Social Media Health-Related Information Credibility and Reliability: An Integrated User Perceived Quality Assessment

The dominance of social media (SM) channels in disseminating health-related information during the COVID-19 came with several credibility and reliability issues. Yet, users' explicit assessment of SM health-related information (SMHI) quality has been less discussed. Situated in Ghana, this study empirically explores the extent to which the source credibility and content reliability influence users' perception of SMHI quality during the COVID-19 pandemic. Grounded on an integrated SMHI quality evaluation framework, the study hypothesized that the source credibility and content reliability influence users' perception of SMHI quality. We tested the model using 361 Ghanaian SMHI consumers. The structural equation modeling results indicate that while source credibility influences content reliability, it does not influence users' perception of quality SMHI during the COVID-19. Again, content reliability significantly influences users' perceived SMHI quality. We offer several theoretical and practical implications on users' choice and consumption of health-related information on SM platforms.

Social media sentiment analysis and emotional intelligence including women role during COVID-19 crisis

Individuals cling on scientific advancements to find out the impediments in day-to-day life;the caliber to assist themselves undoubtedly deteriorates what is happening during COVID-19 pandemic. Social media is a virtual communication among people via virtual communities and networks like Facebook, Twitter, YouTube, and WeChat;where people communicate virtually and share information and ideas in day-to-day lives. The COVID-19 pandemic has changed the utilization of social media by world's population;the lethal effects of social media expanded as a sense of closeness and ideological wellness which strengthened emotional health and lessened the risk for distress and worry, but due to the inaccurate and overwhelmed data, there is an increase in depression and anxiety. False information on social media made the pandemic risky, which in turn is prone to risk factors and aggressively changed everyone's lifestyle and burdened mental health. Therefore, this paper mainly focuses on psychological distress among the public and the mental health challenges like stress, depression, anxiety, grief, negativity, death, as well as positive aspects especially in women and children who are at home taking care of families and children without schools and physical activities. Women play a vital role during this pandemic as they contribute 70% of the health sector and hence are at a higher risk of exposing to infection. Therefore, the main objective of this study is to understand, discuss and identify the objectives of sentiment analysis and emotional intelligence based on how the Internet-based applications or user-generated content during COVID-19 pandemic effects the emotional well-being and mental health of public globally and counting especially women. Hence, administrative bodies must consider taking some immediate emergency steps to support women manage their work and caring their parental responsibilities wisely and come forward to give a helping hand toward their concerns during the COVID-19 pandemic including the importance of social media and misleading information on social media. © 2021 Elsevier Inc. All rights reserved.

Time series sentiment analysis (SA) of relief operations using social media (SM) platform for efficient resource management

The ease of access to the internet has sparked a worldwide interest in SM in recent years. The possibilities for the use of SM as a potential source in improving the management of disasters are increasing. Although there is an existing research ongoing with the analysis of the usage of SM during disasters, but none of the works have exploited the use of explainable artificial intelligence (XAI) for the validation of the transparency of the ML models. The contribution of this research is two-fold: Firstly, ML based time series analysis for relief operations using social media information with situational information gathered using Twitter from the users and resource providers and secondly, XAI has been used to increase the transparency and understand-ability of model decisions. For the case study, public dataset from Nepal, Italy earthquakes, COVID-19 dataset along with originally collected Twitter dataset has been considered. The performance of the extreme gradient boosting (xgboost) model is relatively superior than other techniques with 10-fold training mean accuracy of 87.17%. Thus, the experiments conclude the possibility of automation for the time series analysis for optimal relief operation management to serve the victims in the most efficient way and to control legal and administration implications.

Communicating COVID-19 on Social Media: The Effects of the Spiral of Silence

In this paper, the Spiral of Silence theory (SOS) in the study of mass communications is applied to examine the trends and mechanisms of public opinion in Social Media (SM), using the popular topic of the COVID-19 pandemic. The study includes a secondary analysis of the data on pandemic information consumption obtained through four mass surveys conducted in Armenia. In the period from July 1 to August 30, 2020, we also surveyed Armenian Facebook users by means of Google forms during the highest outbreak of the pandemic in Armenia. In particular, the study demonstrates that although the majority of people are well informed about both public conduct requirements and the sanctions for misconduct during the pandemic, they do not follow the rules but hide their real opinion, preferring to openly agree with the official position while silently breaking the rules (that is, they keep their silence). We have found a correlation between the opinion environment of "friends" and other Facebook users, and a willingness to express their own opinion. Due to the predominance of the self-presentation mode as a communication strategy on Facebook, there is a trend among Armenian users not to risk their reputation, and avoid possible critics by keeping silence, if the discussion goes against their opinion. The findings of the study might be helpful both for the further development of communication theories and its application to the conditions of new pandemic reality, and for a better understanding of communicative behavior mechanisms in SM.