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1.
《Information processing & management》2022,59(6):103073
With the onset of COVID-19, the pandemic has aroused huge discussions on social media like Twitter, followed by many social media analyses concerning it. Despite such an abundance of studies, however, little work has been done on reactions from the public and officials on social networks and their associations, especially during the early outbreak stage. In this paper, a total of 9,259,861 COVID-19-related English tweets published from 31 December 2019 to 11 March 2020 are accumulated for exploring the participatory dynamics of public attention and news coverage during the early stage of the pandemic. An easy numeric data augmentation (ENDA) technique is proposed for generating new samples while preserving label validity. It attains superior performance on text classification tasks with deep models (BERT) than an easier data augmentation method. To demonstrate the efficacy of ENDA further, experiments and ablation studies have also been implemented on other benchmark datasets. The classification results of COVID-19 tweets show tweets peaks trigged by momentous events and a strong positive correlation between the daily number of personal narratives and news reports. We argue that there were three periods divided by the turning points on January 20 and February 23 and the low level of news coverage suggests the missed windows for government response in early January and February. Our study not only contributes to a deeper understanding of the dynamic patterns and relationships of public attention and news coverage on social media during the pandemic but also sheds light on early emergency management and government response on social media during global health crises. 相似文献
2.
《Information processing & management》2022,59(3):102935
The rapid dissemination of misinformation in social media during the COVID-19 pandemic triggers panic and threatens the pandemic preparedness and control. Correction is a crucial countermeasure to debunk misperceptions. However, the effective mechanism of correction on social media is not fully verified. Previous works focus on psychological theories and experimental studies, while the applicability of conclusions to the actual social media is unclear. This study explores determinants governing the effectiveness of misinformation corrections on social media with a combination of a data-driven approach and related theories on psychology and communication. Specifically, referring to the Backfire Effect, Source Credibility, and Audience’s role in dissemination theories, we propose five hypotheses containing seven potential factors (regarding correction content and publishers’ influence), e.g., the proportion of original misinformation and warnings of misinformation. Then, we obtain 1487 significant COVID-19 related corrections on Microblog between January 1st, 2020 and April 30th, 2020, and conduct annotations, which characterize each piece of correction based on the aforementioned factors. We demonstrate several promising conclusions through a comprehensive analysis of the dataset. For example, mentioning excessive original misinformation in corrections would not undermine people’s believability within a short period after reading; warnings of misinformation in a demanding tone make correction worse; determinants of correction effectiveness vary among different topics of misinformation. Finally, we build a regression model to predict correction effectiveness. These results provide practical suggestions on misinformation correction on social media, and a tool to guide practitioners to revise corrections before publishing, leading to ideal efficacies. 相似文献
3.
《Information processing & management》2022,59(6):103095
Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information. 相似文献
4.
Coronavirus related discussions have spiraled at an exponential rate since its initial outbreak. By the end of May, more than 6 million people were diagnosed with this infection. Twitter witnessed an outpouring of anxious tweets through messages associated with the spread of the virus. Government and health officials replied to the troubling tweets, reassuring the public with regular alerts on the virus's progress and information to defend against the virus. We observe that social media users are worried about Covid 19-related crisis and we identify three separate conversations on virus contagion, prevention, and the economy. We analyze the tone of officials’ tweet text as alarming and reassuring and capture the response of Twitter users to official communications. Such studies can provide insights to health officials and government agencies for crisis management, specifically regarding communicating emergency information to the public via social media for establishing reassurance. 相似文献
5.
《Information processing & management》2023,60(3):103294
The paper presents new annotated corpora for performing stance detection on Spanish Twitter data, most notably Health-related tweets. The objectives of this research are threefold: (1) to develop a manually annotated benchmark corpus for emotion recognition taking into account different variants of Spanish in social posts; (2) to evaluate the efficiency of semi-supervised models for extending such corpus with unlabelled posts; and (3) to describe such short text corpora via specialised topic modelling.A corpus of 2,801 tweets about COVID-19 vaccination was annotated by three native speakers to be in favour (904), against (674) or neither (1,223) with a 0.725 Fleiss’ kappa score. Results show that the self-training method with SVM base estimator can alleviate annotation work while ensuring high model performance. The self-training model outperformed the other approaches and produced a corpus of 11,204 tweets with a macro averaged f1 score of 0.94. The combination of sentence-level deep learning embeddings and density-based clustering was applied to explore the contents of both corpora. Topic quality was measured in terms of the trustworthiness and the validation index. 相似文献
6.
《Information processing & management》2023,60(1):103163
Guided by three major theoretical frameworks, this meta-analysis synthesizes 17 empirical studies (15 articles with 18,297 participants, 13 of them are from non-representative samples) and quantifies the effect sizes of a list of antecedents (e.g., cognitive, affective, and social factors) on information avoidance during the COVID-19 context. Findings indicated that information-related factors including channel belief (r = -0.35, p < .01) and information overload (r = 0.23, p < .01) are more important in determining individual's avoidance behaviors toward COVID-19 information. Factors from the psychosocial aspects, however, had low correlations with information avoidance. While informational subjective norms released a negative correlation (r = -0.16, p < .1) which was approaching significant, positive and negative risk responses were not associated with information avoidance. Moderator analysis further revealed that the impacts of several antecedents varied for people with different demographic characteristics (i.e., age, gender, region of origin), and under certain sampling methods. Theoretically, this meta-analysis may help determine the most dominant factors from a larger landscape, thus providing valuable directions to refine frameworks and approaches in health information behaviors. Findings from moderator analysis have also practically inspired certain audience segmentation strategies to tackle occurrence of information avoidance during the COVID-19 pandemic. 相似文献
7.
《Information processing & management》2022,59(4):102990
Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations’ representations did not align much with the other two groups, suggesting a lack of organizations’ influence in public representations of COVID-19 on social media in China. 相似文献
8.
Grounded in the vast changes to work life (jobs) and home life that people are facing due to the COVID-19 pandemic (hereinafter COVID), this article presents five research directions related to COVID’s impacts on jobs—i.e., job loss, job changes, job outcomes, coping, and support—and five research directions related to COVID’s impact on home life—i.e., home life changes, children, life-related outcomes, social life, and support. In addition to this, I discuss overarching possible research directions and considerations for researchers, editors, and reviewers, as we continue our scientific journey to support people through this pandemic and beyond. I organize these directions and considerations into two sets of five each: focal groups that should be studied—i.e., underprivileged populations, different countries and cultural contexts, women (vs. men), workers in healthcare (frontline workers), elderly and at-risk—and five general issues and special considerations—i.e., role of technology as the oxygen, pre- vs. mid- vs. post-COVID studies, constraints on data collection/research due to COVID, evolution of COVID, and focus on contextualization (generalizability is irrelevant). 相似文献
9.
《Information processing & management》2022,59(3):102918
This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks.Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence. 相似文献
10.
《Information processing & management》2022,59(3):102932
Pregnant women are experiencing enormous physical changes and suffering pregnancy-related losses, which may lead to depression symptoms during pregnancy. Given that the onslaught of COVID-19 had exacerbated pregnant women's anxiety because of disruptions in antenatal care and concerns regarding safe delivery, it is worth exploring how they obtain social support to cope with stress during COVID-19. Although many works have explored the impact of coping resources that people have on coping strategies, few studies have been done on the relationship between people's coping strategies and their acquisition of coping resources such as social support. To fill this gap, based on the stress and coping theory (SCT) and social penetration theory (SPT), this study investigates the impacts of pregnant women's different coping strategies on the acquisition of social support and the moderating role of the adverse impacts of COVID-19 and their online participation roles (support providers vs. support seekers) using the data of 814 pregnant women's online behavior from a parenting community in China1. Our study indicates that both women's superficial level disclosure and personal level disclosure positively affect online social support received. Moreover, self-disclosure about the adverse impacts of COVID-19 negatively moderates the relationship between personal level disclosure and social support received. Participation role positively moderates the relationship between personal level disclosure and social support received, but negatively moderates the relationship between superficial level disclosure and social support received. This paper makes theoretical contributions to the literature of SCT, SPT and the literature about social support in online communities. 相似文献
11.
《Information processing & management》2023,60(4):103325
Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet’s stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers’ tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods. 相似文献
12.
Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19. 相似文献
13.
《Research Policy》2022,51(1):104393
In this paper we draw a parallel between the insights developed within the framework of the current COVID-19 health crisis and the views and insights developed with respect to the long term environmental crisis, the implications for science, technology and innovation (STI) policy, Christopher Freeman analyzed already in the early 90′s. With at the time of writing, the COVID-19 pandemic entering in many countries a third wave with a very differentiated implementation path of vaccination across rich and poor countries, drawing such a parallel remains of course a relatively speculative exercise. Nevertheless, based on the available evidence of the first wave of the pandemic, we feel confident that some lessons from the current health crisis and its parallels with the long-term environmental crisis can be drawn. The COVID-19 pandemic has also been described as a “syndemic”: a term popular in medical anthropology which marries the concept of ‘synergy’ with ‘epidemic’ and provides conceptually an interesting background for these posthumous Freeman reflections on crises. The COVID-19 crisis affects citizens in very different and disproportionate ways. It results not only in rising structural inequalities among social groups and classes, but also among generations. In the paper, we focus on the growing inequality within two particular groups: youngsters and the impact of COVID-19 on learning and the organization of education; and as mirror picture, the elderly many of whom witnessed despite strict confinement in long-term care facilities, high mortality following the COVID-19 outbreak. From a Freeman perspective, these inequality consequences of the current COVID-19 health crisis call for new social STI policies: for a new “corona version” of inclusion versus exclusion. 相似文献
14.
《Information processing & management》2022,59(1):102796
In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method – AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice. 相似文献
15.
16.
Mariangela Palladino 《Biochemia medica : ?asopis Hrvatskoga dru?tva medicinskih biokemi?ara / HDMB》2021,31(3)
Coronavirus disease 2019 (COVID-19) pandemic represents a scientific and social crisis. One of the main unmet needs for coronavirus disease 2019 is its unpredictable clinical course, which can rapidly change in an irreversible outcome. COVID-19 patients can be classified into mild, moderate, and severe. Several haematological parameters, such as platelets, white blood cell total count, lymphocytes, neutrophils, (together with neutrophil-lymphocyte and platelet-lymphocyte ratio), and haemoglobin were described to be associated with COVID-19 infection and severity. The purpose of these review is to describe the current state of the art about complete blood count alterations during COVID-19 infection, and to summarize the crucial role of some haematological parameters during the course of the disease. Decreased platelet, lymphocyte, haemoglobin, eosinophil, and basophil count, increased neutrophil count and neutrophil-lymphocyte and platelet-lymphocyte ratio have been associated with COVID-19 infection and a worse clinical outcome. Our study adds some novelty about the identification of effective biomarkers of progressive disease, and might be helpful for diagnosis, prevention of complications, and effective therapy. 相似文献
17.
As social distancing and lockdown orders grew more pervasive, individuals increasingly turned to social media for support, entertainment, and connection to others. We posit that global health emergencies - specifically, the COVID-19 pandemic - change how and what individuals self-disclose on social media. We argue that IS research needs to consider how privacy (self-focused) and social (other-focused) calculus have moved some issues outside in (caused by a shift in what is considered socially appropriate) and others inside out (caused by a shift in what information should be shared for the public good). We identify a series of directions for future research that hold potential for furthering our understanding of online self-disclosure and its factors during health emergencies. 相似文献
18.
A. Gadwal D. Roy M. Khokhar A. Modi P. Sharma P. Purohit 《Indian journal of clinical biochemistry : IJCB》2021,36(4):459
The current pandemic of COVID-19, with its climbing number of cases and deaths, has us searching for tools for rapid, reliable, and affordable methods of detection on one hand, and novel, improved therapeutic strategies on the other. The currently employed RT-PCR method, despite its all-encompassing utility, has its shortcomings. Newer diagnostic tools, based on the Clustered Regularly Interspaced Short Palindromic Repeats/Cas(CRISPR-Cas) system, with its better diagnostic accuracy measures, have come up to fill that void. These assay platforms are expected to slowly take up the place of COVID-19 diagnostics. Further, the current therapeutic options focus mainly on counteracting the viral proteins and components and their entry into host cells. The CRISPR-based system, especially through the RNA-guided Cas13 approach, can identify the genomic characteristics of SARS-CoV-2 and provide a novel inhibition strategy for coronaviruses. In this mini-review, we have discussed the available and upcoming CRISPR-based diagnostic assays and the potential of the CRISPR/Cas system as a therapeutic or prevention strategy in COVID-19. CRISPR-Cas system shows promise in both diagnostics as well as therapeutics and may as well change the face of molecular diagnosis and precision medicine. 相似文献
19.
The dissemination of misinformation in health emergencies poses serious threats to public health and increases health anxiety. To understand the underlying mechanism of the dissemination of misinformation regarding health emergencies, this study creatively draws on social support theory and text mining. It also explores the roles of different types of misinformation, including health advice and caution misinformation and health help-seeking misinformation, and emotional support in affecting individuals’ misinformation dissemination behavior on social media and whether such relationships are contingent on misinformation ambiguity and richness. The theoretical model is tested using 12,101 textual data about COVID-19 collected from Sina Weibo, a leading social media platform in China. The empirical results show that health caution and advice, help seeking misinformation, and emotional support significantly increase the dissemination of misinformation. Furthermore, when the level of ambiguity and richness regarding misinformation is high, the effect of health caution and advice misinformation is strengthened, whereas the effect of health help-seeking misinformation and emotional support is weakened, indicating both dark and bright misinformation ambiguity and richness. This study contributes to the literature on misinformation dissemination behavior on social media during health emergencies and social support theory and provides implications for practice. 相似文献
20.
Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in risk-related tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes. 相似文献