Stability Analysis of a Delayed Rumor Propagation Model with Nonlinear Incidence Incorporating Impulsive Vaccination

The presence of information asymmetry can hinder the public's ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered a periodic impulsive intervention to mitigate the impact of information asymmetry and promote a more informed and discerning public. Drawing on these findings, this paper proposes a susceptible-hesitant-infected-refuting-recovered (SHIDR) rumor-spreading model to explain the spread of rumors. The model incorporates elements such as time delay, nonlinear incidence, and refuting individuals. Firstly, by applying the comparison theorem of an impulsive differential equation, we calculate two thresholds for rumor propagation. Additionally, we analyze the conditions of global attractiveness of the rumor-free periodic solution. Furthermore, we consider the condition for the rumor's permanence. Finally, numerical simulations are conducted to validate the accuracy of our findings. The results suggest that increasing the proportion of impulsive vaccination, reducing the impulsive period, or prolonging the delay time can effectively suppress rumors.

DGTR: Dynamic graph transformer for rumor detection

Social media rumors have the capacity to harm the public perception and the social progress. The news propagation pattern is a key clue for detecting rumors. Existing propagation-based rumor detection methods represent propagation patterns as a static graph structure. They simply consider the structure information of news distribution in social networks and disregard the temporal information. The dynamic graph is an effective modeling tool for both the structural and temporal information involved in the process of news dissemination. Existing dynamic graph representation learning approaches struggle to capture the long-range dependence of the structure and temporal sequence as well as the rich semantic association between full graph features and individual parts. We build a transformer-based dynamic graph representation learning approach for rumor identification DGTR to address the aforementioned challenges. We design a position embedding format for the graph data such that the original transformer model can be utilized for learning dynamic graph representations. The model can describe the structural long-range reliance between the dynamic graph nodes and the temporal long-range dependence between the temporal snapshots by employing a self-attention mechanism. In addition, the CLS token in transformer may model the rich semantic relationships between the complete graph and each subpart. Extensive experiments demonstrate the superiority of our model when compared to the state of the art.

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

BACKGROUND

In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020.

OBJECTIVE

The aim of this study was to examine the propagation patterns and textual transformation of COVID-19-related rumors on a closed social media platform.

METHODS

We obtained a data set of suspicious text messages collected on Taiwan's most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a "rumor group." Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor.

RESULTS

A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19-related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network-certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19-related messages, compared to non-COVID-19-related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations.

CONCLUSIONS

To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas.

Epidemic spreading in complex networks

The study of epidemic spreading in complex networks is currently a hot topic and a large body of results have been achieved. In this paper, we briefly review our contributions to this field, which includes the underlying mechanism of rumor propagation, the epidemic spreading in community networks, the influence of varying topology, and the influence of mobility of agents. Also, some future directions are pointed out.