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Computers
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Computational Modeling of Social Processes and Social Networks
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Computational Modeling of Social Processes and Social Networks

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11146

Special Issue Editors

Dr. Ivan Kozitsin

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Guest Editor
Institute of Control Sciences, Russian Academy of Sciences, Moscow 117997, Russia
Interests: opinion formation models; temporal networks; information dissemination
Dr. Anastasia Peshkovskaya

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Guest Editor
Neuroscience Center, Tomsk State University, Tomsk Oblast 634050, Russia
Interests: collective action; eye tracking; decision making; cooperation; social networking; social media use behavior
Dr. Alexander Petrov

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Guest Editor
Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow 125047, Russia
Interests: mathematical modeling; social movements; information dissemination
Prof. Dr. Gholamreza Jafari

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Guest Editor
1. Department of Physics, Institute for Cognitive Science and Brian, Shahid Beheshti University, Tehran19839-63113, Iran
2. Institute of Information Technology and Data Science, Irkutsk National Research Technical University, Irkutsk Oblast 664074, Russia
Interests: complex networks; social media; statistical physics cognitive science; collective actions

Special Issue Information

Dear Colleagues,

The field of computational social science is currently going through a crucial period of development. The growing availability of data on social dynamics provide more opportunities to apply methods from mathematical modeling, statistical physics, social psychology, behavioral economics, and network theory and to thus elaborate upon comprehensive analytical descriptions of social phenomena. Nonetheless, all of these approaches still find it difficult to capture the complexity of social systems. There is no doubt that there is a need for further theoretical and empirical research aimed at exploring how people’s opinions and behavior change over time, how social networks (both real-world and online) self-organize and evolve, and why echo chambers persist in the online environment. Furthermore, this knowledge has to be instrumentalized so as to combat the dissemination of misinformation and dangerous content, mitigate polarization between and within nations, and provide and sustain cooperation in the face of current and future global challenges.

This Special Issue intends to publish original research whereby different computational methods are applied to investigate a range of social phenomena, such as collective action and prosocial behavior, opinion formation, information dissemination, and social network evolution. Both theoretical and empirical studies are encouraged. In our opinion, special attention should be devoted to linking the theoretical and empirical aspects of modeling the role of social media platforms in social dynamics as well as to studying the impact of ranking algorithms on the organization of information environment. Research on opinion mining and sentiment analysis are also welcome.

Dr. Ivan Kozitsin
Dr. Anastasia Peshkovskaya
Dr. Alexander Petrov
Prof. Dr. Gholamreza Jafari
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. Computers 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 1800 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

  • opinion formation models
  • temporal networks
  • ranking algorithms
  • social movements
  • big data
  • artificial societies
  • social contagion
  • social networks
  • collective action
  • cooperation
  • computational social science

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Published Papers (5 papers)

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Research

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24 pages, 485 KiB  
Article
Modeling Seasonality of Emotional Tension in Social Media
by Alexey Nosov, Yulia Kuznetsova, Maksim Stankevich, Ivan Smirnov and Oleg Grigoriev
Computers 2024, 13(1), 3; https://doi.org/10.3390/computers13010003 - 22 Dec 2023
Viewed by 1841
Abstract
Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due [...] Read more.
Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due to the laboriousness and complexity of obtaining a sufficient amount of data, processing and evaluating them, and presenting the results. At the same time, understanding the annual dynamics of collective sentiment provides us with important insights into collective behavior, especially in various crises or disasters. In our study, we propose a scheme for identifying and evaluating signs of the seasonal rise and fall of emotional tension based on social media texts. The analysis is based on Russian-language comments in VKontakte social network communities devoted to city news and the events of a small town in the Nizhny Novgorod region, Russia. Workflow steps include a statistical method for categorizing data, exploratory analysis to identify common patterns, data aggregation for modeling seasonal changes, the identification of typical data properties through clustering, and the formulation and validation of seasonality criteria. As a result of seasonality modeling, it is shown that the calendar seasonal model corresponds to the data, and the dynamics of emotional tension correlate with the seasons. The proposed methodology is useful for a wide range of social practice issues, such as monitoring public opinion or assessing irregular shifts in mass emotions. Full article
(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)
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31 pages, 4302 KiB  
Article
Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis
by Nirmalya Thakur, Shuqi Cui, Karam Khanna, Victoria Knieling, Yuvraj Nihal Duggal and Mingchen Shao
Computers 2023, 12(11), 221; https://doi.org/10.3390/computers12110221 - 31 Oct 2023
Cited by 2 | Viewed by 2248
Abstract
This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that [...] Read more.
This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these Tweets were positive. The results of gender-specific sentiment analysis indicate that for positive Tweets, negative Tweets, and neutral Tweets, between males and females, males posted a higher percentage of the Tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated Tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that females posted a higher percentage of highly opinionated Tweets as compared to males. However, males posted a higher percentage of least opinionated and neutral opinionated Tweets as compared to females. Third, toxicity detection was performed on the Tweets to detect different categories of toxic content—toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of Tweets posted by each gender for each of these categories of toxic content revealed several novel insights related to the degree, type, variations, and trends of toxic content posted by males and females related to online learning. Fourth, the average activity of males and females per month in this context was calculated. The findings indicate that the average activity of females was higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India, a higher percentage of the Tweets about online learning during COVID-19 were posted by males as compared to females. Full article
(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)
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19 pages, 8953 KiB  
Article
The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain
by Vladislav N. Gezha and Ivan V. Kozitsin
Computers 2023, 12(6), 116; https://doi.org/10.3390/computers12060116 - 2 Jun 2023
Cited by 4 | Viewed by 1787
Abstract
The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line [...] Read more.
The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line of research by studying longitudinal data that describe the opinion dynamics of a large sample (~30,000) of online social network users, all citizens of one city. Using these data, we systematically investigate the effects of users’ demographic (age, gender) and structural (degree centrality, the number of common friends) properties on opinion formation processes. We revealed that females are less easily influenced than males. Next, we found that individuals that are characterized by similar ages have more chances to reach a consensus. Additionally, we report that individuals who have many common peers find an agreement more often. We also demonstrated that the impacts of these effects are virtually the same, and despite being statistically significant, are far less strong than that of opinion-related features: knowing the current opinion of an individual and, what is even more important, the distance in opinions between this individual and the person that attempts to influence the individual is much more valuable. Next, after conducting a series of simulations with an agent-based model, we revealed that accounting for non-opinion characteristics may lead to not very sound but statistically significant changes in the macroscopic predictions of the populations of opinion camps, primarily among the agents with radical opinions (≈3% of all votes). In turn, predictions for the populations of neutral individuals are virtually the same. In addition, we demonstrated that the accumulative effect of non-opinion features on opinion dynamics is seriously moderated by whether the underlying social network correlates with the agents’ characteristics. After applying the procedure of random shuffling (in which the agents and their characteristics were randomly scattered over the network), the macroscopic predictions have changed by ≈9% of all votes. What is interesting is that the population of neutral agents was again not affected by this intervention. Full article
(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)
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16 pages, 2492 KiB  
Article
Disparity of Density in the Age of Mobility: Analysis by Opinion Formation Model
by Shiro Horiuchi
Computers 2023, 12(5), 94; https://doi.org/10.3390/computers12050094 - 1 May 2023
Cited by 1 | Viewed by 1813
Abstract
High mobility has promoted the concentration of people’s aggregation in urban areas. As people pursue areas with higher density, gentrification and sprawl become more serious. Disadvantaged people are then pushed out of urban centers. Conversely, as mobility increases, the disadvantaged may also migrate [...] Read more.
High mobility has promoted the concentration of people’s aggregation in urban areas. As people pursue areas with higher density, gentrification and sprawl become more serious. Disadvantaged people are then pushed out of urban centers. Conversely, as mobility increases, the disadvantaged may also migrate in pursuit of their desired density. As a result, disparities relative to density and housing may shrink. Hence, migration is a complex system. Understanding the effects of migration on disparities intuitively is difficult. This study explored the effects of mobility on disparity using an agent-based model of opinion formation. We find that as mobility increases, disparities between agents in density and diversity widen, but as mobility increases further, the disparities shrink, and then widen again. Our results present possibilities for a just city in the age of mobility. Full article
(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)
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Review

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15 pages, 813 KiB  
Review
Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art
by Ryan Schuerkamp, Luke Liang, Ketra L. Rice and Philippe J. Giabbanelli
Computers 2023, 12(7), 132; https://doi.org/10.3390/computers12070132 - 29 Jun 2023
Cited by 4 | Viewed by 2091
Abstract
Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, [...] Read more.
Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions. Full article
(This article belongs to the Special Issue Computational Modeling of Social Processes and Social Networks)
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