Yaya Hamadou Koura

In public opinion events, social media is often used as an important channel for the public to collect, process, and share information, and the topics and information on social media stimulates people to behave in creating related contents. This study integrates the elaboration likelihood model (ELM) to examine how information topics (central cue) and emotional responses (peripheral cue) shape user information behavior (likes, comments, forwards), and how user credibility moderates these effects. 56,663 Weibo posts (August 22 to September 5, 2023) about “Japan’s announcement of nuclear contaminated water discharge” were used for empirical analysis. Topics were identified via Latent Dirichlet Allocation (LDA), yielding four categories: Japanese aquatic products trade (Topic 1, 1.850%), seawater blackening (Topic 2, 8.822%), radioactivity exceedance (Topic 3, 13.286%), and global impact (Topic 4, 76.043%). Seven emotions were scored using the Dalian University of Technology affective lexicon. User credibility was coded from verification type. Baseline regression controlling for richness, mention, hashtags, URL, and text length showed that information topics significantly increased likes (β range: 0.164 to 0.333, p < 0.001), comments (β range: 0.063 to 0.138, p < 0.001), and forwards (β range: 0.083 to 0.156, p < 0.001), with the largest effects on likes. For emotions, anger, disgust, good and surprise positively influenced information behaviors (β range: 0.004 to 0.060, p < 0.01 to p < 0.001), while sadness, happy and fear showed negative or non‑significant effects. User credibility positively moderated all these relationships, with interaction β values ranging from 0.010 to 0.066 for topics and from 0.006 to 0.040 for emotions. Likes were the most influenced behavior, followed by forwards, then comments. The study demonstrates that information topics and emotions, moderated by user credibility, drive distinct information behaviors. Authorities should leverage high‑credibility users, highlight impactful topics, and monitor emotional tones, especially negative ones, to guide public opinion effectively.

Purpose: Immunization programs depend on high vaccination coverage to effectively lower the incidence and prevalence of specific infectious diseases. This study examines the relationship between public and media attention to vaccines and vaccination coverage during COVID-19 in China, drawing on behavioral agenda-setting theory and considering the influence of online health information on real-world health behaviors. Design/methodology/approach: We collected daily data on COVID-19 vaccine doses administered in China from April 13, 2021, to September 19, 2022, and quantified public and media attention using the Baidu Search Index and Baidu Media Index over the same period. Time series analysis, logarithmic fitting, and Granger causality tests were conducted to analyze relationships and temporal dynamics among the datasets. Findings: Public attention to vaccination spurred vaccination-related media coverage and contributed to increased vaccine uptake. Notably, the decay tail of the distribution function for vaccination doses was longer than for public and media attention, revealing a long-term “echo effect” of online information exposure on vaccination behavior. Granger causality test revealed that the media attention did not Granger cause the public attention, suggesting a reconfiguration of the classic agenda-setting dynamic between the public and media. Originality/value: This study provides evidence-based insights into the temporal dynamics between health information acquisition and health behaviors, while also revealing a reverse agenda-setting relationship between media and public attention in the health domain.

Reducing fake news and rumor propagation through social media may be challenging to achieve when dealing with sensible contents and communities with free access to online shared resources. Controlling rumor dissemination and promoting true news are the main techniques used to strangle false information that may result in dramatic effect on human wellbeing in an open or closed environment. In this article, we studied a predator-prey model with constant delay in both predator and prey equations and applied the proposed model to the underlying relationship between the existing rumor propagating through social media and the related authoritative information containing the truth broadcast to reduce the respective rumor negative effect on the targeted community. We showed that the proposed system was very responsive to small perturbations and exhibited complex dynamical behavior around the steady-state equilibrium when interaction occurs and delay is applied, considering the controlled situations. Numerical results suggested applying relatively small delay, which represents the ideal time to publish the related propagating rumor curative content to reduce its diffusion speed and promote the truth.

As a major source of pollution and the cause of climate change, greenhouse gas emissions are attracting the attention of scholars, policymakers, and governors in Europe and the world. This article assesses the impact of population, energy taxes, and energy prices on greenhouse gas emissions from the residential and industrial energy consumption in Europe. The paper establishes a theoretical framework that predicts that rising energy prices and increased energy taxes will reduce residential and industrial GHG emissions. According to this framework, it is expected that the labor force will have an impact on industrial greenhouse gas emissions depending on wages elasticity. Between 2007 and 2017, panel data from 21 European countries were used to test the proposed hypothesis. First, a complete sample test was conducted. The results confirmed the proposed hypothesis. In addition, it was found that the size of the population increased residential greenhouse gas emissions, while the urbanization process reduced these emissions. Next, the sample was divided according to the economic development level. The split sample analysis shows the regional heterogeneity of population factors and energy costs impacts on GHG emissions. Finally, the time-varying coefficient test indicates that during the study period, the negative impact of urbanization has decreased over time, while the positive impact of industrial production on greenhouse gas emissions has increased. We believe this article will contribute to the formulation of environmental policies and provide additional insights for environmentally sustainable development

When network users are intensively interacting during rush hour, avoiding data loss and latency is a concern in guaranteeing segment reliability. Implementing a leaky bucket could be needed to achieve flows effective monitoring by generating network of queues at given output link. Packets associated with different sessions and originated from different hosts may be mixed up and queuing delay may become longer, depending on network segment state, buffering strategy, users’ behavior among other factors. It is interesting to assimilate these stages of packets traveling through network segment to the concept of epidemic control. This paper proposes a SIR (Susceptible–Infected–Recovered) approach in modeling data packets transmission at a leaky bucket at peak hour. We focused our analysis on packets buffering and recovery strategy impact on segment forwarding performance in heavy load situation. Numerical results suggested adapting buffering strategy and packets recovery to enhance transmission and network overall performance.

Controlling information diffusion or propagation through social networks can be challenging when dealing with information related to a subject of highest interest for the public. The complexity level of control depends on subject importance, users’ dynamic, and network structure. When two published messages or pieces of information share the same interest for targeted readers, analyzing their propagation dynamic for control and prediction is of great interest. This article proposes to model, based on a modified interactive system with Holling type functional response, the dynamic of underlying relationship between two broadcasted messages traveling through social networks media. We showed in the qualitative analysis of the proposed model that system could be stable at certain conditions, and the model-system exhibited very rich dynamical behavior. Numerical simulation results validated theoretical analyses and suggested adapting resources harvesting and assimilation efficiency for an authoritative message to stabilize the system and control the dissemination of information in a closed environment.

Rumours spreading in nowadays highly connected society is an interesting and important topic in studying human behaviour and communication networks dynamics. As rumours propagation or evolution with respect to time is highly submitted to networks users’ dynamics, rumours propagation in a targeted community may inherit self-purification mechanism due to human ability to judge and clarify allegations and assumptions not based on truth. In this article, we analysed rumours spreading through social media taking into account self-purification mechanism using epidemic control approach. Rumours spreading threshold value was obtained. Results of numerical simulation validated theoretical analysis and suggested controlling social media influence on potential rumours purifiers and disseminators.