Exploring public sentiment during COVID-19 : a cross country analysis
- Authors: Yu, Shuo , He, Sihan , Cai, Zhen , Lee, Ivan , Naseriparsa, Mehdi , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 10, no. 3 (2023), p. 1083-1094
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- Description: COVID-19 has spread all over the world, accounting for countless death and enormous economic loss. Since the World Health Organization (WHO) declared COVID-19 as a pandemic, governments from different countries have made various policies to prevent the pandemic from becoming worse. However, civilian reactions to the pandemic vary when they face similar situations. This behavioral variation creates a challenge when it comes to policy-making. Such differences are generally implicit, hidden in ones' social lives. As a result, it is challenging to analyze such differences when the governments make policies. In this work, we investigate social media posts on Twitter and Weibo in order to effectively explore the difference in reactions across various countries, with the aim to understand national differences. To this end, we employ natural language processing (NLP) methods and Linguistic Inquiry and Word Count (LIWC) tools to process six languages in different countries, including the USA, Germany, France, Italy, the U.K., and China. We provide a comprehensive analysis of public reaction differences from the emotional perspective. Our findings verify that the reactions vary noticeably among various countries for some policies. Therefore, sentiment analysis can significantly influence policy-making. Our work sheds light on the mechanism of detecting the reaction differences in various countries, which can be utilized to conduct effective communication and make appropriate policy decisions. © 2014 IEEE.
The effect of facial perception and academic performance on social centrality
- Authors: Zhang, Dongyu , Peng, Ciyuan , Chang, Xiaojun , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 10, no. 3 (2023), p. 970-981
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- Description: Facial perception is of significant influence on the positions of people in social networks. Particularly, students' facial traits can affect their social centrality in educational settings (e.g., students looking intelligent can attract more friends). However, in educational environments, the social biases associated with appearances have alarming consequences, and little research has been done to investigate the effect of facial perception on social networks. Therefore, it is necessary to comprehensively analyze the influence of perceived facial traits on students' status in social interaction. In this article, we explore the effect of facial perception on the social centrality of students in social networks. Because students' social centrality is based on both their study ability and facial traits, this study does a comparative analysis of how facial perception and academic performance influence the social centrality of students. Subsequently, the experimental results demonstrate that facial perception, as well as academic performance, closely correlates with the social centrality of students. Finally, this study contributes to a comprehensive and deep understanding of social networks by analyzing facial trait-based social biases. © 2014 IEEE.
Trust-aware detection of malicious users in dating social networks
- Authors: Shen, Xingfa , Lv, Wentao , Qiu, Jianhui , Kaur, Achhardeep , Xiao, Fengjun , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 10, no. 5 (2023), p. 2587-2598
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- Description: Online dating is an increasingly thriving business which boosts billion-dollar revenues and attracts users in the tens of millions. Despite its popularity, internet dating is not exempt from the concerns about privacy and trust posed by the revelation of potentially sensitive data as well as the exposure to self-reported (and hence potentially distorted) information. The increasing popularity of online dating networks leads to an increase in security concerns and challenges, as well as harmful actions and attacks, such as creating fake accounts, phishing on these networks. To maintain the safety of legitimate online dating users, it is critical to recognize and isolate criminal people as soon as possible. However, researchers concerning malicious user detection in dating social networks are merely a few. To address some key challenges in this space, we propose a trust-aware detection framework to detect malicious users based on different kinds of data from a real dating site. In particular, we develop a user trust model to distinguish between malicious and legitimate users. Furthermore, we propose a novel data-balancing method to improve the recall rate of malicious user detection. Extensive experiments have been conducted over real-world datasets. The results show that the proposed approach yields a precision of up to 59.16% and a recall rate of up to 73%, which is significantly higher than other baseline algorithms. © 2014 IEEE.
CHIEF : clustering With higher-order motifs in big networks
- Authors: Xia, Feng , Yu, Shuo , Liu, Chengfei , Li, Jianxin , Lee, Ivan
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Network Science and Engineering Vol. 9, no. 3 (2022), p. 990-1005
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- Description: Clustering network vertices is an enabler of various applications such as social computing and Internet of Things. However, challenges arise for clustering when networks increase in scale. This paper proposes CHIEF (Clustering with HIgher-ordEr motiFs), a solution which consists of two motif clustering techniques: standard acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms firstly find the maximal $k$-edge-connected subgraphs within the target networks to lower the network scale by optimizing the network structure with maximal $k$-edge-connected subgraphs, and then use heterogeneous four-node motifs clustering in higher-order dense networks. For CHIEF-ST, we illustrate that all target motifs will be kept after this procedure when the minimum node degree of the target motif is equal or greater than $k$. For CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the Laplacian matrix are relatively stable after this step. CHIEF offers an improved efficiency of motif clustering for big networks, and it verifies higher-order motif significance. Experiments on real and synthetic networks demonstrate that the proposed solutions outperform baseline approaches in large network analysis, and higher-order motifs outperform traditional triangle motifs in clustering. © 2022 IEEE Computer Society. All rights reserved.
Familiarity-based collaborative team recognition in academic social networks
- Authors: Yu, Shuo , Xia, Feng , Zhang, Chen , Wei, Haoran , Keogh, Kathleen , Chen, Honglong
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 9, no. 5 (2022), p. 1432-1445
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- Description: Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose faMiliarity-based cOllaborative Team recOgnition (MOTO) algorithm to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then, MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale dataset. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by the MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution. © 2014 IEEE.
Urban region profiling with spatio-temporal graph neural networks
- Authors: Hou, Mingliang , Xia, Feng , Gao, Haoran , Chen, Xin , Chen, Honglong
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 9, no. 6 (2022), p. 1736-1747
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- Description: Region profiles are summaries of characteristics of urban regions. Region profiling is a process to discover the correlations between urban regions. The learned urban profiles can be used to represent and identify regions in supporting downstream tasks, e.g., region traffic status estimation. While some efforts have been made to model urban regions, representation learning with awareness of graph-structured data can improve the existing methods. To do this, we first construct an attribute spatio-temporal graph, in which a node represents a region, an edge represents mobility across regions, and a node attribute represents a region's point of interest (PoI) distribution. The problem of region profiling is reformulated as a representation learning problem based on attribute spatio-temporal graphs. To solve this problem, we developed URGENT, a spatio-temporal graph learning framework. URGENT is made up of two modules. The graph convolutional neural network is used in the first module to learn spatial dependencies. The second module is an encoding-decoding temporal learning structure with self-attention mechanism. Furthermore, we use the learned representations of regions to estimate region traffic status. Experimental results demonstrate that URGENT outperforms major baselines in estimation accuracy under various settings and produces more meaningful results. © 2014 IEEE.
Data-driven decision-making in COVID-19 response : a survey
- Authors: Yu, Shuo , Qing, Qing , Zhang, Chen , Shehzad, Ahsan , Oatley, Giles , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 4 (2021), p. 989-1002
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- Description: COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE.
The dominance of big teams in china’s scientific output
- Authors: Liu, Linlin , Yu, Jianfei , Huang, Junming , Xia, Feng , Jia, Tao
- Date: 2021
- Type: Text , Journal article
- Relation: Quantitative Science Studies Vol. 2, no. 1 (2021), p. 350-362
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- Description: Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams. © 2020 Linlin Liu, Jianfei Yu, Junming Huang, Feng Xia, and Tao Jia. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.