An overview of online fake news: Characterization, detection, and discussion

Fake news detection: state-of-the-art review and advances with attention to Arabic language aspects

The proliferation of fake news has become a significant threat, influencing individuals, institutions, and societies at large. This issue has been exacerbated by the pervasive integration of social media into daily life, directly shaping opinions, trends, and even the economies of nations. Social media platforms have struggled to mitigate the effects of fake news, relying primarily on traditional methods based on human expertise and knowledge. Consequently, machine learning (ML) and deep learning (DL) techniques now play a critical role in distinguishing fake news, necessitating their extensive deployment to counter the rapid spread of misinformation across all languages, particularly Arabic. Detecting fake news in Arabic presents unique challenges, including complex grammar, diverse dialects, and the scarcity of annotated datasets, along with a lack of research in the field of fake news detection compared to English. This study provides a comprehensive review of fake news, examining its types, domains, characteristics, life cycle, and detection approaches. It further explores recent advancements in research leveraging ML, DL, and transformer-based techniques for fake news detection, with a special attention to Arabic. The research delves into Arabic-specific pre-processing techniques, methodologies tailored for fake news detection in the language, and the datasets employed in these studies. Additionally, it outlines future research directions aimed at developing more effective and robust strategies to address the challenge of fake news detection in Arabic content.

A public opinion propagation model for technological disasters

Public opinion on technological disasters is influenced by unique factors and characteristics. Based on the infectious disease model, this paper develops a public opinion dissemination model for technological disasters, considering factors such as disaster severity, government response, accountability, and the impact of both positive and negative media content. Using differential equation stability theory, we analyze the existence and stability of both the free propagation equilibrium point and the propagation equilibrium point. The next-generation matrix method is applied to calculate the propagation threshold, revealing that disaster severity, government response, and accountability are key factors in the spread of public opinion. Sensitivity analyses examine how these key factors affect public opinion dynamics. A case study on the Shiyan gas explosion in Hubei Province is presented, with microblog data used to calculate model parameters. The proposed public opinion dissemination model is applied to this case and compared with two other models, demonstrating the viability and effectiveness of the developed model. The analyses also show that well-handled government responses can help calm public opinion, even in cases where accountability is lacking. Finally, policy suggestions are offered to enhance public opinion management during technological disasters.

Mapping the terrain of social media misinformation: A scientometric exploration of global research

The rise of social media has enabled unrestricted information sharing, regardless of its accuracy. Unfortunately, this has also resulted in the widespread dissemination of misinformation. This study aims to provide a comprehensive scientometric analysis under the PRISMA paradigm to clarify the repetitive trajectory of misinformation on social media in the current digital age. In this study, 3724 publications on social media misinformation from the Web of Science between January 2010 and February 2024 were analyzed scientifically and metrically using CiteSpace software. The findings reveal a sharp increase in annual publication output starting from 2015. The United States of America and China have made more significant contributions in publication volume and global collaborations than other nations. The top five keywords with high frequency are social media, fake news, information, misinformation, and news. In contrast to a brief review of existing articles, this study provides an exhaustive review of annual scientific research output, journals, countries, institutions, contributors, highly cited papers, and keywords in social media misinformation research. The developmental stages of social media misinformation research are charted, current hot topics are discussed, and avenues for future research are suggested.

A Survey on the Criteria Used to Judge (Fake) News in Italian Population

INTRODUCTION

Fake news detection falls within the field of deception detection and, consequently, can be problematic due to no consensus on which cues increase the detection accuracy and because people's ability to detect is poor.

METHODS

The present study aimed to investigate the criteria used by general population to establish if a given news item is true or fake by surveying a sample of the Italian population. We recruited 329 participants who had to reply to some questions on which criteria they used to conclude a given news item was true. The same questions were also asked to investigate the ones used for fake news judgments.

RESULTS AND CONCLUSION

Our results showed that, overall, people use similar criteria (e.g., reliability of the source and presence of scientific references) to conclude that news is true versus fake, although their use rates differ for true and fake news.

Large-scale analysis of fact-checked stories on Twitter reveals graded effects of ambiguity and falsehood on information reappearance

Misinformation disrupts our information ecosystem, adversely affecting individuals and straining social cohesion and democracy. Understanding what causes online (mis)information to (re)appear is crucial for fortifying our information ecosystem. We analyzed a large-scale Twitter (now "X") dataset of about 2 million tweets across 123 fact-checked stories. Previous research suggested a falsehood effect (false information reappears more frequently) and an ambiguity effect (ambiguous information reappears more frequently). However, robust indicators for their existence remain elusive. Using polynomial statistical modeling, we compared a falsehood model, an ambiguity model, and a dual effect model. The data supported the dual effect model ( 13.76 times as likely as a null model), indicating both ambiguity and falsehood promote information reappearance. However, evidence for ambiguity was stronger: the ambiguity model was 6.6 times as likely as the falsehood model. Various control checks affirmed the ambiguity effect, while the falsehood effect was less stable. Nonetheless, the best-fitting model explained < 7 % of the variance, indicating that (i) the dynamics of online (mis)information are complex and (ii) falsehood effects may play a smaller role than previous research has suggested. These findings underscore the importance of understanding the dynamics of online (mis)information, though our focus on fact-checked stories may limit the generalizability to the full spectrum of information shared online. Even so, our results can inform policymakers, journalists, social media platforms, and the public in building a more resilient information environment, while also opening new avenues for research, including source credibility, cross-platform applicability, and psychological factors.

The Society of Information and the European Citizens' Perception of Climate Change: Natural or Anthropological Causes

The scientific community has reached a consensus on humans' important role as causative agents of climate change; however, branches of society are still sceptical about this. Climate change is a key issue for humanity and only the commitment to change human attitudes and lifestyles, at the global level, can be effective in its mitigation. With this purpose, it is important to convey the right message and prevent misinformation to manipulate people's minds. The present study aims to understand the factors shaping European citizens' thoughts on the causes of climate change. Using data from the European Social Survey 10 collected in 2022, we fitted statistical models using the people's thoughts on causes of climate change (natural, anthropogenic or both) as dependent variables. As independent variables, we used the impact of the media through time spent on news and time spent on the internet, level of education, level of trust in scientists, awareness of online or mobile misinformation and gender. We concluded that the typical European citizen who believes in anthropogenic causes of climate change is a female, is more literate, trusts more in scientists, is younger, spends more time reading the news and has more awareness of misinformation presence in online and mobile communications.

An overview of fake news detection: From a new perspective

With the rapid development and popularization of Internet technology, the propagation and diffusion of information become much easier and faster. While making life more convenient, the Internet also promotes the wide spread of fake news, which will have a great negative impact on countries, societies, and individuals. Therefore, a lot of research efforts have been made to combat fake news. Fake news detection is typically a classification problem aiming at verifying the veracity of news contents, which may include texts, images and videos. This article provides a comprehensive survey of fake news detection. We first summarize three intrinsic characteristics of fake news by analyzing its entire diffusion process, namely intentional creation, heteromorphic transmission, and controversial reception. The first refers to why users publish fake news, the second denotes how fake news propagates and distributes, and the last means what viewpoints different users may hold for fake news. We then discuss existing fake news detection approaches according to these characteristics. Thus, this review will enable readers to better understand this field from a new perspective. We finally discuss the trends of technological advances in this field and also outline some potential directions for future research.

Novel approach for Arabic fake news classification using embedding from large language features with CNN-LSTM ensemble model and explainable AI

The widespread fake news challenges the management of low-quality information, making effective detection strategies necessary. This study addresses this critical issue by advancing fake news detection in Arabic and overcoming limitations in existing approaches. Deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), EfficientNetB4, Inception, Xception, ResNet, ConvLSTM and a novel voting ensemble framework combining CNN and LSTM are employed for text classification. The proposed framework integrates the ELMO word embedding technique having contextual representation capabilities, which is compared with GloVe, BERT, FastText and FastText subwords. Comprehensive experiments demonstrate that the proposed voting ensemble, combined with ELMo word embeddings, consistently outperforms previous approaches. It achieves an accuracy of 98.42%, precision of 98.54%, recall of 99.5%, and an F1 score of 98.93%, offering an efficient and highly effective solution for text classification tasks.The proposed framework benchmark against state-of-the-art transformer architectures, including BERT and RoBERTa, demonstrates competitive performance with significantly reduced inference time and enhanced interpretability accompanied by a 5-fold cross-validation technique. Furthermore, this research utilizes the LIME XAI technique to provide deeper insights into the contribution of each feature in predicting a specific target class. These findings show the proposed framework's effectiveness in dealing with the issues of detecting false news, particularly in Arabic text. By generating higher performance metrics and displaying comparable results, this work opens the way for more reliable and interpretable text classification solutions.

Real-time fake news detection in online social networks: FANDC Cloud-based system

Social networks have become a common way for people to communicate with each other and share ideas, thanks to their fast information-sharing features. But fake news spread on social networks can cause many negative consequences by affecting people's daily lives. However, the literature lacks online and real-time fake news detection systems. This study aims to fill this gap in the literature and to handle the fake news detection problem with a system called FANDC, based on cloud computing, to cope with fake news in seven different categories, and to solve the real-time fake news detection problems. The system was developed using the CRISP-DM methodology with a hybrid approach. BERT algorithm was used in the system running on the cloud to avoid possible cyber threats with the dataset created with approximately 99 million big data from COVID-19-TweetIDs GitHub repository. It was trained in two periods with 100% accuracy during the modeling phase in terms of training accuracy. Experimental results of the FANDC system performed the real-time detection of fake news at 99% accuracy. However, previous studies experimental level success rate in the literature, were around 90%. We hope that the developed system will greatly assist social network users in detecting fake news in real-time.

Leveraging Chatbots to Combat Health Misinformation for Older Adults: Participatory Design Study

BACKGROUND

Older adults, a population particularly susceptible to misinformation, may experience attempts at health-related scams or defrauding, and they may unknowingly spread misinformation. Previous research has investigated managing misinformation through media literacy education or supporting users by fact-checking information and cautioning for potential misinformation content, yet studies focusing on older adults are limited. Chatbots have the potential to educate and support older adults in misinformation management. However, many studies focusing on designing technology for older adults use the needs-based approach and consider aging as a deficit, leading to issues in technology adoption. Instead, we adopted the asset-based approach, inviting older adults to be active collaborators in envisioning how intelligent technologies can enhance their misinformation management practices.

OBJECTIVE

This study aims to understand how older adults may use chatbots' capabilities for misinformation management.

METHODS

We conducted 5 participatory design workshops with a total of 17 older adult participants to ideate ways in which chatbots can help them manage misinformation. The workshops included 3 stages: developing scenarios reflecting older adults' encounters with misinformation in their lives, understanding existing chatbot platforms, and envisioning how chatbots can help intervene in the scenarios from stage 1.

RESULTS

We found that issues with older adults' misinformation management arose more from interpersonal relationships than individuals' ability to detect misinformation in pieces of information. This finding underscored the importance of chatbots to act as mediators that facilitate communication and help resolve conflict. In addition, participants emphasized the importance of autonomy. They desired chatbots to teach them to navigate the information landscape and come to conclusions about misinformation on their own. Finally, we found that older adults' distrust in IT companies and governments' ability to regulate the IT industry affected their trust in chatbots. Thus, chatbot designers should consider using well-trusted sources and practicing transparency to increase older adults' trust in the chatbot-based tools. Overall, our results highlight the need for chatbot-based misinformation tools to go beyond fact checking.

CONCLUSIONS

This study provides insights for how chatbots can be designed as part of technological systems for misinformation management among older adults. Our study underscores the importance of inviting older adults to be active co-designers of chatbot-based interventions.

Federated Learning in the Detection of Fake News Using Deep Learning as a Basic Method

This article explores the possibilities for federated learning with a deep learning method as a basic approach to train detection models for fake news recognition. Federated learning is the key issue in this research because this kind of learning makes machine learning more secure by training models on decentralized data at decentralized places, for example, at different IoT edges. The data are not transformed between decentralized places, which means that personally identifiable data are not shared. This could increase the security of data from sensors in intelligent houses and medical devices or data from various resources in online spaces. Each station edge could train a model separately on data obtained from its sensors and on data extracted from different sources. Consequently, the models trained on local data on local clients are aggregated at the central ending point. We have designed three different architectures for deep learning as a basis for use within federated learning. The detection models were based on embeddings, CNNs (convolutional neural networks), and LSTM (long short-term memory). The best results were achieved using more LSTM layers (F1 = 0.92). On the other hand, all three architectures achieved similar results. We also analyzed results obtained using federated learning and without it. As a result of the analysis, it was found that the use of federated learning, in which data were decomposed and divided into smaller local datasets, does not significantly reduce the accuracy of the models.

Critical Gaps in Medical Research Reporting by Online News Media

BACKGROUND

The integrity of medical research reporting in online news publications is crucial for informed healthcare decisions and public health discourse. However, omissions, lack of transparency, and the rapid spread of misinformation on digital and social media platforms can lead to an incomplete or inaccurate understanding of research findings. This study aims to analyze the fidelity of online news in reporting medical research findings, focusing on conflicts of interest, study limitations, statistical data, and research conclusions.

METHODS

Fifty randomized controlled trials published in major medical journals and their corresponding news reports were evaluated for the inclusion of conflicts of interest, study limitations, and inferential statistics in the news reports. The alignment of conclusions was evaluated. A binomial test with a Bonferroni correction was used to assess the inclusion rate of these variables against a 90% threshold.

RESULTS

Conflicts of interest were reported in 10 (20%) of news reports, study limitations in 14 (28%), and inferential statistics in 19 (38%). These rates were significantly lower than the 90% threshold (p<0.001). Research conclusions aligned in 43 (86%) cases, which was not significantly different from 90% (p=0.230). Misaligned conclusions resulted from overstating claims.

CONCLUSION

Significant gaps exist in the reporting of critical contextual information in medical news articles. Adopting a structured reporting format could enhance the quality and transparency of medical research communication. Collaboration among journalists, news organizations, and medical researchers is crucial for establishing and promoting best practices, fostering informed public discourse, and better health outcomes.

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.

Chapter 11: Astrobiology Education, Engagement, and Resources

Although astrobiology is a relatively new field of science, the questions it seeks to answer (e.g., "What is life?" "What does life require?") have been investigated for millennia. In recent decades, formal programs dedicated specifically to the science of astrobiology have been organized at academic, governmental, and institutional scales. Constructing educational programs around this emerging science relies on input from broad expertise and backgrounds. Because of the interdisciplinary nature of this field, career pathways in astrobiology often begin in more specific fields such as astronomy, geology, or biology, and unlike many other sciences, typically involve substantial training outside one's primary discipline. The recent origin of astrobiology as a field of science has led to strong collaborations with education research in the development of astrobiology courses and offers a unique instructional laboratory for further pedagogical studies. This chapter is intended to support students, educators, and early career scientists by connecting them to materials and opportunities that the authors and colleagues have found advantageous. Annotated lists of relevant programs and resources are included as a series of appendices in the supplementary material.

An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

BACKGROUND

Amidst the COVID-19 pandemic, misinformation on social media has posed significant threats to public health. Detecting and predicting the spread of misinformation are crucial for mitigating its adverse effects. However, prevailing frameworks for these tasks have predominantly focused on post-level signals of misinformation, neglecting features of the broader information environment where misinformation originates and proliferates.

OBJECTIVE

This study aims to create a novel framework that integrates the uncertainty of the information environment into misinformation features, with the goal of enhancing the model's accuracy in tasks such as misinformation detection and predicting the scale of dissemination. The objective is to provide better support for online governance efforts during health crises.

METHODS

In this study, we embraced uncertainty features within the information environment and introduced a novel Environmental Uncertainty Perception (EUP) framework for the detection of misinformation and the prediction of its spread on social media. The framework encompasses uncertainty at 4 scales of the information environment: physical environment, macro-media environment, micro-communicative environment, and message framing. We assessed the effectiveness of the EUP using real-world COVID-19 misinformation data sets.

RESULTS

The experimental results demonstrated that the EUP alone achieved notably good performance, with detection accuracy at 0.753 and prediction accuracy at 0.71. These results were comparable to state-of-the-art baseline models such as bidirectional long short-term memory (BiLSTM; detection accuracy 0.733 and prediction accuracy 0.707) and bidirectional encoder representations from transformers (BERT; detection accuracy 0.755 and prediction accuracy 0.728). Additionally, when the baseline models collaborated with the EUP, they exhibited improved accuracy by an average of 1.98% for the misinformation detection and 2.4% for spread-prediction tasks. On unbalanced data sets, the EUP yielded relative improvements of 21.5% and 5.7% in macro-F1-score and area under the curve, respectively.

CONCLUSIONS

This study makes a significant contribution to the literature by recognizing uncertainty features within information environments as a crucial factor for improving misinformation detection and spread-prediction algorithms during the pandemic. The research elaborates on the complexities of uncertain information environments for misinformation across 4 distinct scales, including the physical environment, macro-media environment, micro-communicative environment, and message framing. The findings underscore the effectiveness of incorporating uncertainty into misinformation detection and spread prediction, providing an interdisciplinary and easily implementable framework for the field.

Linguistic indicators for predicting the veracity of online health rumors

This study aims to examine the role of language in discerning the authenticity of online health rumors. To achieve this goal, it specifically focuses on analyzing five categories of linguistic indicators: (1) emotional language characterized by sentiment words, sensory words, and continuous punctuations, (2) exaggerated language defined by the presence of extreme numbers and extreme adverbs, (3) personalized language denoted by first-person pronouns, (4) unprofessional language represented by typographical errors, and (5) linkage language marked by inclusion of hyperlinks. To conduct the investigation, a dataset consisting of 1,500 information items was utilized. The dataset exhibited a distribution pattern wherein 20% of the information was verified to be true, while the remaining 80% was categorized as rumors. These items were sourced from two prominent rumor-clarification websites in China. A binomial logistic regression was used for data analysis to determine whether the language used in an online health rumor could predict its authenticity. The results of the analysis showed that the presence of sentiment words, continuous punctuation marks, extreme numbers and adverbs in an online health rumor could predict its authenticity. Personalized language, typographical errors, and hyperlinks were also found to be useful indicators for identifying health rumors using linguistic indicators. These results provide valuable insights for identifying health rumors using language-based features and could help individuals and organizations better understand the credibility of online health information.

The relevance of syntactic complexity for truth judgments: A registered report

Fluency theories predict higher truth judgments for easily processed statements. We investigated two factors relevant for processing fluency: repetition and syntactic complexity. In three online experiments, we manipulated syntactic complexity by creating simple and complex versions of trivia statements. Experiments 1 and 2 replicated the repetition-based truth effect. However, syntactic complexity did not affect truth judgments although complex statements were processed slower than simple statements. This null effect is surprising given that both studies had high statistical power and varied in the relative salience of syntactic complexity. Experiment 3 provides a preregistered test of the discounting explanation by using improved trivia statements of equal length and by manipulating the salience of complexity in a randomized design. As predicted by fluency theories, simple statements were more likely judged as true than complex ones, while this effect was small and not moderated by the salience of complexity.

Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

Conspiracy mentality, subclinical paranoia, and political conservatism are associated with perceived status threat

Status threat (i.e., concern that one's dominant social group will be undermined by outsiders) is a significant factor in current United States politics. While demographic factors such as race (e.g., Whiteness) and political affiliation (e.g., conservatism) tend to be associated with heightened levels of status threat, its psychological facets have yet to be fully characterized. Informed by a "paranoid" model of American politics, we explored a suite of possible psychological and demographic associates of perceived status threat, including race/ethnicity, political conservatism, analytic thinking, magical ideation, subclinical paranoia, and conspiracy mentality. In a small, quota sample drawn from the United States (N = 300), we found that conspiracy mentality, subclinical paranoia, conservatism, and age were each positively (and uniquely) associated with status threat. In addition to replicating past work linking conservatism to status threat, this study identifies subclinical paranoia and conspiracy mentality as novel psychological associates of status threat. These findings pave the way for future research regarding how and why status threat concerns may become exaggerated in certain individuals, possibly to the detriment of personal and societal wellbeing.

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

In the rapidly evolving information era, the dissemination of information has become swifter and more extensive. Fake news, in particular, spreads more rapidly and is produced at a lower cost compared to genuine news. While researchers have developed various methods for the automated detection of fake news, challenges such as the presence of multimodal information in news articles or insufficient multimodal data have hindered their detection efficacy. To address these challenges, we introduce a novel multimodal fusion model (TLFND) based on a three-level feature matching distance approach for fake news detection. TLFND comprises four core components: a two-level text feature extraction module, an image extraction and fusion module, a three-level feature matching score module, and a multimodal integrated recognition module. This model seamlessly combines two levels of text information (headline and body) and image data (multi-image fusion) within news articles. Notably, we introduce the Chebyshev distance metric for the first time to calculate matching scores among these three modalities. Additionally, we design an adaptive evolutionary algorithm for computing the loss functions of the four model components. Our comprehensive experiments on three real-world publicly available datasets validate the effectiveness of our proposed model, with remarkable improvements demonstrated across all four evaluation metrics for the PolitiFact, GossipCop, and Twitter datasets, resulting in an F1 score increase of 6.6%, 2.9%, and 2.3%, respectively.

Experience of Disease Acceptance in Chinese Patients with Newly Diagnosed Crohn's Disease: A Descriptive Qualitative Study

Background

High levels of disease acceptance are important predictors of improved psychological well-being, treatment outcomes, and enhanced quality of life. Relatively few studies have focused on the process of disease acceptance in patients with Crohn's disease (CD), particularly those who are newly diagnosed.

Purpose

To explore the disease acceptance process in newly diagnosed CD patients.

Patients and Methods

A descriptive qualitative approach was employed. Sixteen CD patients from 2 tertiary hospitals in Hangzhou, Zhejiang were recruited through purposive sampling using a maximum variation strategy. Semi-structured interviews were conducted. The interviews were transcribed verbatim and analysed using conventional content analysis.

Results

Five phases of the psychosocial process of the "acceptance journey" of newly diagnosed CD patients emerged from the data analysis: (1) praying for the illness to not be CD; (2) not being able to accept CD; (3) having to accept CD; (4) knowing that CD should be acceptable; and (5) starting to accept CD. Patients at the stage of "starting to accept CD" are more proactive and motivated to face the disease, and their overall acceptance of the disease is higher than that of the previous stages. However, by the end of the interview, 2 patients remained at the stage of "having to accept CD", and 3 patients remained at the stage of "knowing that CD should be acceptable". Two patients entered the stage of "starting to accept CD" and then reverted back to one of the previous stages.

Conclusion

The "acceptance journey" of newly diagnosed CD patients is dynamic, individual and reversible. Traditional Chinese cultural values such as respect for authority, the philosophy of wu-wei and family responsibility contribute to the acceptance of CD in Chinese patients. Hence, there is a need to provide early and culturally tailored psychological support or interventions according to the stages of acceptance.

Characterizing the semantic features of climate change misinformation on Chinese social media

Climate change misinformation leads to significant adverse impacts and has become a global concern. Identifying misinformation and investigating its characteristics are of great importance to counteract misinformation. Therefore, this study aims to characterize the semantic features (frames and authority references) of climate change misinformation in the context of Chinese social media. Posts concerning climate change were collected from Weibo between January 2010 and December 2020. First, veracity, frames, and authority references were manually labeled. Then, we applied logistic regression to examine the relationship between information veracity and semantic features. The results revealed that posts concerning environmental and health impact and science and technology were more likely to be misinformation. Moreover, posts referencing non-specific authority sources are more likely to be misinformed than posts making no references to any authority references. This study provides a theoretical understanding of the semantic characteristics of climate change misinformation and practical suggestions for combating them.

Burn Prevention in Spanish: Assessment of Content Accuracy, Website Quality, and Readability of Online Sources

Burn prevention information may be inadequate or inaccessible to communities with non-English language preference. Our objective was to systematically analyze the content accuracy, website quality, and readability of online Spanish information for burn prevention in the home and compare it to English websites. We collected the top ten burn prevention results from a search on Google, Bing, and Yahoo using a list of Spanish key terms. Using recommendations from national organizations and a burn care expert team, content accuracy was evaluated for each website. We assessed website quality following the "Health on the Net" Code of Conduct. Readability was scored by averaging five validated readability tests for the Spanish language. After using the same protocol, a comparison was made with English websites as a control. Once duplicates and non-relevant search results were removed, 23 Spanish websites were assessed. Out of 21 possible points for content accuracy, the top website scored 14 (67%) and the average score was 6.6 (31%). For website quality, the average score was 50%. The average grade level needed to read the websites was 8.6. Compared to English, Spanish websites were less accurate (31% vs 41%), harder to read (9.8 vs 7.8), but were of higher website quality (50% vs 43%). Online burn prevention information in Spanish is often inaccurate, incomplete, and inferior to available English language websites. We propose a call to action to increase the quality of online burn prevention material available in Spanish.

Caring in an Algorithmic World: Ethical Perspectives for Designers and Developers in Building AI Algorithms to Fight Fake News

This article suggests several design principles intended to assist in the development of ethical algorithms exemplified by the task of fighting fake news. Although numerous algorithmic solutions have been proposed, fake news still remains a wicked socio-technical problem that begs not only engineering but also ethical considerations. We suggest employing insights from ethics of care while maintaining its speculative stance to ask how algorithms and design processes would be different if they generated care and fight fake news. After reviewing the major characteristics of ethics of care and the phases of care, we offer four algorithmic design principles. The first principle highlights the need to develop a strategy to deal with fake news on the part of the software designers. The second principle calls for the involvement of various stakeholders in the design processes in order to increase the chances of successfully fighting fake news. The third principle suggests allowing end-users to report on fake news. Finally, the last principle proposes keeping the end-user updated on the treatment in the suspected news items. Implementing these principles as care practices can render the developmental process more ethically oriented as well as improve the ability to fight fake news.

Blockchain-based fake news traceability and verification mechanism

The rapid development of the Internet and Internet of Things has rapidly introduced human society into the information age, and the way of fake news production has been updated, which has greatly affected the normal life of human beings. In order to identify worthless fake news and trace massive fake news data from unknown sources, and share valuable news data to fully disseminate effective real news, news owners usually store news data in cloud. Users of IoT terminals can access news data on demand without storing it locally. However, the authenticity of the fictive newspaper numbers source, which is easy to destroy, and the social media platform. Besides, when massive news data is saved on cloud server, the news owners have to at the risk of lose physical control over news data and it will face the risk of fake news being disseminated and real news being falsified. Thus, this paper proposes a novel mechanism for secure storage of news data using blockchain technology. Firstly, traceability and verification of fake news data is improved by the cooperative storage model on and off the chain. Secondly due to the inability of past polynomial commitment to update the commitment, we will be a hindrance to use polynomial commitment to build a secure authentication protocol. Therefore, in this paper, we design the update algorithm for polynomial commitment in order to be able to guarantee the consistency of on-chain and blockchain database news data.

Combating Fake News and Digital Deception at the Workplace: An Integrative Review and Open Systems Theory-led Framework for Future Research

Research on fake news and related acts of deception in the domain of human resource management is growing but still in its infancy. This escalating crisis necessitates immediate attention, as fake news evolves into an all-pervasive phenomenon that surpasses domain boundaries and affects organizations at scale. This study analyzes the growing corpus of research on fake news and concomitant acts of deceit in the domain of human resource management through an integrative review of 64 scholarly papers published in peer-reviewed journals over the last 30 years. We identify key themes and draw attention to gaps that merit scrutiny. We then propose an open systems theory-led conceptual framework that elucidates the relationships between fake news, related acts of deceit and its effects on various facets of human resource management practice and serves as a guide to advance contributions in the field. Directions for future research and implications for practice are discussed.

Coordinated inauthentic behavior: An innovative manipulation tactic to amplify COVID-19 anti-vaccine communication outreach via social media

Coordinated inauthentic behavior (CIB) is a manipulative communication tactic that uses a mix of authentic, fake, and duplicated social media accounts to operate as an adversarial network (AN) across multiple social media platforms. The article aims to clarify how CIB's emerging communication tactic "secretly" exploits technology to massively harass, harm, or mislead the online debate around crucial issues for society, like the COVID-19 vaccination. CIB's manipulative operations could be one of the greatest threats to freedom of expression and democracy in our society. CIB campaigns mislead others by acting with pre-arranged exceptional similarity and "secret" operations. Previous theoretical frameworks failed to evaluate the role of CIB on vaccination attitudes and behavior. In light of recent international and interdisciplinary CIB research, this study critically analyzes the case of a COVID-19 anti-vaccine adversarial network removed from Meta at the end of 2021 for brigading. A violent and harmful attempt to tactically manipulate the COVID-19 vaccine debate in Italy, France, and Germany. The following focal issues are discussed: (1) CIB manipulative operations, (2) their extensions, and (3) challenges in CIB's identification. The article shows that CIB acts in three domains: (i) structuring inauthentic online communities, (ii) exploiting social media technology, and (iii) deceiving algorithms to extend communication outreach to unaware social media users, a matter of concern for the general audience of CIB-illiterates. Upcoming threats, open issues, and future research directions are discussed.

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation

Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (nlp) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (lda) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by nlp researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and rouge-l results endorse its potential as a valuable tool for busy investors. The rouge-l values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with lda to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors’ reputations.

A Scientometric Analysis of Deep Learning Approaches for Detecting Fake News

The unregulated proliferation of counterfeit news creation and dissemination that has been seen in recent years poses a constant threat to democracy. Fake news articles have the power to persuade individuals, leaving them perplexed. This scientometric study examined 569 documents from the Scopus database between 2012 and mid-2022 to look for general research trends, publication and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in publications since 2016, and this dissemination of fake news is still an issue from a global perspective. Thematic analysis of papers reveals that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped, while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute niche areas. Furthermore, the results show that China and the USA have the strongest international collaboration, despite India writing more articles. This paper also examines the current state of the art in deep learning techniques for fake news detection, with the goal of providing a potential roadmap for researchers interested in undertaking research in this field.

A hybrid intelligent model for early validation of infectious diseases: An explorative study of machine learning approaches

Literature reports several infectious diseases news validation approaches, but none is economically effective for collecting and classifying information on different infectious diseases. This work presents a hybrid machine-learning model that could predict the validity of the infectious disease's news spread on the media. The proposed hybrid machine learning (ML) model uses the Dynamic Classifier Selection (DCS) process to validate news. Several machine learning models, such as K-Neighbors-Neighbor (KNN), AdaBoost (AB), Decision Tree (DT), Random Forest (RF), SVC, Gaussian Naïve Base (GNB), and Logistic Regression (LR) are tested in the simulation process on benchmark dataset. The simulation employs three DCS process methods: overall Local Accuracy (OLA), Meta Dynamic ensemble selection (META-DES), and Bagging. From seven ML classifiers, the AdaBoost with Bagging DCS method got a 97.45% high accuracy rate for training samples and a 97.56% high accuracy rate for testing samples. The second high accuracy was obtained at 96.12% for training and 96.45% for testing samples from AdaBoost with the Meta-DES method. Overall, the AdaBoost with Bagging model obtained higher accuracy, AUC, sensitivity, and specificity rate with minimum FPR and FNR for validation.

Attention-Enriched Mini-BERT Fake News Analyzer Using the Arabic Language

Internet use resulted in people becoming more reliant on social media. Social media have become the main source of fake news or rumors. They spread uncertainty in each sector of the real world, whether in politics, sports, or celebrities’ lives—all are affected by the uncontrolled behavior of social media platforms. Intelligent methods used to control this fake news in various languages have already been much discussed and frequently proposed by researchers. However, Arabic grammar and language are a far more complex and crucial language to learn. Therefore, work on Arabic fake-news-based datasets and related studies is much needed to control the spread of fake news on social media and other Internet media. The current study uses a recently published dataset of Arabic fake news annotated by experts. Further, Arabic-language-based embeddings are given to machine learning (ML) classifiers, and the Arabic-language-based trained minibidirectional encoder representations from transformers (BERT) is used to obtain the sentiments of Arabic grammar and feed a deep learning (DL) classifier. The holdout validation schemes are applied to both ML classifiers and mini-BERT-based deep neural classifiers. The results show a consistent improvement in the performance of mini-BERT-based classifiers, which outperformed ML classifiers, by increasing the training data. A comparison with previous Arabic fake news detection studies is shown where results of the current study show greater improvement.

Detecting and classifying online health misinformation with 'Content Similarity Measure (CSM)' algorithm: an automated fact-checking-based approach

Information dissemination occurs through the 'word of media' in the digital world. Fraudulent and deceitful content, such as misinformation, has detrimental effects on people. An implicit fact-based automated fact-checking technique comprising information retrieval, natural language processing, and machine learning techniques assist in assessing the credibility of content and detecting misinformation. Previous studies focused on linguistic and textual features and similarity measures-based approaches. However, these studies need to gain knowledge of facts, and similarity measures are less accurate when dealing with sparse or zero data. To fill these gaps, we propose a 'Content Similarity Measure (CSM)' algorithm that can perform automated fact-checking of URLs in the healthcare domain. Authors have introduced a novel set of content similarity, domain-specific, and sentiment polarity score features to achieve journalistic fact-checking. An extensive analysis of the proposed algorithm compared with standard similarity measures and machine learning classifiers showed that the 'content similarity score' feature outperformed other features with an accuracy of 88.26%. In the algorithmic approach, CSM showed improved accuracy of 91.06% compared to the Jaccard similarity measure with 74.26% accuracy. Another observation is that the algorithmic approach outperformed the feature-based method. To check the robustness of the algorithms, authors have tested the model on three state-of-the-art datasets, viz. CoAID, FakeHealth, and ReCOVery. With the algorithmic approach, CSM showed the highest accuracy of 87.30%, 89.30%, 85.26%, and 88.83% on CoAID, ReCOVery, FakeHealth (Story), and FakeHealth (Release) datasets, respectively. With a feature-based approach, the proposed CSM showed the highest accuracy of 85.93%, 87.97%, 83.92%, and 86.80%, respectively.

A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection

The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures.

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.

Impact of fake news on firm performance during COVID-19: an assessment of moderated serial mediation using PLS-SEM

Purpose

Fake news on social media about COVID-19 pandemic and its associated issues (e.g. lockdown) caused public panic that lead to supply chain (SC) disruptions, which eventually affect firm performance. The purpose of this study is to understand how social media fake news effects firm performance, and how to mitigate such effects.

Design/methodology/approach

Grounded on dynamic capability view (DCV), this study suggests that social media fake news effects firm performance via SC disruption (SCD) and SC resilience (SCR). Moreover, the relation between SCD and SCR is contingent upon SC learning (SCL) – a moderated mediation effect. To validate this complex model, the authors suggest effectiveness of using partial least squares structural equation modeling (PLS-SEM). Using an online survey, the results support the authors’ hypotheses.

Findings

The results suggest that social media fake news does not affect firm performance directly. However, the authors’ serial mediation test confirms that SCD and SCR sequentially mediate the relationship between social media fake news and firm performance. In addition, a moderated serial mediation test confirms that a higher level of SCL strengthens the SCD–SCR relationship.

Research limitations/implications

This work offers a new theoretical and managerial perspective to understand the effect of fake news on firm performance, in the context of crises, e.g. COVID-19. In addition, this study offers the advancement of PLS as more robust for real-world applications and more advantageous when models are complex.

Originality/value

Prior studies in the SC and marketing domain suggest different effects of social media fake news on consumer behavior (e.g. panic buying) and SCD, respectively. This current study is a unique effort that investigates the ultimate effect of fake news on firm performance with complex causal relationships via SCD, SCR and SCL.

Automatic Detection of Fake News in Spanish: Ecuadorian Political Satire

La circulación de noticias falsas en internet, especialmente las de sátira política a través de redes sociales, ha afectado a la mayoría de la población ecuatoriana. Este trabajo presenta una metodología basada en el aprendizaje estadístico que detecta de forma precisa y automática noticias falsas en español utilizando técnicas de aprendizaje automático y procesamiento del lenguaje natural. El documento comienza presentando conceptos básicos relacionados con las noticias falsas y trabajos relacionados con su detección automática. La segunda sección explica el proceso de creación del corpus de noticias, procesamiento de los textos, representación numérica con TF-IDF y entrenamiento de algoritmos de clasificación supervisados con dos conjuntos de datos diferentes. Los resultados obtenidos del entrenamiento se analizan en la tercera sección, siendo los modelos con máquinas de soporte vectorial los que ofrecen mejores predicciones, mejorando aproximadamente un 15%, 6% y 3% al rendimiento de los modelos con naive bayes, random forests y árboles boosting respectivamente. Finalmente, las conclusiones de la investigación y el trabajo futuro se presentan en la cuarta sección.

Towards a soft three-level voting model (Soft T-LVM) for fake news detection

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

Mpox panic, infodemic, and stigmatization of the 2SLGBTQIAP+ community: geospatial analysis, topic modeling, and sentiment analysis of a large, multilingual social media database (Preprint)

BACKGROUND

The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community.

OBJECTIVE

The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)-related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination.

METHODS

In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets' main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets' and Facebook posts' languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries.

RESULTS

The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04).

CONCLUSIONS

The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies.

Adaptive cost-sensitive stance classification model for rumor detection in social networks

As online social networks are experiencing extreme popularity growth, determining the veracity of online statements denoted by rumors automatically as earliest as possible is essential to prevent the harmful effects of propagating misinformation. Early detection of rumors is facilitated by considering the wisdom of the crowd through analyzing different attitudes expressed toward a rumor (i.e., users’ stances). Stance detection is an imbalanced problem as the querying and denying stances against a given rumor are significantly less than supportive and commenting stances. However, the success of stance-based rumor detection significantly depends on the efficient detection of “query” and “deny” classes. The imbalance problem has led the previous stance classifier models to bias toward the majority classes and ignore the minority ones. Consequently, the stance and subsequently rumor classifiers have been faced with the problem of low performance. This paper proposes a novel adaptive cost-sensitive loss function for learning imbalanced stance data using deep neural networks, which improves the performance of stance classifiers in rare classes. The proposed loss function is a cost-sensitive form of cross-entropy loss. In contrast to most of the existing cost-sensitive deep neural network models, the utilized cost matrix is not manually set but adaptively tuned during the learning process. Hence, the contributions of the proposed method are both in the formulation of the loss function and the algorithm for calculating adaptive costs. The experimental results of applying the proposed algorithm to stance classification of real Twitter and Reddit data demonstrate its capability in detecting rare classes while improving the overall performance. The proposed method improves the mean F-score of rare classes by about 13% in RumorEval 2017 dataset and about 20% in RumorEval 2019 dataset.

A review on fake news detection 3T's: typology, time of detection, taxonomies

Fake news has become an industry on its own, where users paid to write fake news and create clickbait content to allure the audience. Apparently, the detection of fake news is a crucial problem and several studies have proposed machine-learning-based techniques to combat fake news. Existing surveys present the review of proposed solutions, while this survey presents several aspects that are required to be considered before designing an effective solution. To this aim, we provide a comprehensive overview of false news detection. The survey presents (1) a clarity to problem definition by explaining different types of false information (like fake news, rumor, clickbait, satire, and hoax) with real-life examples, (2) a list of actors involved in spreading false information, (3) actions taken by service providers, (4) a list of publicly available datasets for fake news in three different formats, i.e., texts, images, and videos, (5) a novel three-phase detection model based on the time of detection, (6) four different taxonomies to classify research based on new-fangled viewpoints in order to provide a succinct roadmap for future, and (7) key bibliometric indicators. In a nutshell, the survey focuses on three key aspects represented as the three T's: Typology of false information, Time of detection, and Taxonomies to classify research. Finally, by reviewing and summarizing several studies on fake news, we outline some potential research directions.

A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection

The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real news. Fake news is a significant social barrier that has a profoundly negative impact on society. Despite the large number of studies on fake news detection, they have not yet been combined to offer coherent insight on trends and advancements in this domain. Hence, the primary objective of this study was to fill this knowledge gap. The method for selecting the pertinent articles for extraction was created using the preferred reporting items for systematic reviews and meta-analyses (PRISMA). This study reviewed deep learning, machine learning, and ensemble-based fake news detection methods by a meta-analysis of 125 studies to aggregate their results quantitatively. The meta-analysis primarily focused on statistics and the quantitative analysis of data from numerous separate primary investigations to identify overall trends. The results of the meta-analysis were reported by the spatial distribution, the approaches adopted, the sample size, and the performance of methods in terms of accuracy. According to the statistics of between-study variance high heterogeneity was found with τ2 = 3.441; the ratio of true heterogeneity to total observed variation was I2 = 75.27% with the heterogeneity chi-square (Q) = 501.34, the degree of freedom = 124, and p ≤ 0.001. A p-value of 0.912 from the Egger statistical test confirmed the absence of a publication bias. The findings of the meta-analysis demonstrated satisfaction with the effectiveness of the recommended approaches from the primary studies on fake news detection that were included. Furthermore, the findings can inform researchers about various approaches they can use to detect online fake news.