A cross platform framework for social media information diffusion analysis

Abstract

In the current digital era, social media platforms have emerged as one of the most effec- tive channels for the diffusion of information. People may readily access and exchange information, news, and opinions from anywhere worldwide because of increasing social media usage. Information diffusion across multiplex social media platforms is one of the most prominent research problems ever. Social media content generators diffuse information on multiplex social media platforms by targeting many objectives such as popularity, online presence, hate targets, and customer engagement. Regardless of the ”content” posted on social media platforms, evaluating the dissemination velocity of each piece of content published on those platforms is essential. It will help to get an overall picture of ”how it flows” throughout the social media platforms. Most social media platforms have a platform-specific algorithm for calculating the degree of information diffusion on those platforms. The main objective of this research was to develop a method to calculate the velocity of information diffusion across multiplex social media platforms. Existing literature on information diffusion strategies, effects, and measurements was used to develop the proposed algorithm. The information diffusion velocity of so- cial media influencers varies according to the content. The platform-specific algorithms for diffusion strength detection vary based on the platform. Somehow, these platform- specific algorithms influence the community to engage with the trending content. i.e., platforms support increasing the strength of information diffusion. Conventional information diffusion algorithms were designed to measure content diffusion speed on a simplex social media platform, which might be content-specific. The missing dimension is ubiquitous nature. Hence, regardless of the platform, it is mandatory to calculate a ubiquitous information diffusion velocity over multiplex social media platforms. Both structured information diffusion in a graph for diffusion in a closed network and unstructured patterns in an open-ended coarse-grained information diffusion model check the importance of information diffusion on multiplex social media platforms. Time is another critical factor in defining velocity. i.e., a time series of information diffusion provides a rich picture of information diffusion. Event-driven architecture is a well-known software architectural approach that fa- cilitates the implementation of microservice-based solutions. The suggested algorithm utilizes an event-driven architecture to manage the information flow by processing social media events. Eventually, this research uses the event-triggering process to understand how information is propagated through an event-driven microservice architecture. Data science and artificial intelligence are being employed in information diffusion studies. Understanding how information spreads and the variables and features that influence it is another crucial study area of this research. There are several techniques for studying information dissemination using artificial intelligence. Applying artificial intelligence to information diffusion studies might improve our knowledge of ”How information travels” and ”how to disseminate information” in various circumstances efficiently. The research used natural language processing to evaluate the textual content of the social media post. That is to find a general textual meaning given by the end-user reactions. Event-driven architecture is one of the best possible for information diffusion an- alytics. Using event-driven architecture, data may be delivered in real-time to vari- ous analytics services, allowing for the speedy and effective processing of enormous amounts of data. This is especially true in today’s data-driven world, when businesses and organizations must make quick, well-informed decisions based on real-time data. Because of its event-driven nature, it is also simple to interface with other systems and services, making it a highly adaptable and versatile option for information distribution analytics. Since the diffusion of information starts with an event’s occurrence, it fol- lows numerous steps to flow among the community. An event-driven micro-services architecture that uses artificial intelligence methods (like natural language processing to evaluate textual information) has been experimented with to propose a simple solution for this complex problem. As per the research work, I can summarize the key findings. I have proposed a tree-structured diffusion tree that can explain how information flows through multiplex social networks. Under this multiplex context, I have experimented with multiple trees and a more robust graph that focused on the diffusion of information. The diffusion strength was based on the SIR model, and the time series analysis focused on how quickly information spread throughout the network. The proposed solution was tested in several real-world cases. Technique-specific tests like seasonality and autocorrelation were conducted to evaluate how the time-series model works in a graph context. Further tests like cohesiveness and robustness were tested, and the proposed algorithm achieved good robustness (an average of 75%) and cohesiveness (an average of 70%) in each case. The best experimental results show an average of more than 80% accuracy in any given instance, and it constructs the tree in less than a second. Most of the predicted values generated an average accuracy of around 70%. In summary, social media platforms have emerged as prominent channels for in- formation propagation within the contemporary digital landscape. Quantifying the velocity at which information propagates across diverse social networks presents a no- table challenge in research. While algorithms tailored to specific platforms influence community engagement, a ”universal metric for information dissemination strength” is necessary across multiple social media platforms. The envisioned algorithm considers time series data, integrating structured and unstructured patterns during construction. Keywords: Information diffusion analysis, Social Media Data Analytics, Graph Learning, Time series analysis, Event-driven micro-services, Artificial Intelligence, Natural Language Processing.