Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation

A synergistic study of policy and literature on rumor governance

Rumor governance is an important guarantee for social stability and public safety. Based on the life cycle and crisis cycle model, this paper conducts a synergistic analysis of China's rumor governance policies and regulations and the core scientific research literature on rumor governance in WOS and CNKI. In this paper, we use the TF-IDF algorithm to count the word frequencies of 326 policy and regulation texts, the Jieba-RoBERTa-Kmeans model to cluster high-frequency keywords, and CiteSpace software and the LLR clustering algorithm are utilized to extract and cluster keywords from 391 documents in the WOS database and from 703 documents in the CNKI database. Based on the synergistic analysis of the life cycle model, it is found that the research on policies and regulations precedes the research on literature, and both are in the period of refinement.Based on the synergistic analysis using the co-occurrence comparison of subject terms in the crisis cycle model, it is found that there is a lack of research in the stages of prevention, monitoring, and governance, and this paper proposes the systematic governance mechanism and strategy for crisis resolution that conforms to the trend of life cycle evolution and is synergistic with policy and literature. This study has only selected Chinese policies and regulations, and the proposed governance strategies have not yet been verified in practice; future research can expand the scope and depth of the study and conduct empirical research and pilot projects.

Does a perceptual gap lead to actions against digital misinformation? A third-person effect study among medical students

BACKGROUND

We are making progress in the fight against health-related misinformation, but mass participation and active engagement are far from adequate. Focusing on pre-professional medical students with above-average medical knowledge, our study examined whether and how third-person perceptions (TPP), which hypothesize that people tend to perceive media messages as having a greater effect on others than on themselves, would motivate their actions against misinformation.

METHODS

We collected the cross-sectional data through a self-administered paper-and-pencil survey of 1,500 medical students in China during April 2022.

RESULTS

Structural equation modeling (SEM) analysis, showed that TPP was negatively associated with medical students' actions against digital misinformation, including rebuttal of misinformation and promotion of corrective information. However, self-efficacy and collectivism served as positive predictors of both actions. Additionally, we found professional identification failed to play a significant role in influencing TPP, while digital misinformation self-efficacy was found to broaden the third-person perceptual gap and collectivism tended to reduce the perceptual bias significantly.

CONCLUSIONS

Our study contributes both to theory and practice. It extends the third-person effect theory by moving beyond the examination of restrictive actions and toward the exploration of corrective and promotional actions in the context of misinformation., It also lends a new perspective to the current efforts to counter digital misinformation; involving pre-professionals (in this case, medical students) in the fight.

Graph global attention network with memory: A deep learning approach for fake news detection

With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To address this issue, deep learning has emerged as a promising approach, especially with the development of Natural Language Processing (NLP). This study introduces a novel approach called Graph Global Attention Network with Memory (GANM) for detecting fake news. This approach leverages NLP techniques to encode nodes with news context and user content. It employs three graph convolutional networks to extract informative features from the news propagation network and aggregates endogenous and exogenous user information. This methodology aims to address the challenge of identifying fake news within the context of social media. Innovatively, the GANM combines two strategies. First, a novel global attention mechanism with memory is employed in the GANM to learn the structural homogeneity of news propagation networks, which is the attention mechanism of a single graph with a history of all graphs. Second, we design a module for partial key information learning aggregation to emphasize the acquisition of partial key information in the graph and merge node-level embeddings with graph-level embeddings into fine-grained joint information. Our proposed method provides a new direction in news detection research with a combination of global and partial information and achieves promising performance on real-world datasets.

Ivermectin prescription fill rates among U.S. Military members during the coronavirus disease 2019 (COVID-19) pandemic

This report describes ivermectin prescription fill rates among U.S. active component service members (ACSM) over time during the early phases of the COVID-19 pandemic. Information about the unsubstantiated benefits of ivermectin for coronavirus 2019 (COVID-19) prevention and treatment was widely available online early in the COVID-19 pandemic. Ivermectin prescription fill rates increased among ACSM during periods of Alpha and Delta coronavirus variant predominance, but not during the predominance of the Omicron variant. At the peak of the fill rate curve, in August 2021, rates were higher among men compared to women, older compared to younger age groups, senior officers compared to junior officers, senior enlisted compared to junior enlisted service members, and those with a bachelor's or advanced degree compared to those without a bachelor's degree. Ivermectin prescriptions were more likely to have been filled at a retail pharmacy than at a military hospital or clinic. During the COVID-19 pandemic fill rates for ivermectin prescriptions among ACSM increased, including those without a qualifying diagnosis. Rates peaked in August 2021 but subsequently declined. The decrease in ivermectin fill rates was coincident with vigorous efforts to correct previous misinformation and implement pre-authorization requirements for prescriptions. Research on the impact of unproven online claims about clinical and public health interventions has potential to curtail future unnecessary and potentially harmful treatments.

Between brand attacks and broader narratives: How direct and indirect misinformation erode consumer trust

Misinformation can take various forms, from political propaganda and health-related fake news to conspiracy theories. This review investigates the consequences of both direct and indirect misinformation for brands and consumers. We review the marketing literature focused on the consequences of misinformation spread and propose a framework that acknowledges the relationship between brands and consumers in a misinformation environment. We argue that the primary consequence of misinformation is the erosion of trust among the various actors in the marketplace. Additionally, we highlight that a comprehensive understanding of the consequences of misinformation should also consider the effects of indirect misinformation on the marketplace.

(Why) Is Misinformation a Problem?

In the last decade there has been a proliferation of research on misinformation. One important aspect of this work that receives less attention than it should is exactly why misinformation is a problem. To adequately address this question, we must first look to its speculated causes and effects. We examined different disciplines (computer science, economics, history, information science, journalism, law, media, politics, philosophy, psychology, sociology) that investigate misinformation. The consensus view points to advancements in information technology (e.g., the Internet, social media) as a main cause of the proliferation and increasing impact of misinformation, with a variety of illustrations of the effects. We critically analyzed both issues. As to the effects, misbehaviors are not yet reliably demonstrated empirically to be the outcome of misinformation; correlation as causation may have a hand in that perception. As to the cause, advancements in information technologies enable, as well as reveal, multitudes of interactions that represent significant deviations from ground truths through people's new way of knowing (intersubjectivity). This, we argue, is illusionary when understood in light of historical epistemology. Both doubts we raise are used to consider the cost to established norms of liberal democracy that come from efforts to target the problem of misinformation.

Analyzing facial action units in children to differentiate genuine and fake pain during inferior alveolar nerve block: a cross-sectional study

This study aimed to investigate the association between facial action units and pain levels in Syrian children, focusing on both genuine and fake pain expressions. A total of 300 Syrian children aged 6-9 years participated in the study. Pain levels were assessed using the validated Face, Legs, Activity, Cry, Consolability scale, and facial expressions were analyzed using the Facial Action Coding System. The children were asked to mimic their feelings after receiving a dental injection to elicit fake pain expressions. Statistical analysis, including multinomial logistic regression and chi-square tests, was conducted to determine the Action Units (AUs) associated with each pain level and to compare the differences between real and fake pain expressions. The results revealed significant associations between specific AUs and pain levels. For real pain expressions, the most activated AUs across different pain levels with positive coefficient values of correlation (P-value < 0.01) were analyzed. In contrast, for fake pain expressions, AU12 and AU38 were consistently observed to be the most activated. These findings suggest that certain AUs are uniquely associated with fake pain expressions, distinct from those observed in real pain expressions. Furthermore, there were no significant differences between boys and girls in terms of their genuine and fake pain expressions, indicating a similar pattern of AU activation (P-value > 0.05). It was concluded that AUs 4, 6, 41, and 46 were associated with mild pain, and AUs 4, 6, 41, 46, and 11 were associated with moderate pain cases. In severe pain, AUs 4, 6, 7, 9, 11, and 43 were associated. In fake pain feelings, AU43, AU38, and AU12 were the most activated with no difference between boys and girls.

Complementary, alternative, and integrative medicine-specific COVID-19 misinformation on social media: A scoping review

Background

The sharing of health-related information has become increasingly popular on social media. Unregulated information sharing has led to the spread of misinformation, especially regarding complementary, alternative, and integrative medicine (CAIM). This scoping review synthesized evidence surrounding the spread of CAIM-related misinformation on social media during the COVID-19 pandemic.

Methods

This review was informed by a modified version of the Arksey and O'Malley scoping review framework. AMED, EMBASE, PsycINFO and MEDLINE databases were searched systematically from inception to January 2022. Eligible articles explored COVID-19 misinformation on social media and contained sufficient information on CAIM therapies. Common themes were identified using an inductive thematic analysis approach.

Results

Twenty-eight articles were included. The following themes were synthesized: 1) misinformation prompts unsafe and harmful behaviours, 2) misinformation can be separated into different categories, 3) individuals are capable of identifying and refuting CAIM misinformation, and 4) studies argue governments and social media companies have a responsibility to resolve the spread of COVID-19 misinformation.

Conclusions

Misinformation can spread more easily when shared on social media. Our review suggests that misinformation about COVID-19 related to CAIM that is disseminated online contributes to unsafe health behaviours, however, this may be remedied via public education initiatives and stricter media guidelines. The results of this scoping review are crucial to understanding the behavioural impacts of the spread of COVID-19 misinformation about CAIM therapies, and can inform the development of public health policies to mitigate these issues.

The effect of disinformation about COVID-19 on consumer confidence: Insights from a survey experiment

Although the COVID-19 pandemic was accompanied by an infodemic about the origin of the virus and effectiveness of vaccines, little is known about the causal effect of this disinformation on the economy. This article fills in this void by examining the effects of disinformation about COVID-19 vaccines on consumer confidence by means of an original survey experiment in Dutch speaking communities of Belgium. Our findings show that the information set that impacts consumer confidence is much broader than previously assumed. We show that disinformation changes the perception of the effectiveness of vaccines which in turn indirectly impacts the future economic outlook, measured by the metric consumer confidence. Moreover, we find that the above effects are larger for respondents exposed to disinformation that is framed as containing 'scientific evidence' compared to 'conspiracy frames'.

Fake news, disinformation and misinformation in social media: a review

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Microblog sentiment analysis based on deep memory network with structural attention

Microblog sentiment analysis has important applications in many fields, such as social media analysis and online product reviews. However, the traditional methods may be challenging to compute the long dependencies between them and easy to lose some semantic information due to low standardization of text and emojis in microblogs. In this paper, we propose a novel deep memory network with structural self-attention, storing long-term contextual information and extracting richer text and emojis information from microblogs, which aims to improve the performance of sentiment analysis. Specifically, the model first utilizes a bidirectional long short-term memory network to extract the semantic information in the microblogs, and considers the extraction results as the memory component of the deep memory network, storing the long dependencies and free of syntactic parser, sentiment lexicon and feature engineering. Then, we consider multi-step structural self-attention operations as the generalization and output components. Furthermore, this study also employs a penalty mechanism to the loss function to promote the diversity across different hops of attention in the model. This study conducted extensive comprehensive experiments with eight baseline methods on real datasets. Results show that our model outperforms those state-of-the-art models, which validates the effectiveness of the proposed model.

Could Belief in Fake News Predict Vaccination Behavior in the Elderly?

Willingness to get a vaccine was important during the COVID-19 pandemic. Previous studies suggest that vaccine hesitation during the pandemic could have been related to truth discernment, belief in information, exposure to misinformation, attitudes to vaccines, and conspiracy beliefs. Previous studies were mostly with younger adults, and studies with older adults are lacking. This study aimed to analyze the relationship between the trust or belief in fake online news (print news was not included), truth discernment, attitudes, and willingness to be vaccinated during the COVID-19 pandemic while controlling for some significant factors/variables that could affect vaccination in a sample of older adults. There were 504 pre-retirees and retirees participating in this study. Participants from Lithuania age ranged from 50 to 90 years old (M = 64.37, SD = 9.10), 58.3 percent were females. Results from several path models predicting the participants willingness to get a vaccine suggested that stronger conspiracy beliefs and skeptical attitudes toward vaccination would be related to lower willingness to get vaccinated. Participants who disbelieved in the headlines were already vaccinated. Therefore, it seems that discernment (the ability to distinguish which information is true and which is not) is not related to the willingness to vaccinate.

Evolutionary emergence of collective intelligence in large groups of students

The emergence of collective intelligence has been studied in much greater detail in small groups than in larger ones. Nevertheless, in groups of several hundreds or thousands of members, it is well-known that the social environment exerts a considerable influence on individual behavior. A few recent papers have dealt with some aspects of large group situations, but have not provided an in-depth analysis of the role of interactions among the members of a group in the creation of ideas, as well as the group's overall performance. In this study, we report an experiment where a large set of individuals, i.e., 789 high-school students, cooperated online in real time to solve two different examinations on a specifically designed platform (Thinkhub). Our goal of this paper 6 to describe the specific mechanisms of idea creation we were able to observe and to measure the group's performance as a whole. When we deal with communication networks featuring a large number of interacting entities, it seems natural to model the set as a complex system by resorting to the tools of statistical mechanics. Our experiment shows how an interaction in small groups that increase in size over several phases, leading to a final phase where the students are confronted with the most popular answers of the previous phases, is capable of producing high-quality answers to all examination questions, whereby the last phase plays a crucial role. Our experiment likewise shows that a group's performance in such a task progresses in a linear manner in parallel with the size of the group. Finally, we show that the controlled interaction and dynamics foreseen in the system can reduce the spread of "fake news" within the group.

Freedom and Constraint in Digital Environments: Implications for the Self

We evaluate how features of the digital environment free or constrain the self. Based on the current empirical literature, we argue that modern technological features, such as predictive algorithms and tracking tools, pose four potential obstacles to the freedom of the self: lack of privacy and anonymity, (dis)embodiment and entrenchment of social hierarchy, changes to memory and cognition, and behavioral reinforcement coupled with reduced randomness. Comparing these constraints on the self to the freedom promised by earlier digital environments suggests that digital reality can be designed in more freeing ways. We describe how people reassert personal agency in the face of the digital environment's constraints and provide avenues for future research regarding technology's influence on the self.

The Mediation Effects of Social Media Usage and Sharing Fake News about Companies

Trust in social media information is gaining in importance and relevance for both companies and individuals as nowadays contemporary society is confronted with a wave of fake news about daily life situations, brands, organizations, etc. As it becomes more difficult to accurately assess social media information and to determine its origin or source, as well as to be able to double-check information spread across different Social Networking Sites (SNS), businesses must understand how individuals' perceived control, concentration, and time distortion enhances the social media usage, thus allowing them to correctly assess online information. Therefore, the scope of the paper is to assess, based on a conceptual model, the antecedents of trust in online information about companies by considering users' perceived control, concentration, and time distortion, while browsing social media networks and sharing fake news about companies in SNS. With the help of an online survey, data was collected from social media users, later being analysed with SmartPLS. The findings suggest that social media usage and sharing of fake news mediate the relationship between users' perceived control, concentration, and time distortion (i.e., flow characteristics) and trust in online information about companies.

eHealth Literacy and its Associated Factors Among Health Professionals During the COVID-19 Pandemic in Resource-Limited Settings: Cross-sectional Study

Background

The COVID-19 pandemic has wreaked havoc on health care systems and governments worldwide. Although eHealth literacy is acknowledged as a critical component of public health, it was overlooked during the pandemic. To assist patients and their families, health professionals should be knowledgeable about online health information resources and capable of evaluating relevant online information. In a resource-constrained situation, the level of eHealth literacy among health professionals is not well documented.

Objective

The aim of this study was to assess the eHealth literacy level and its associated factors among health professionals working in Amhara regional state teaching hospitals, Ethiopia.

Methods

A self-administered questionnaire was used in an institutional-based cross-sectional study design. Descriptive statistics were calculated to describe eHealth literacy statements and key variables using SPSS v.24. Bivariable and multivariable logistic regression models were fit to identify factors related to eHealth literacy. Variables with P<.05 were declared to be statistically significant predictors.

Results

A total of 383 participants completed and returned the questionnaire with a response rate of 90.5%. Health professionals demonstrated a moderate level of eHealth literacy (mean 29.21). Most of the professionals were aware of the available health resources located on the internet, and know how to search and locate these resources. However, they lack the ability to distinguish high-quality health resources from low-quality resources. Factors that were significantly associated with eHealth literacy were computer access, computer knowledge, perceived ease of use, and perceived usefulness of eHealth information resources.

Conclusions

It is crucial to provide training and support to health care workers on how to find, interpret, and, most importantly, evaluate the quality of health information found on the internet to improve their eHealth literacy level. Further research is needed to explore the role of eHealth literacy in mitigating pandemics in developing countries.

Associations between Coping Strategies and Cyberhate Involvement: Evidence from Adolescents across Three World Regions

Cyberhate represents a risk to adolescents' development and peaceful coexistence in democratic societies. Yet, not much is known about the relationship between adolescents' ability to cope with cyberhate and their cyberhate involvement. To fill current gaps in the literature and inform the development of media education programs, the present study investigated various coping strategies in a hypothetical cyberhate scenario as correlates for being cyberhate victims, perpetrators, and both victim-perpetrators. The sample consisted of 6829 adolescents aged 12-18 years old (M_age = 14.93, SD = 1.64; girls: 50.4%, boys: 48.9%, and 0.7% did not indicate their gender) from Asia, Europe, and North America. Results showed that adolescents who endorsed distal advice or endorsed technical coping showed a lower likelihood to be victims, perpetrators, or victim-perpetrators. In contrast, if adolescents felt helpless or endorsed retaliation to cope with cyberhate, they showed higher odds of being involved in cyberhate as victims, perpetrators, or victim-perpetrators. Finally, adolescents who endorsed close support as a coping strategy showed a lower likelihood to be victim-perpetrators, and adolescents who endorsed assertive coping showed higher odds of being victims. In conclusion, the results confirm the importance of addressing adolescents' ability to deal with cyberhate to develop more tailored prevention approaches. More specifically, such initiatives should focus on adolescents who feel helpless or feel inclined to retaliate. In addition, adolescents should be educated to practice distal advice and technical coping when experiencing cyberhate. Implications for the design and instruction of evidence-based cyberhate prevention (e.g., online educational games, virtual learning environments) will be discussed.

Factors influencing fake news rebuttal acceptance during the COVID-19 pandemic and the moderating effect of cognitive ability

Fake news is spreading rapidly on social media and poses a serious threat to the COVID-19 outbreak response. This study thus aims to reveal the factors influencing the acceptance of fake news rebuttals on Sina Weibo. Drawing on the elaboration likelihood model (ELM), we used text mining and the econometrics method to investigate the relationships among the central route (rebuttal's information readability and argument quality), peripheral route (rebuttal's source credibility, including authority and influence), and rebuttal acceptance, as well as the moderating effect of receiver's cognitive ability on these relationships. Our findings suggest that source authority had a negative effect on rebuttal acceptance, while source influence had a positive effect. Second, both information readability and argument quality had positive effects on rebuttal acceptance. In addition, individuals with low cognitive abilities relied more on source credibility and argument quality to accept rebuttals, while individuals with high cognitive abilities relied more on information readability. This study can provide decision support for practitioners to establish more effective fake news rebuttal strategies; it is especially valuable to reduce the negative impact of fake news related to major public health emergencies and safeguard the implementation of anti-epidemic strategies.

(Mis)perceptions and engagement on Twitter: COVID-19 vaccine rumors on efficacy and mass immunization effort

This paper reports the findings of a 606-participant study analyzing the perception of, and engagement with, COVID-19 vaccine rumors on efficacy and mass immunization effort on Twitter. Misperceptions were successfully induced through simple content alterations and the addition of popular anti-COVID-19 hashtags such as #COVIDIOT and #covidhoax to otherwise valid Twitter content. Twitter's soft moderation warning label helped the majority of our participants to dismiss the rumors about mass immunization. However, for the skeptic, vaccine-hesitant minority, the soft moderation caused a “backfire effect” i.e., make them perceive the rumor as accurate. While the majority of the participants staunchly refrain from engaging with the COVID-19 rumors, the hesitant and skeptic minority was open to comment, retweet, like and share the vaccine efficacy rumors. Based on these findings, we recommend misinformation label designs to prevent the “backfire effect” of COVID-19 vaccine rumors on Twitter.

Ridge count thresholding to uncover coordinated networks during onset of the Covid-19 pandemic

In order to combat information operations (IO) and disinformation campaigns, one must look at the behaviors of the accounts pushing specific narratives and stories through social media, not at the content itself. In this work, we present a new process for extracting tweet storms and uncovering networks of accounts that are working in a coordinated fashion using ridge count thresholding (RCT). To do this, we started with a dataset of 60 million individual tweets from the early weeks of the Covid-19 pandemic. Coherent topics are extracted from this data by testing three different preprocessing pipelines and applying Orthogonal Nonnegative Matrix Factorization (ONMF). The most effective preprocessing pipeline used hashtag preclustering to downselect the total dataset to the 7 million tweets that included the top hashtags. Each topic identified by ONMF is described by a topic-tweet signal, crafted using the time stamp included in each tweet’s metadata. These signals were broken down into tweet storms using RCT, which is calculated from the Dynamic Wavelet Fingerprint transform of each topic-tweet signal. Each tweet storm described a time of increased activity around a topic. Tweet storms identified in this way each represent some behavior in the underlying network. In total, we identified 39,817 total tweet storms that included about 2 million unique tweets. These tweet storms were used to identify networks of accounts that commonly co-occur within tweet storms to isolate those communities most responsible for driving narratives and pushing stories through social media. Through this process, we were able to identify 22 unique networks of accounts that were densely connected based on RCT tweet storm identification. Many of the identified networks exhibit obvious inauthentic behaviors that are potentially a part of an IO campaign.

Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains

Many scholars have explored the COVID-19 impact on the crude oil, gold, and Bitcoin markets, whereas most have ignored the media coverage influence. This paper focuses on examining information spillover from epidemic-related news to the crude oil, gold, and Bitcoin markets with the time-frequency analysis method. The empirical results reveal that both the return and volatility spillovers from epidemic-related news to the crude oil, gold, and Bitcoin markets are stronger in the short term (less than 1 week). In the long term, only the media sentiment index notably impacts crude oil, gold, and Bitcoin market returns. The volatility spillover from media coverage to crude oil mainly occurs in the short term. Regarding the gold and Bitcoin markets, the long-term volatility spillovers are significant. An obvious risk contagion path is found. Media hype is the main risk transmitter and transmits vast shocks to these three markets, especially the Bitcoin market, which subsequently transmits these shocks to the gold market. Risk accumulates systemically in the gold and Bitcoin markets. These findings have crucial empirical implications for policymakers and investors when formulating related short- or long-term decisions during the pandemic.

COVID-19 infodemic and depressive symptoms: The impact of the exposure to news about COVID-19 on the general Paraguayan population

INTRODUCTION

The COVID-19 pandemic has led to an increase of social stressors and mental health issues in the general population as well as among mentally ill patients. A COVID-19- related "infodemic", including too much information in digital and physical environments, has been recognized globally.

AIM

This study aimed to describe the impact of COVID-19 infodemic (exposure to news related to COVID-19) in terms of depressive symptoms in the Paraguayan general population.

METHODS

This was a descriptive and cross-sectional study. An online survey, designed in Google Forms, has been launched nationwide through the most popular social networks (Facebook, Twitter, Instagram) and messaging applications (WhatsApp, Telegram) in April (1st-30th) 2021. The Mental Health Inventory-5 (MHI-5) was employed for detecting depressive symptoms.

RESULTS

The survey included 1102 responders aged 35.4 ± 12.9 years old, 74.9% were women. MHI-5 mean score was 44.07 ± 14.16 in the general population with 34.4% (n = 379) of responders suffering from severe symptoms of depression (>52, as cut-off point). 53.5% of sample reported to have been exposed between 1 and 3 h to COVID-19 news, daily. An OR 1.933 (95% CI 1.48 - 2.52) was found between the exposure to news and depressive symptoms.

CONCLUSIONS

This study suggests that people exposed to a higher number of hours of COVID-19 news were 93.3% more likely to develop depressive symptoms.

Responses to digital disinformation as part of hybrid threats: a systematic review on the effects of disinformation and the effectiveness of fact-checking/debunking

The dissemination of purposely deceitful or misleading content to target audiences for political aims or economic purposes constitutes a threat to democratic societies and institutions, and is being increasingly recognized as a major security threat, particularly after evidence and allegations of hostile foreign interference in several countries surfaced in the last five years. Disinformation can also be part of hybrid threat activities. This research paper examines findings on the effects of disinformation and addresses the question of how effective counterstrategies against digital disinformation are, with the aim of assessing the impact of responses such as the exposure and disproval of disinformation content and conspiracy theories. The paper's objective is to synthetize the main scientific findings on disinformation effects and on the effectiveness of debunking, inoculation, and forewarning strategies against digital disinformation. A mixed methodology is used, combining qualitative interpretive analysis and structured technique for evaluating scientific literature such as a systematic literature review (SLR), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework.

A Transformer-Based Model for Evaluation of Information Relevance in Online Social-Media: A Case Study of Covid-19 Media Posts

Online social media has become a major source of information gathering for a huge section of society. As the amount of information flows in online social media is enormous but on the other hand, the fact-checking sources are limited. This shortfall of fact-checking gives birth to the problem of misinformation and disinformation in the case of the truthfulness of facts on online social media which can have serious effects on the wellbeing of society. This problem of misconception becomes more rapid and critical when some events like the recent outbreak of Covid-19 happen when there is no or very little information is available anywhere. In this scenario, the identification of the content available online which is mostly propagated from person to person and not by any governing authority is very needed at the hour. To solve this problem, the information available online should be verified properly before being conceived by any individual. We propose a scheme to classify the online social media posts (Tweets) with the help of the BERT (Bidirectional Encoder Representations from Transformers)-based model. Also, we compared the performance of the proposed approach with the other machine learning techniques and other State of the art techniques available. The proposed model not only classifies the tweets as relevant or irrelevant, but also creates a set of topics by which one can identify a text as relevant or irrelevant to his/her need just by just matching the keywords of the topic. To accomplish this task, after the classification of the tweets, we apply a possible topic modelling approach based on latent semantic analysis and latent Dirichlet allocation methods to identify which of the topics are mostly propagated as false information.

Analysis on Health Information Acquisition of Social Network Users by Opinion Mining: Case Analysis Based on the Discussion on COVID-19 Vaccinations

This study aims to explore phenomena and laws that occur when different users on social network platforms obtain health information by constructing an opinion mining model, analyzing the user's position on selected cases, and exploring the reflection of the phenomenon of truth decay on platforms. It selects group posts regarding the COVID-19 vaccination dispute on the Douban platform, analyzes the positions of different users, and explores phenomena related to users obtaining health information on domestic social platforms according to different topics and information behaviors. The results reveal a linear relationship between the negative and neutral attitudes of netizens on social networking platforms. Moreover, netizens tend to hold subjective language when expressing their views and attitudes, and their views on social platforms will not change easily. The study explores the health information acquisition behavior of netizens on social platforms based on the constructed user opinion mining model. The study is helpful for relevant units and platforms to make scientific decisions and provide guidance according to different positions of Internet users.

Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda.

Clusters of Skeptical Consumers Based on Technology and AI Acceptance, Perception of Social Media Information and Celebrity Trend Setter

The present consumer is surrounded every day by a huge amount of data and information and is confronted with the need to process the received information. Based on the existing content and the development of user generated content and fake news, consumers develop more and more a skeptical opinion regarding existing media information. In this paper we determine four clusters of consumers based on technology and artificial intelligence (AI) acceptance, skeptical opinion regarding media information, need for validation of received information and the influence of celebrity trend setters. For each of the clusters, we analyze the relationship between the skeptical opinion of consumers related to social media information and some present trends about the development of technologies and AI, the influence of celebrity trend setters and the need to check the received information. The results of the research show that the development of technologies and AI have an influence on the skeptical opinion related to media information for some consumer groups. The celebrity trend setter has a significant influence on the skeptical opinion only for one of the consumer groups. The need for information and news validation is related to the skeptical opinion regarding media information, but in different ways for the determined clusters.