Information Spreading on Networks

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

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 17211

Special Issue Editors


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Guest Editor
Dipartimento di Ingegneria Elettrica Elettronica Informatica, Università di Catania, Viale Andrea Doria, 9-95127 Catania, Italy
Interests: network science; natural language processing; data analysis; machine learning; information spread; distributed systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), University of Catania, 95124 Catania, Italy
Interests: network science; natural language processing; data analysis; machine learning; information spread; distributed systems

Special Issue Information

Dear Colleagues,

Since the dawn of modern society, information has played a central role in the building of the social fabric. Today, the dissemination of information has turned into an extremely serious problem due to the large amount of data and the extreme speed with which it spreads. The growing interest in the study of techniques and models to understand, promote, and steer this phenomenon both on computer networks as well as on social networks is flanked by the interest in the classification of information to detect and analyze rumors, misinformation, and fake news.

Although numerous researchers are constantly engaged in this field, many open challenges remain involving experts in various fields, such as artificial intelligence, distributed systems, social science, and natural language analysis.

This Special Issue seeks contributions reporting on recent advancements concerning information spread in the context of humanities and computer science. This includes novel techniques to classify information, as well as the discussion of the impact of information dissemination in several fields.

Topics of interest include but are not limited to:

  • Technologies for information spread;
  • Technologies for detecting misinformation;
  • Model information spread and game theory
  • Information spread on dynamic networks
  • Information spread on social networks;
  • Intelligent systems for information modeling;
  • Rumor spread on networks;
  • Fake news data: collection and classification;
  • Fake news spread and detection;
  • Information spread and trust;
  • ML and deep learning for information spread and classification;
  • Epidemic system model;
  • Information spread and recommendation systems;
  • Real case study and application of information spread.

In addition to application-based contributions, this Special Issue also welcomes proposals with extensive reflections on interdisciplinary collaborations between information experts and social experts with a particular emphasis on real-world applications, such as those involving politics and health. These papers will serve as a guide for researchers working on the intersection between information dissemination and social sciences and will be useful for scholars following participatory design approaches involving experts from various research domains.

Prof. Vincenza Carchiolo
Prof. Alessandro Longheu
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 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

  • Information spread
  • Fake news detection
  • Rumors detection
  • Social networks
  • Distributed system
  • Artificial intelligence

Published Papers (6 papers)

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Research

19 pages, 757 KiB  
Article
A Novel Epidemic Model for the Interference Spread in the Internet of Things
by Emmanuel Tuyishimire, Jean de Dieu Niyigena, Fidèle Mweruli Tubanambazi, Justin Ushize Rutikanga, Paul Gatabazi, Antoine Bagula and Emmanuel Niyigaba
Information 2022, 13(4), 181; https://doi.org/10.3390/info13040181 - 02 Apr 2022
Cited by 1 | Viewed by 2204
Abstract
Due to the multi-technology advancements, internet of things (IoT) applications are in high demand to create smarter environments. Smart objects communicate by exchanging many messages, and this creates interference on receivers. Collection tree algorithms are applied to only reduce the nodes/paths’ interference but [...] Read more.
Due to the multi-technology advancements, internet of things (IoT) applications are in high demand to create smarter environments. Smart objects communicate by exchanging many messages, and this creates interference on receivers. Collection tree algorithms are applied to only reduce the nodes/paths’ interference but cannot fully handle the interference across the underlying IoT. This paper models and analyzes the interference spread in the IoT setting, where the collection tree routing algorithm is adopted. Node interference is treated as a real-life contamination of a disease, where individuals can migrate across compartments such as susceptible, attacked and replaced. The assumed typical collection tree routing model is the least interference beaconing algorithm (LIBA), and the dynamics of the interference spread is studied. The underlying network’s nodes are partitioned into groups of nodes which can affect each other and based on the partition property, the susceptible–attacked–replaced (SAR) model is proposed. To analyze the model, the system stability is studied, and the compartmental based trends are experimented in static, stochastic and predictive systems. The results shows that the dynamics of the system are dependent groups and all have points of convergence for static, stochastic and predictive systems. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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19 pages, 10700 KiB  
Article
Autoethnography on Researcher Profile Cultivation
by Małgorzata Pańkowska
Information 2022, 13(3), 154; https://doi.org/10.3390/info13030154 - 16 Mar 2022
Cited by 1 | Viewed by 3272
Abstract
Information Communication Technology (ICT) and social networks have significant impact on everyday life. One the one hand, Internet users enjoy promoting themselves and feel free to disseminate information about themselves through websites and social networks, but on the other hand, people feel forced [...] Read more.
Information Communication Technology (ICT) and social networks have significant impact on everyday life. One the one hand, Internet users enjoy promoting themselves and feel free to disseminate information about themselves through websites and social networks, but on the other hand, people feel forced to reveal information about them on the Internet. Web technologies enable self-promotion for many reasons, i.e., social relations development, acquiring a new job, or research career support. This paper concerns autoethnography application for social science researcher profile cultivation. Autoethnography belongs to qualitative methods and focuses on deep analysis of experiences and competencies in a narrative way. In this study, autoethnography is self-reflection for personal development strategy. This study methodology includes the literature survey and case study. The Literature Survey (LS) on autoethnographic research is included to answer the question for what purposes autoethnography is applied. In the case study, the author proposes to expand autoethnography and presents that beyond stories, statistical data can be used to reveal researcher’s experiences and personality, and data anonymization is a solution for privacy protection in autoethnographic research. The results indicate that perception of individual profile is significantly influenced by ICT, Internet services, and social networks platforms and portals. Contemporary researchers are evaluated by Web statistical measures. The researcher’s profiling is much more complex and statistical measures and metrics provide a general view of the researcher. Application of statistical measures leads to concluding on general competencies of the researcher and precludes a deep focus on local scientific specificity of the researcher. This paper has added value because of presenting the academic community integration with the Internet social networks, e.g., Facebook, LinkedIn, or SciVal. The paper emphasizes transparency and visibility of researchers’ profiles, as well as the necessity to analyze their activities and publications in academic community context and in comparisons with others. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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11 pages, 341 KiB  
Article
Link Prediction in Time Varying Social Networks
by Vincenza Carchiolo, Christian Cavallo, Marco Grassia, Michele Malgeri and Giuseppe Mangioni
Information 2022, 13(3), 123; https://doi.org/10.3390/info13030123 - 01 Mar 2022
Cited by 11 | Viewed by 2809
Abstract
Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, [...] Read more.
Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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26 pages, 5706 KiB  
Article
Towards a Bibliometric Mapping of Network Public Opinion Studies
by Yujie Qiang, Xuewen Tao, Xiaoqing Gou, Zhihui Lang and Hui Liu
Information 2022, 13(1), 17; https://doi.org/10.3390/info13010017 - 03 Jan 2022
Cited by 11 | Viewed by 3184
Abstract
To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and [...] Read more.
To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and keyword burst analysis were performed using VOSviewer and CiteSpace software. The results show that the NPO is mainly distributed in the disciplinary fields associated with journalism and communication and public management. There are four main hotspots: analysis of public opinion, analysis of communication channels, technical means and challenges faced. The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. With the advent of the era of big data, big data technology has been widely used in various fields and to some extent can be said to be the research frontier in the field. Transforming big data public opinion into early warning, realizing in-depth analysis and accurate prediction of public opinion as well as improving decision-making ability of public opinion are the future research directions of NPO. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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15 pages, 1286 KiB  
Article
An Approach to Ranking the Sources of Information Dissemination in Social Networks
by Lidia Vitkova, Igor Kotenko and Andrey Chechulin
Information 2021, 12(10), 416; https://doi.org/10.3390/info12100416 - 11 Oct 2021
Cited by 1 | Viewed by 1785
Abstract
The problem of countering the spread of destructive content in social networks is currently relevant for most countries of the world. Basically, automatic monitoring systems are used to detect the sources of the spread of malicious information, and automated systems, operators, and counteraction [...] Read more.
The problem of countering the spread of destructive content in social networks is currently relevant for most countries of the world. Basically, automatic monitoring systems are used to detect the sources of the spread of malicious information, and automated systems, operators, and counteraction scenarios are used to counteract it. The paper suggests an approach to ranking the sources of the distribution of messages with destructive content. In the process of ranking objects by priority, the number of messages created by the source and the integral indicator of the involvement of its audience are considered. The approach realizes the identification of the most popular and active sources of dissemination of destructive content. The approach does not require the analysis of graphs of relationships and provides an increase in the efficiency of the operator. The proposed solution is applicable both to brand reputation monitoring systems and for countering cyberbullying and the dissemination of destructive information in social networks. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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13 pages, 3618 KiB  
Article
Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis
by Panagiotis Kasnesis, Lazaros Toumanidis and Charalampos Z. Patrikakis
Information 2021, 12(10), 409; https://doi.org/10.3390/info12100409 - 03 Oct 2021
Cited by 9 | Viewed by 2546
Abstract
The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post’s content with respect to its [...] Read more.
The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post’s content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post’s subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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