What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments. We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler, including texts and images posted by users. In order to effectively discover what is being discussed about COVID-19 without human labeling, we propose a unified multi-modal framework, including an unsupervised short-text topic model to discover and track bursty topics, and a selfsupervised model to learn image features so that we can retrieve related images about COVID-19. Experimental results have shown the effectiveness and superiority of the proposed models, and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.     

The changes in the effects of social media use of Cypriots due to COVID-19 pandemic

Social media is the leading medium which is used for communication during the COVID-19 pandemic. The research conducted aims to fill the gap of literature related to social media use during the COVID-19 pandemic. This research aims at uncovering the influences of social media use in several dimensions during lockdown(s). The study aims to answer the research question of: Are the influences of social media use different from normal times? The online questionnaire has been completed by six hundred and sixty-eight users within the period of lockdown. The author prepared the questionnaire, which is composed of 22 positive statements in order to evaluate the effects of social media use during the COVID-19 pandemic. A 5 point Likert scale was used, where reliability and validity were calculated by the Cronbach's alpha value, which was 0.751. Findings highlight that users have more information about COVID-19, and they follow recent information via social media, which shows the shift towards digital medium. Findings also indicate that users are aware of fake news, and they follow official sources. Social media is powerful to affect decision-makers, and respondents' social media use did not create any panic or anxiety amongst them. This research indicates that respondents' social media use during COVID-19 is different from normal times as a common purpose triggers this, survival. Before the COVID-19 pandemic, most of social media shares were like a dream or a strong desire that may cause anxiety in others. During the pandemic, people are in lockdown and share similar feelings and follow similar behavioural patterns. As there is a common purpose and struggle via users, psychological well-being is not affected negatively.     

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus infection. Twitter has become one of the popular social media platforms and allows anyone to post tweets. This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based sentiment analysis (SCOBGRU-SA) on COVID-19 tweets. The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic. The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this. Moreover, the BGRU model is utilized to recognise and classify sentiments present in the tweets. Furthermore, the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter, which helps attain improved classification performance. The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset, and the results signify its promising performance compared to other DL models.     

To share or not to share – The underlying motives of sharing fake news amidst the COVID-19 pandemic in Malaysia

This study investigates the underlying motives for online fake news sharing during the COVID-19 pandemic, an unprecedented time that witnessed a spike in the spread of false content. Motives were identified based on a fake news sharing model developed using the SocioCultural-Psychological-Technology (SCulPT) model, Uses and Gratification (U&G) theory and Self-Determination Theory (SDT), and further extended using fake news predictors/gratifications from past studies. A self-administered survey resulted in 869 online Malaysian respondents aged between 18 and 59 years old (Mean = 22.6, Standard deviation = 6.13). Structured equation modelling revealed the fake news sharing model to collectively account for 49.2 % of the variance, with Altruism (β = 0.333; p < 0.001), Ignorance (β = 0.165; p < 0.001) and Entertainment (β = 0.139; p < 0.001) significantly predicting the behaviour. Conversely, Availability/Effort, Pass Time and Fear of Missing Out were found to be insignificant. Our findings indicate that fake news sharing behavior is determined by different motives, hence these need to be understood in order to develop better solutions to mitigate this problem.     

Social TV viewing during the COVID-19 lockdown: The mediating role of social presence

The COVID-19 lockdown has transformed the way of life for many people. One key change is media intake, as many individuals reported an increase in media consumption during the COVID-19 lockdown. Specifically, social media and television usage increased. In this regard, the present study examines social TV viewing, the simultaneous use of watching TV while communicating with others about the TV content on various communication technologies, during the COVID-19 lockdown. An online survey was conducted to collect data from college students in the United States during the COVID-19 lockdown. Primary results indicate that different motives predict different uses of communication platforms for social TV engagement, such as public platforms, text-based private platforms, and video-based private platforms. Specifically, the social motive significantly predicts social TV engagement on most of the platforms. Further, the study finds that social presence of virtual co-viewers mediates the relationship between social TV engagement and social TV enjoyment. Overall, the study's findings provide a meaningful understanding of social TV viewing when physical social gatherings are restricted.     

Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.     

Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom of Saudi Arabia (KSA) is additionally combating with the COVID-19 danger as the cases announced till June 15, 2020 reached the count of 132,048 with 1,011 deaths. According to the report released by the KSA on June 14, 2020, more than 4,000 cases of COVID-19 pandemic had been registered in the country. Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease, our examination expects to assess the severity of COVID-19 pandemic in cities of KSA. In addition, computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease. Furthermore, a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia. Further, this paper has taken the Fuzzy Analytic Hierarchy Process (AHP) technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA. The discoveries and the proposed structure would be a practical, expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA. Hence these urban zones clearly emerge as the COVID-19 hotspots. The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic. The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.     

Government policies and attitudes to social media use among users in Turkey: The role of awareness of policies,political involvement,online trust,and party identification

Few research studies have examined the impact of government policies toward social media on individuals’ attitudes to social media use, particularly when these policies aim to denounce and control social media platforms, as was the case in Turkey in 2013–2016. A conceptual model, based on the Theory of Planned Behavior (Ajzen, 2005) [1], was proposed to investigate the mediating role of awareness of government policies, degree of political involvement, online trust, and the moderating role of party identification in predicting the attitudes to social media use. Data were collected through a survey of 653 social media users in Istanbul, Turkey (mean age = 31.76, SD = 10.96; 40 % women, 83 % Turkish ethnicity) in September 2015. Using PLS-SEM modelling, the awareness of government policies, the degree of political involvement, and the online trust were found to partially mediate the relationship between the frequency of social media use and the attitudes to social media use for the users of Twitter, YouTube, and Instagram, while the moderating role of party identification was not significant in this model. The results provide additional support for the role of social context and past behaviors in predicting the attitudes and future intentions in the use of digital communication technologies.     

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved.     

News or social media? Socio-economic divide of mobile service consumption

Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3.7 billion time-stamped and geo-referenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage—especially on news, e-mail, social media consumption and audio/video streaming. We relate such diversity with income, educational attainment and inequality, and reveal how low-income or low-education areas are more likely to engage in video streaming or social media and less in news consumption, information searching, e-mail or audio streaming. The digital usage gap is so large that we can accurately infer the socio-economic status of a small area or even its Gini coefficient only from aggregated data traffic. Our results make the case for an inexpensive, privacy-preserving, real-time and scalable way to understand the digital usage divide and, in turn, poverty, unemployment or economic growth in our societies through mobile phone data.     

基于传播心理学的新型冠状病毒肺炎防疫信息科普图形设计与应用

基于传播心理学的新型冠状病毒肺炎防治信息科普图形设计的简介性与有效性、共通性与沟通性、特色性与共鸣性的创作定位。展开新型冠状病毒肺炎防疫信息科普的图形设计,探索传播心理学下,特殊防治信息科普图形的色彩研究、元素研究和表现手法研究。实现互联网新媒体时代下防疫信息图形的传播媒介、传播受众、传播场所的具体应用剖析。立足艺术设计专业加强防疫符号辨识,形成积极防疫心理暗示,助推新型冠状病毒肺炎疫情防治及舆情宣传的传播效应。     

开源公益社区自组织协同创新模式研究

2020年新型冠状病毒肺炎疫情期间,诸多社会公益组织发挥了重要的作用,其中很多志愿者致力于线上各类信息的收集共享,以社会创新的形式为大众提供公益服务。开源社区(开放源代码社区)文化是信息时代的独特产物,它的核心价值要素包括共同承担社会责任、奉献和共享的社区精神,以及协同创新,因此在应对新型冠状病毒肺炎疫情这样的公共卫生危机时,开源社区能够发挥其独特的潜能和影响力。本文以“武汉2020”开源社区为例,基于杨氏基金会社会创新理论框架,研究分析了该社区在线上自组织的演化过程、弱中心化的分布式协作模式和工具等。该社区是由近四千名志愿者自发组织形成的线上公益开源社区,以跨地域协同创新的形式在短期内完成了一款公益服务产品的设计、开发和运行。这次实践拓展了社会创新的边界,展示了广大社区成员基于共同的价值标准和目标凝聚起来后,如何通过自组织搭建一个分布式决策和协作的社区框架,进行协同创新和快速产出面向大众的公益服务。     

Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.     

Bayesian Model Fusion for Forecasting Civil Unrest

With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and spurring them into action. We are interested in using Big Data approaches to generate forecasts of civil unrest from open source indicators. The heterogenous nature of data coupled with the rich and diverse origins of civil unrest call for a multi-model approach to such forecasting. We present a modular approach wherein a collection of models use overlapping sources of data to independently forecast protests. Fusion of alerts into one single alert stream becomes a key system informatics problem and we present a statistical framework to accomplish such fusion. Given an alert from one of the numerous models, the decision space for fusion has two possibilities: (i) release the alert or (ii) suppress the alert. Using a Bayesian decision theoretical framework, we present a fusion approach for releasing or suppressing alerts. The resulting system enables real-time decisions and more importantly tuning of precision and recall. Supplementary materials for this article are available online.     

Buying Time for an Effective Epidemic Response: The Impact of a Public Holiday for Outbreak Control on COVID-19 Epidemic Spread

Rapid responses in the early stage of a new epidemic are crucial in outbreak control. Public holidays for outbreak control could provide a critical time window for a rapid rollout of social distancing and other control measures at a large population scale. The objective of our study was to explore the impact of the timing and duration of outbreak-control holidays on the coronavirus disease 2019 (COVID-19) epidemic spread during the early stage in China. We developed a compartment model to simulate the dynamic transmission of COVID-19 in China starting from January 2020. We projected and compared epidemic trajectories with and without an outbreak-control holiday that started during the Chinese Lunar New Year. We considered multiple scenarios of the outbreak-control holiday with different durations and starting times, and under different assumptions about viral transmission rates. We estimated the delays in days to reach certain thresholds of infections under different scenarios. Our results show that the outbreak-control holiday in China likely stalled the spread of COVID-19 for several days. The base case outbreak-control holiday (21 d for Hubei Province and 10 d for all other provinces) delayed the time to reach 100 000 confirmed infections by 7.54 d. A longer outbreak-control holiday would have had stronger effects. A nationwide outbreak-control holiday of 21 d would have delayed the time to 100 000 confirmed infections by nearly 10 d. Furthermore, we find that outbreak-control holidays that start earlier in the course of a new epidemic are more effective in stalling epidemic spread than later holidays and that additional control measures during the holidays can boost the holiday effect. In conclusion, an outbreak-control holiday can likely effectively delay the transmission of epidemics that spread through social contacts. The temporary delay in the epidemic trajectory buys time, which scientists can use to discover transmission routes and identify effective public health interventions and which governments can use to build physical infrastructure, organize medical supplies, and deploy human resources for long-term epidemic mitigation and control efforts.     

The ethical challenges and issues of online journalism practice in Nigeria: What do professionals and academics think?

This study evaluated the ethical challenges and issues of online media in Nigeria. The study used a mixed research method. Data were collected from journalists in Jalingo and lecturers of Mass Communication in one public University. The quantitative data were analysed using SPSS version 25. While the qualitative data collected via interviews were analysed thematically. Both data suggest there is a high rate of unethical practices in online media in Nigeria. Fake news was ranked first, while other issues such as a lack of objectivity, lack of decency, invasion of privacy, hate speeches and sensationalism were found to be common in online media practice. Furthermore, the study found that desire to break news, lack of professional training and regulation, and political interest were key factors encouraging unethical practices in online media. The study also established that unethical practices among online media can be checkmated if online media practitioners are also regulated by licensing and exposed to training. Thus, the study recommended among others that the Nigeria Communication Commission should work with internet service providers and Telecommunication companies to design a model that will ensure that online media are registered before they can operate. Telecommunication companies and service providers should block online media that are known for perpetrating unethical practices.     

Three-dimensional printing in the fight against novel virus COVID-19: Technology helping society during an infectious disease pandemic

Indeed, the scientific milestones set by the ever-emerging three-dimensional printing (3DP) technologies are tremendous. Till now, the innovative 3DP technologies have benefitted the aerospace, automobile, textile, pharmaceutical, and biomedical sectors by developing pre-requisite designed and customized performance standards of the end-user products. As the scientific world, at this moment, is expediting efforts to fight against the highly damaging novel coronavirus (COVID-19) pandemic, the 3DP technologies are facilitating creative solutions in terms of personal protective equipment (PPE), medical equipment (such as ventilators and other respiratory devices), and other health and welfare tools to aid the personal hygiene as well as safe environment for humans by restricting the communication of risks. Various sources (including journal articles, news articles, white papers of the government and other non-profit organizations, commercial enterprises, as well as academic institutions have been reviewed for the collection of the information relevant to COVID-19 and 3DP. This communication presents the recent applications of the 3DP technologies aiding in developing innovative products designed to save the lives of millions of people around the world. Moreover, the potential of 3DP technologies in developing test swabs and controlled medicines has been highlighted. The literature reviewed in the present study indicated that the fused filament fabrication (FFF) is one of the most preferred technologies and contribute about 62% in the overall production of the protective gears developed through overall class of 3DP.     

Optimization Model for Selecting Temporary Hospital Locations During COVID-19 Pandemic

The two main approaches that countries are using to ease the strain on healthcare infrastructure is building temporary hospitals that are specialized in treating COVID-19 patients and promoting preventive measures. As such, the selection of the optimal location for a temporary hospital and the calculation of the prioritization of preventive measures are two of the most critical decisions during the pandemic, especially in densely populated areas where the risk of transmission of the virus is highest. If the location selection process or the prioritization of measures is poor, healthcare workers and patients can be harmed, and unnecessary costs may come into play. In this study, a decision support framework using a fuzzy analytic hierarchy process (FAHP) and a weighted aggregated sum product assessment model are proposed for selecting the location of a temporary hospital, and a FAHP model is proposed for calculating the prioritization of preventive measures against COVID-19. A case study is performed for Ho Chi Minh City using the proposed decision-making framework. The contribution of this work is to propose a multiple criteria decision-making model in a fuzzy environment for ranking potential locations for building temporary hospitals during the COVID-19 pandemic. The results of the study can be used to assist decision-makers, such as government authorities and infectious disease experts, in dealing with the current pandemic as well as other diseases in the future. With the entire world facing the global pandemic of COVID-19, many scientists have applied research achievements in practice to help decision-makers make accurate decisions to prevent the pandemic. As the number of cases increases exponentially, it is crucial that government authorities and infectious disease experts make optimal decisions while considering multiple quantitative and qualitative criteria. As such, the proposed approach can also be applied to support complex decision-making processes in a fuzzy environment in different countries.