IEEE Transactions on Computational Social Systems 

About: IEEE Transactions on Computational Social Systems is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Computer science & Social media. It has an ISSN identifier of 2329-924X. Over the lifetime, 1119 publications have been published receiving 14503 citations. The journal is also known as: Computational social systems & Transactions on computational social systems.

Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

Abstract: During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

Blockchain-Powered Parallel Healthcare Systems Based on the ACP Approach

Abstract: To improve the accuracy of diagnosis and the effectiveness of treatment, a framework of parallel healthcare systems (PHSs) based on the artificial systems + computational experiments + parallel execution (ACP) approach is proposed in this paper PHS uses artificial healthcare systems to model and represent patients’ conditions, diagnosis, and treatment process, then applies computational experiments to analyze and evaluate various therapeutic regimens, and implements parallel execution for decision-making support and real-time optimization in both actual and artificial healthcare processes In addition, we combine the emerging blockchain technology with PHS, via constructing a consortium blockchain linking patients, hospitals, health bureaus, and healthcare communities for comprehensive healthcare data sharing, medical records review, and care auditability Finally, a prototype named parallel gout diagnosis and treatment system is built and deployed to verify and demonstrate the effectiveness and efficiency of the blockchain-powered PHS framework

Community Detection via Maximization of Modularity and Its Variants

Abstract: In this paper, we first discuss the definition of modularity ( ${\mbi{Q}}$ ) used as a metric for community quality and then we review the modularity maximization approaches which were used for community detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization; in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so-called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density ( ${{\mbi{Q}}_{{\bf ds}}}$ ), which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first one, referred to as Fine-tuned ${\mbi{Q}}$ , is based on modularity ( ${\mbi{Q}}$ ), while the second is based on Modularity Density ( ${{\mbi{Q}}_{{\bf ds}}}$ ) and denoted as Fine-tuned ${{\mbi{Q}}_{{\bf ds}}}$ . Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy ${\mbi{Q}}$ ), Fine-tuned ${\mbi{Q}}$ , and Fine-tuned ${{\mbi{Q}}_{{\bf ds}}}$ on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned ${{\mbi{Q}}_{{\bf ds}}}$ is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned ${{\mbi{Q}}_{{\bf ds}}}$ can be applied to the communities detected by other algorithms to significantly improve their results.

COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

Abstract: Social media (and the world at large) have been awash with news of the COVID-19 pandemic With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 The tweets have been labeled into positive, negative, and neutral sentiment classes We analyzed the collected tweets for sentiment classification using different sets of features and classifiers Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic

Decentralized On-Demand Energy Supply for Blockchain in Internet of Things: A Microgrids Approach

Abstract: Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.