The proliferation of fake news on social media has the probability to bring an unfavorable impact on public opinion and social development. Many efforts have been paid to develop effective detection and intervention algorithms in recent years. Most of the existing propagation-based fake news detection methods focus on static networks and assume the whole information propagation network structure is accessible before performing learning algorithms. However, in real-world information diffusion networks, new nodes and edges constantly emerge. Therefore, in this paper, we introduce a novel temporal propagation-based fake news detection framework, which could fuse structure, content semantics, and temporal information. In particular, our model can model temporal evolution patterns of real-world news as the graph evolving under the setting of continuous-time dynamic diffusion networks. We conduct extensive experiments on large-scale real-world datasets and the experimental results demonstrate that our proposed model outperforms state-of-the-art fake news detection methods.

Temporally evolving graph neural network for fake news detection

• A new MCGDM model combined with a bidirectional influence relationship is proposed. • The decision-making process takes different psychological attitudes into account. • The IBCSMDM method is developed to accommodate emergency environments. • An extended IT2FS way is implemented for reflecting the uncertainty in assessments. The COVID-19 pandemic outbreak spread rapidly worldwide, posing a severe threat to human life. Due to its unpredictability and destructiveness, the emergency has aroused great common in society. At the same time, the selection of emergency medical supplier is one of the critical links in emergency decision-making, so undertaking appropriate decision-making using scientific tools becomes the primary challenge when an emergency outbreak occurs. The multi criteria group decision-making (MCGDM) method is an applicable and common method for choosing supplier. Nevertheless, because emergency medical supplier selection should consider regarding many aspects, it is difficult for decision makers (DMs) to develop a comprehensive assessment method for emergency medical supplier. Therefore, few academics have focused on emergency situation research by the MCGDM method, and the existing MCGDM method has some areas for improvement. In view of this situation, in this study, we propose a new MCGDM method, which considers the bidirectional influence relation of the criteria, consensus and the psychological factors of DMs. It providers a good aid in emergency decision-making and it could apply to other types of MCGDM research. Firstly, DMs give their assessment in interval type-2 fuzzy sets (IT2FSs). Secondly, an extended IT2FSs assessment method and a novel ISM-BWM-Cosine Similarity-Max Deviation Method (IBCSMDM) are used for weighing all alternatives. The TODIM (an acronym for interactive and multi-criteria decision-making in Portuguese) can obtain the ranking results under different risk attenuation factors. Eventually, this extended IT2FSs-IBCSMDM-TODIM method is applied in a real case in Wuhan in the context of COVID-19 to illustrate the practicability and usefulness. 

https://doi.org/10.1016/j.eswa.2022.117414

An extended multi-criteria group decision-making method with psychological factors and bidirectional influence relation for emergency medical supplier selection

In recent years, there has been a rise in incorrect information, or misinformation, being shared on social media. Such misinformation tends to be more eye-catching and misleading. Previous studies on detecting misinformation online have focused mainly on linguistic characteristics; however, the role of linguistic characteristics in misinformation dissemination has not yet been thoroughly researched. In this study, we propose a misinformation dissemination model that includes the direct effects of four novel linguistic characteristics on dissemination and the moderating effect of information richness. The model is tested by using 9,631 examples of misinformation collected from Sina-Weibo, the leading social media platform in China. The results indicate that compared with correct information, misinformation containing persuasive and uncertainty words is more likely to be disseminated than misinformation containing emotional and comparative words. Furthermore, in the case of information richness, the results indicate that when the misinformation includes images, the effects of persuasive, negative emotion, comparative, and uncertainty words are strengthened, while the inclusion of videos weakens the effects of the linguistic characteristics. Finally, the results of robustness check by using the Fakenewsnet data set are consistent with our hypothesis, indicating that the four linguistic characteristics proposed by this study are also suitable for the dissemination of misinformation in English. The robustness check further demonstrates our method with well generalization.

Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness

Abstract One of today’s most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence ( N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy. 

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A systematic review of worldwide causal and correlational evidence on digital media and democracy

https://doi.org/10.1016/j.dss.2022.113752

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

The dissemination of misinformation in health emergencies poses serious threats to public health and increases health anxiety. To understand the underlying mechanism of the dissemination of misinformation regarding health emergencies, this study creatively draws on social support theory and text mining. It also explores the roles of different types of misinformation, including health advice and caution misinformation and health help-seeking misinformation, and emotional support in affecting individuals’ misinformation dissemination behavior on social media and whether such relationships are contingent on misinformation ambiguity and richness. The theoretical model is tested using 12,101 textual data about COVID-19 collected from Sina Weibo, a leading social media platform in China. The empirical results show that health caution and advice, help seeking misinformation, and emotional support significantly increase the dissemination of misinformation. Furthermore, when the level of ambiguity and richness regarding misinformation is high, the effect of health caution and advice misinformation is strengthened, whereas the effect of health help-seeking misinformation and emotional support is weakened, indicating both dark and bright misinformation ambiguity and richness. This study contributes to the literature on misinformation dissemination behavior on social media during health emergencies and social support theory and provides implications for practice.

Haoxin Xiu

Papers

Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19