Could Social Bots' Sentiment Engagement Shape Humans' Sentiment on COVID-19 Vaccine Discussion on Twitter?

During the COVID-19 pandemic, social media has become an emerging platform for the public to find information, share opinions, and seek coping strategies. Vaccination, one of the most effective public health interventions to control the COVID-19 pandemic, has become the focus of public online discussions. Several studies have demonstrated that social bots actively involved in topic discussions on social media and expressed their sentiments and emotions, which affected human users. However, it is unclear whether social bots' sentiments affect human users' sentiments of COVID-19 vaccines. This study seeks to scrutinize whether the sentiments of social bots affect human users' sentiments of COVID-19 vaccines. The work identified social bots and built an innovative computational framework, i.e., the BERT-CNN sentiment analysis framework, to classify tweet sentiments at the three most discussed stages of COVID-19 vaccines on Twitter from December 2020 to August 2021, thus exploring the impacts of social bots on online vaccine sentiments of humans. Then, the Granger causality test was used to analyze whether there was a time-series causality between the sentiments of social bots and humans. The findings revealed that social bots can influence human sentiments about COVID-19 vaccines. Their ability to transmit the sentiments on social media, whether in the spread of positive or negative tweets, will have a corresponding impact on human sentiments.

Social Bots' Involvement in the COVID-19 Vaccine Discussions on Twitter.

During the COVID-19 pandemic, social media served as an important channel for the public to obtain health information and disseminate opinions when offline communication was severely hindered. Yet the emergence of social bots influencing social media conversations about public health threats will require researchers and practitioners to develop new communication strategies considering their influence. So far, little is known as to what extent social bots have been involved in COVID-19 vaccine-related discussions and debates on social media. This work selected a period of nearly 9 months after the approval of the first COVID-19 vaccines to detect social bots and performed high-frequency word analysis for both social bot-generated and human-generated tweets, thus working out the extent to which social bots participated in the discussion on the COVID-19 vaccine on Twitter and their participation features. Then, a textual analysis was performed on the content of tweets. The findings revealed that 8.87% of the users were social bots, with 11% of tweets in the corpus. Besides, social bots remained active over three periods. High-frequency words in the discussions of social bots and human users on vaccine topics were similar within the three peaks of discourse.

Behavioral Problems of Pediatric Recovered Patients Infected with COVID-19 in Wuhan, China (preprint)

Background: Coronavirus disease 2019 (COVID-19) is profoundly affecting lives around the globe, and up to now, a large of patients have clinically recovered from their initial illness. As the vulnerable population to adverse events, the effect of the COVID-19 outbreak on pediatric recovered patients are of great concern, but relevant researches are limited. We aimed to investigate the prevalence of behavioral problems among pediatric patients with COVID-19 infection after discharge.  Methods: A total of 122 children who were suspected or confirmed COVID-19 cases and hospitalized for treatment were enrolled in the study between April 2020 and May 2020 in Wuhan, China. We collected related information about hospitalization and discharge for the children and emotional symptoms for their parents through electronic medical records and questionnaire. The behavioral problems for children were examined applying the parent-reported the Strengths and Difficulties Questionnaire (SDQ).Results: The participant children were discharged from hospital for about two months. Among them, 76 (62%) were boys, and the mean age were 6.71 years old. The highest prevalence of behavioral problems among pediatric children with COVID-19 was 15% (prosocial behavior), followed by 13% (total difficulties), 11% (emotional symptoms), 10% (hyperactivity), 9% (conduct problems), and 1% (peer problems). With regarding to their parents, 26% of them reported having anxiety symptoms and 23% having depression symptoms. Compared with children without anxious or depressive parents, the scores of SDQ were higher in children whose parents have emotional problems.Conclusions: The long-term follow up studies on the psychological and behavioral problems of children infected with COVID-19 and their parents are warranted.