through social media platforms, thanks to the widespread adoption of the latest technology and the proliferation of smartphones. Obtaining news from social media platforms is fast, easy, and less expensive compared with [...] Read more.
Nowadays, an increasing portion of our lives is spent interacting online through social media platforms, thanks to the widespread adoption of the latest technology and the proliferation of smartphones. Obtaining news from social media platforms is fast, easy, and less expensive compared with other traditional media platforms, e.g., television and newspapers. Therefore, social media is now being exploited to disseminate fake news and false information. This research aims to build the FakeAds corpus, which consists of tweets for product advertisements. The aim of the FakeAds corpus is to study the impact of fake news and false information in advertising and marketing materials for specific products and which types of products (i.e., cosmetics, health, fashion, or electronics) are targeted most on Twitter to draw the attention of consumers. The corpus is unique and novel, in terms of the very specific topic (i.e., the role of Twitter in disseminating fake news related to production promotion and advertisement) and also in terms of its fine-grained annotations. The annotation guidelines were designed with guidance by a domain expert, and the annotation is performed by two domain experts, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.815. Full article
(This article belongs to the Special Issue Automatic Disinformation Detection on Social Media Platforms)
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10 pages, 1021 KiB  
Data Descriptor
Deceptive Content Labeling Survey Data from Two U.S. Midwestern Universities
by Ryan Suttle, Scott Hogan, Rachel Aumaugher, Matthew Spradling, Zak Merrigan and Jeremy Straub
Data 2022, 7(3), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/data7030026 - 22 Feb 2022
Viewed by 2146
Abstract
Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating [...] Read more.
Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating to how news consumption decisions are made and the impact of labels on decision making. This article describes these three surveys and the data that were collected by them. Full article
(This article belongs to the Special Issue Automatic Disinformation Detection on Social Media Platforms)
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15 pages, 2096 KiB  
Data Descriptor
Multi-Ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-Class Extremism Text Classification
by Mayur Gaikwad, Swati Ahirrao, Shraddha Phansalkar and Ketan Kotecha
Data 2021, 6(11), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/data6110117 - 15 Nov 2021
Cited by 4 | Viewed by 2922
Abstract
Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media [...] Read more.
Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media leads to an increased number of susceptible audiences. Hence, detection of extremist text on social media platforms is a significant area of research. The unavailability of extremism text datasets is a challenge in online extremism research. The lack of emphasis on classifying extremism text into propaganda, radicalization, and recruitment classes is a challenge. The lack of data validation methods also challenges the accuracy of extremism detection. This research addresses these challenges and presents a seed dataset with a multi-ideology and multi-class extremism text dataset. This research presents the construction of a multi-ideology ISIS/Jihadist White supremacist (MIWS) dataset with recent tweets collected from Twitter. The presented dataset can be employed effectively and importantly to classify extremist text into popular types like propaganda, radicalization, and recruitment. Additionally, the seed dataset is statistically validated with a coherence score of Latent Dirichlet Allocation (LDA) and word mover’s distance using a pretrained Google News vector. The dataset shows effectiveness in its construction with good coherence scores within a topic and appropriate distance measures between topics. This dataset is the first publicly accessible multi-ideology, multi-class extremism text dataset to reinforce research on extremism text detection on social media platforms. Full article
(This article belongs to the Special Issue Automatic Disinformation Detection on Social Media Platforms)
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