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Electronics
Special Issues
Advances in Social Bots
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Advances in Social Bots

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 1274

Special Issue Editors

Dr. Yangyang Li

E-Mail Website
Guest Editor
Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, University of Science and Technology of China, Hefei 230026, China
Interests: social bots; cyber security; social networks
Special Issues, Collections and Topics in MDPI journals
Dr. Yangzhao Yang

E-Mail Website
Guest Editor
Chinese Institute of Command and Control, Beijing 100089, China
Interests: artificial general intelligence; social network mining; knowledge graph
Dr. Hu Huang

E-Mail Website
Guest Editor
Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Interests: data science; cyber science; social networks

Special Issue Information

Dear Colleagues,

With the profound integration of technology and media, the ongoing development of social bots showcases an enhanced ability to mimic and engage in more covert machine behaviors. This new generation of social bots provides unprecedented convenience for public communication. However, the rapid development also presents substantial real-world risks in diverse domains. In the context of maintaining social stability, these automated systems have the potential to act as catalysts for social unrest, contributing to the proliferation of destabilizing elements. In the field of news dissemination, social bots may be employed to propagate misinformation or fake news, or they might filter and promote news stories based on specific algorithms and datasets, potentially leading to selection bias in the information presented. Moreover, they can be utilized to fabricate or amplify focus on particular issues, thereby manipulating public opinion. Regarding the aspect of financial security, the actions of social bots have the potential to contribute to a notable increase in instances of financial fraud, thereby posing a substantial threat to economic systems. In the realm of commercial marketing, there exists a potential for social bots to disseminate deceptive or exaggerated promotional content, undermining consumer interests and the equity of the marketplace. Crucially, the utilization of social bots gives rise to concerns regarding potential violations of personal privacy, thereby eliciting widespread apprehension. Behind this challenge lies a multitude of research topics spanning various disciplines, including bot detection, emotion analysis, stance detection, human–machine conversation, intelligent interaction, and content generation. Addressing these research topics is essential to guide the advancement of social bot technology. This will ensure that its development promotes communication convenience while minimizing any potential negative effects on societal, communication, financial, marketing, and personal domains.

This Special Issue solicits papers on new research achievements and challenges in social bots.

Topics of interest in this Special Issue include but are not limited to the following:

  • Social Bot Detection in Social Networks;
  • Emotion Analysis in Social Bots;
  • Stance Detection in Social Bots;
  • Human–Machine Conversation;
  • Artificial Intelligence for Social Bots;
  • Intelligent Interaction in Social Bots;
  • Multi-Modal Content Generation;
  • Computational Modelling of Information Diffusion;
  • Misinformation Detection in Social Media;
  • Social Bot Influence Analysis;
  • Dynamics of Online Opinion Propaganda.

Dr. Yangyang Li
Dr. Yangzhao Yang
Dr. Hu Huang
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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 agent
  • multi-agent
  • conversational AI
  • emotion detection
  • sentiment analysis
  • social interaction modelling
  • social bot detection
  • multi-modal content generation
  • user experience evaluation

Published Papers (3 papers)

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Research

16 pages, 4859 KiB  
Article
FLsM: Fuzzy Localization of Image Scenes Based on Large Models
by Weiyi Chen, Lingjuan Miao, Jinchao Gui, Yuhao Wang and Yiran Li
Electronics 2024, 13(11), 2106; https://doi.org/10.3390/electronics13112106 - 29 May 2024
Viewed by 230
Abstract
This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in [...] Read more.
This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments. Full article
(This article belongs to the Special Issue Advances in Social Bots)
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25 pages, 8204 KiB  
Article
IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
by Yi Qu, Sheng Wang, Tianshi Pan and Hui Feng
Electronics 2024, 13(11), 2095; https://doi.org/10.3390/electronics13112095 - 28 May 2024
Viewed by 187
Abstract
IPCBs (Intelligent Pseudolite Constellations based on high-altitude balloons) are a novel type of air-based pseudolite application with many advantages. Compared with ground-based pseudolites and traditional air-based pseudolites, IPCBs have a wider coverage and a lower energy requirement. Compared with LEO satellite constellations, IPCBs [...] Read more.
IPCBs (Intelligent Pseudolite Constellations based on high-altitude balloons) are a novel type of air-based pseudolite application with many advantages. Compared with ground-based pseudolites and traditional air-based pseudolites, IPCBs have a wider coverage and a lower energy requirement. Compared with LEO satellite constellations, IPCBs have a stronger signal, a lower cost, and a shorter deployment period. These merits give promising potential to IPCBs. In IPCB applications, one of the key factors is geometry configuration, which is deeply influenced by the balloon’s unique features. The basic idea of this paper is to pursue a strategy to improve IPCB geometry performance by using diverse winds at different altitudes and balloons’ capability of altering flight altitude intelligently. Starting with a brief introduction to IPCBs, this paper defines an indicator to assess IPCB geometry performance, an approach to adjust IPCB geometry configuration and an IPCB geometry configuration planning algorithm. Next, a series of simulations are implemented with an IPCB composed of six pseudolites in winds with/without a quasi-zero wind layer. Some IPCB geometry configurations are analyzed, and their geometry performances are compared. Simulation results show the effectiveness of the proposed algorithm and the influence of the quasi-zero wind layer on IPCB performance. Full article
(This article belongs to the Special Issue Advances in Social Bots)
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16 pages, 1486 KiB  
Article
Research on Aspect-Level Sentiment Analysis Based on Adversarial Training and Dependency Parsing
by Erfeng Xu, Junwu Zhu, Luchen Zhang, Yi Wang and Wei Lin
Electronics 2024, 13(10), 1993; https://doi.org/10.3390/electronics13101993 - 20 May 2024
Viewed by 321
Abstract
Aspect-level sentiment analysis is used to predict the sentiment polarity of a specific aspect in a sentence. However, most current research cannot fully utilize semantic information, and the models lack robustness. Therefore, this article proposes a model for aspect-level sentiment analysis based on [...] Read more.
Aspect-level sentiment analysis is used to predict the sentiment polarity of a specific aspect in a sentence. However, most current research cannot fully utilize semantic information, and the models lack robustness. Therefore, this article proposes a model for aspect-level sentiment analysis based on a combination of adversarial training and dependency syntax analysis. First, BERT is used to transform word vectors and construct adjacency matrices with dependency syntactic relationships to better extract semantic dependency relationships and features between sentence components. A multi-head attention mechanism is used to fuse the features of the two parts, simultaneously perform adversarial training on the BERT embedding layer to enhance model robustness, and, finally, to predict emotional polarity. The model was tested on the SemEval 2014 Task 4 dataset. The experimental results showed that, compared with the baseline model, the model achieved significant performance improvement after incorporating adversarial training and dependency syntax relationships. Full article
(This article belongs to the Special Issue Advances in Social Bots)
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