The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study.

BACKGROUND: In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. OBJECTIVE: The aim of this study was to examine the propagation patterns and textual transformation of COVID-19-related rumors on a closed social media platform. METHODS: We obtained a data set of suspicious text messages collected on Taiwan's most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a "rumor group." Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. RESULTS: A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19-related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network-certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19-related messages, compared to non-COVID-19-related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. CONCLUSIONS: To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas.

Hepatic Clostridium Perfringens Abscess Formation after Radiofrequency Ablation Therapy for Hepatocellular Carcinoma: Report of a Rare Case.

Liver abscess formation is one of the major complications following radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC). Clostridium perfringens is a rare but fatal (mortality rate: 70-100%) organism that could lead to severe sepsis. We presented a case where a 63-year-old woman with diabetes mellitus, hypertension, chronic hepatitis B-related cirrhosis in Child-Pugh class A and HCC with initial TNM stage II who had undergone 2 sessions of transarterial chemoembolization. RFA was performed for 4 small HCC due to poor effect of previous transarterial chemoembolization. However, all 4 treated tumors developed liver abscesses presenting with septic shock within 1 day. Aspirated abscesses and blood culture both yielded C. perfringens infection. After intensive care, optimal intravenous antibiotic, and abscesses aspiration, the patient recovered successfully. All tumors achieved complete response during the follow-up period without local recurrence. The clinical presentations and risk factors of C. perfringens-related liver abscess after RFA will be discussed in this manuscript.

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach.

Online social media platforms play an important role in political communication where users can freely express and exchange their political opinion. Political entities have leveraged social media platforms as essential channels to disseminate information, interact with voters, and even influence public opinion. For this purpose, some organizations may create one or more accounts to join online political discussions. Using these accounts, they could promote candidates and attack competitors. To avoid such misleading speeches and improve the transparency of the online society, spotting such malicious accounts and understanding their behaviors are crucial issues. In this paper, we aim to use network-based analysis to sense influential human-operated malicious accounts who attempt to manipulate public opinion on political discussion forums. To this end, we collected the election-related articles and malicious accounts from the prominent Taiwan discussion forum spanning from 25 May 2018 to 11 January 2020 (the election day). We modeled the discussion network as a multilayer network and used various centrality measures to sense influential malicious accounts not only in a single-layer but also across different layers of the network. Moreover, community analysis was performed to discover prominent communities and their characteristics for each layer of the network. The results demonstrate that our proposed method can successfully identify several influential malicious accounts and prominent communities with apparent behavior differences from others.

Privacy-Preserving Broker-ABE Scheme for Multiple Cloud-Assisted Cyber Physical Systems.

Cloud-assisted cyber-physical systems (CCPSs) integrate the physical space with cloud computing. To do so, sensors on the field collect real-life data and forward it to clouds for further data analysis and decision-making. Since multiple services may be accessed at the same time, sensor data should be forwarded to different cloud service providers (CSPs). In this scenario, attribute-based encryption (ABE) is an appropriate technique for securing data communication between sensors and clouds. Each cloud has its own attributes and a broker can determine which cloud is authorized to access data by the requirements set at the time of encryption. In this paper, we propose a privacy-preserving broker-ABE scheme for multiple CCPSs (MCCPS). The ABE separates the policy embedding job from the ABE task. To ease the computational burden of the sensors, this scheme leaves the policy embedding task to the broker, which is generally more powerful than the sensors. Moreover, the proposed scheme provides a way for CSPs to protect data privacy from outside coercion.

Employment Outcomes After Vocational Training for People With Chronic Psychiatric Disorders: A Multicenter Study.

People with a chronic psychiatric disorder (CPD) have low employment rates worldwide. This multicenter retrospective cohort study examined the employment outcomes of an innovative in-house vocational training program for people with CPD in Taiwan and explored which program parameters significantly predicted outcomes. Data were retrieved from the records of 323 participants who completed the training program. Satisfactory employment rates were found at 1 (52.9%), 3 (60.4%), and 6 (65.6%) mo posttraining, and 38.7% of participants demonstrated high employment sustainability (employed throughout the first 6 mo posttraining). Three program parameters significantly predicted employment outcomes: (1) occupational therapists' pursuit of additional training or short-term apprenticeship pertinent to clients' job interests, (2) posttraining vocational counseling provided by community-based occupational therapists, and (3) the duration of 6 mo posttraining to allow clients to prepare for competitive employment. Occupational therapist-led in-house vocational training is a promising alternative to traditional vocational training for people with CPD.

A Psychometric Validation of the Employers' Stigmatizing Attitudes Toward Cancer Survivors Scale.

Purpose The aim of this study was to evaluate the measurement structure of the Employers' Stigmatizing Attitudes Toward Cancer Survivors Scale (ESATPD-Cancer Scale). Methods A quantitative descriptive design using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). One hundred and seventy-seven Taiwanese human resources (HR) professionals participated in this study. Results EFA revealed a one-factor measurement structure accounting for 50% of the total variance. We conducted a CFA to confirm the one-factor structure; after two pairs of error terms were correlated, the re-specified model was found to fit the data adequately: χ2/df = 1.69, GFI = 0.97, CFI = 0.98, and RMSEA = 0.06. Internal consistency reliability coefficient (Cronbach's alpha) for the ESATPD-Cancer Scale was computed to be 0.80. In addition, stigmatizing attitudes were found to be negatively associated with HR professionals' willingness to hire cancer survivors. Conclusions The results of this study support a one-factor measurement structure for the ESATPD-Cancer Scale in a sample of HR professionals in Taiwan. Findings also support a relationship between ESATPD-Cancer Scale scores and HR professionals' willingness to hire cancer survivors, demonstrating support for the construct validity of the scale.