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Data-Driven Social Intelligence and Its Applications
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Data-Driven Social Intelligence and Its Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 21435

Special Issue Editor

Dr. Meng Han

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Interests: fintech; data mining; machine learning; big data; security and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social data have been broadly generated from a variety of sources, ranging from earlier platforms such as Facebook, Twitter, LinkedIn, YouTube, and Instagram to the latest emerging short video platforms such as TikTok. The concept of data-driven social intelligence can be defined as intelligence that is developed based on the broad of data and taking the data-driven approach to generate recommendations and information to support users, companies, and societies. These data sources can be characterized by their different formats and contents, their scale, and the online or streamed generation of information. The problem of managing and extracting valuable information from these social data is currently one of the most popular topics in computer science research, creating new technologies and application challenges.

To address these challenges, it will be necessary to integrate methods and techniques from different areas includes but not limited to sensor data, social network data, cyber-physical data, and other relevant sources. The goal of this Special Issue is to gather the recent developments and applications of data-driven approaches/models for social intelligence. A special emphasis is placed on the presentation of novel algorithms, architectures, and emerging applications, as well as survey papers that review the novel technologies and new trends in this area. The relevant security and privacy concerns toward the data-driven social intelligence are also welcome.

Dr. Meng Han
Guest Editor

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Published Papers (6 papers)

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Research

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26 pages, 2524 KiB  
Article
K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks
by Bing Su, Jiaxuan Huang, Kelei Miao, Zhangquan Wang, Xudong Zhang and Yourong Chen
Sensors 2023, 23(3), 1554; https://doi.org/10.3390/s23031554 - 31 Jan 2023
Cited by 10 | Viewed by 3009
Abstract
Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems [...] Read more.
Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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19 pages, 6619 KiB  
Article
Effective Model Update for Adaptive Classification of Text Streams in a Distributed Learning Environment
by Min-Seon Kim, Bo-Young Lim, Kisung Lee and Hyuk-Yoon Kwon
Sensors 2022, 22(23), 9298; https://doi.org/10.3390/s22239298 - 29 Nov 2022
Cited by 2 | Viewed by 2519
Abstract
In this study, we propose dynamic model update methods for the adaptive classification model of text streams in a distributed learning environment. In particular, we present two model update strategies: (1) the entire model update and (2) the partial model update. The former [...] Read more.
In this study, we propose dynamic model update methods for the adaptive classification model of text streams in a distributed learning environment. In particular, we present two model update strategies: (1) the entire model update and (2) the partial model update. The former aims to maximize the model accuracy by periodically rebuilding the model based on the accumulated datasets including recent datasets. Its learning time incrementally increases as the datasets increase, but we alleviate the learning overhead by the distributed learning of the model. The latter fine-tunes the model only with a limited number of recent datasets, noting that the data streams are dependent on a recent event. Therefore, it accelerates the learning speed while maintaining a certain level of accuracy. To verify the proposed update strategies, we extensively apply them to not only fully trainable language models based on CNN, RNN, and Bi-LSTM, but also a pre-trained embedding model based on BERT. Through extensive experiments using two real tweet streaming datasets, we show that the entire model update improves the classification accuracy of the pre-trained offline model; the partial model update also improves it, which shows comparable accuracy with the entire model update, while significantly increasing the learning speed. We also validate the scalability of the proposed distributed learning architecture by showing that the model learning and inference time decrease as the number of worker nodes increases. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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19 pages, 3670 KiB  
Article
A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
by Yang Zhang, Yourong Chen, Kelei Miao, Tiaojuan Ren, Changchun Yang and Meng Han
Sensors 2022, 22(23), 9125; https://doi.org/10.3390/s22239125 - 24 Nov 2022
Cited by 8 | Viewed by 1686
Abstract
In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) [...] Read more.
In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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16 pages, 5376 KiB  
Article
Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns
by SeungHun Lee, Wafa Shafqat and Hyun-chul Kim
Sensors 2022, 22(19), 7677; https://doi.org/10.3390/s22197677 - 10 Oct 2022
Cited by 4 | Viewed by 2774
Abstract
Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that [...] Read more.
Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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20 pages, 4310 KiB  
Article
Multi-Level Transformer-Based Social Relation Recognition
by Yuchen Wang, Linbo Qing, Zhengyong Wang, Yongqiang Cheng and Yonghong Peng
Sensors 2022, 22(15), 5749; https://doi.org/10.3390/s22155749 - 1 Aug 2022
Cited by 4 | Viewed by 2514
Abstract
Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such [...] Read more.
Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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Review

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41 pages, 7383 KiB  
Review
A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data
by Motahara Sabah Mredula, Noyon Dey, Md. Sazzadur Rahman, Imtiaz Mahmud and You-Ze Cho
Sensors 2022, 22(12), 4531; https://doi.org/10.3390/s22124531 - 15 Jun 2022
Cited by 13 | Viewed by 7750
Abstract
Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried [...] Read more.
Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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