In the era of Industry 5.0, data - driven decision - making has become indispensable for optimizing systems across Industrial Engineering. This paper addresses the value of big data analytics by proposing a novel non - linear hybrid approach for forecasting international tourist arrivals in two different contexts: (i) arrivals to Hong Kong from five major source nations (pre - COVID - 19), and (ii) arrivals t o Sanya in Hainan province, China (post - COVID - 19). The method integrates multiple sources of Internet big data and employs an innovative game theory - based algorithm to identify opinion leaders on social media platforms. Subsequently, nonstationary attribut es in tourism demand data are managed through Empirical Wavelet Transform (EWT), ensuring refined time - frequency analysis. Finally, a memory - aware Stacked Bi - directional Long Short - Term Memory (Stacked BiLSTM) network is used to generate accurate demand fo recasts. Experimental results demonstrate that this approach outperforms existing state - of - the - art techniques and remains robust under dynamic and volatile conditions, highlighting its applicability to broader Industrial Engineering domains -- such as logisti cs, supply chain management, and production planning -- where forecasting and resource allocation are key challenges. By merging advanced Deep Learning (DL), time - frequency analysis, and social media insights, the proposed framework showcases how large - scale data can elevate the quality and efficiency of decision - making processes.

Predicting influencers' views and public sentiment on social media is crucial for anticipating societal trends and guiding strategic responses. This study introduces a novel computational framework to predict opinion leaders' perspectives and the emotive reactions of the populace, addressing the inherent challenges posed by the unstructured, context-sensitive, and heterogeneous nature of online communication. Our research introduces an innovative module that starts with the automatic 5W1H (Where, Who, When, What, Why, and How) questions formulation engine, tailored to emerging news stories and trending topics. We then build a total of 60 anonymous opinion leader agents in six domains and realize the views generation based on an enhanced large language model (LLM) coupled with retrieval-augmented generation (RAG). Subsequently, we synthesize the potential views of opinion leaders and predicted the emotional responses to different events. The efficacy of our automated 5W1H module is corroborated by an average GPT-4 score of 8.83/10, indicative of high fidelity. The influencer agents exhibit a consistent performance, achieving an average GPT-4 rating of 6.85/10 across evaluative metrics. Utilizing the 'Russia-Ukraine War' as a case study, our methodology accurately foresees key influencers' perspectives and aligns emotional predictions with real-world sentiment trends in various domains.

Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning.

Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.

An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.

The understanding of how users in a network update their opinions based on their neighbours opinions has attracted a great deal of interest in the field of network science, and a growing body of literature recognises the significance of this issue. In this research paper, we propose a new dynamic model of opinion formation in directed networks. In this model, the opinion of each node is updated as the weighted average of its neighbours opinions, where the weights represent social influence. We define a new centrality measure as a social influence metric based on both influence and conformity. We measure this new approach using two opinion formation models: (i) the Degroot model and (ii) our own proposed model. Previously published research studies have not considered conformity, and have only considered the influence of the nodes when computing the social influence. In our definition, nodes with low in-degree and high out-degree that were connected to nodes with high out-degree and low in-degree had higher centrality. As the main contribution of this research, we propose an algorithm for finding a small subset of nodes in a social network that can have a significant impact on the opinions of other nodes. Experiments on real-world data demonstrate that the proposed algorithm significantly outperforms previously published state-of-the-art methods.

Indonesian Ministry of Finance had endorsed 10 Key Opinion Leaders to help promoting government retail bonds SBR011 during selling period of 25 May-16 June 2022. This study analyzed effectiveness of the endorsement by using several indicators; engagement rate, enthusiasm rate and sentiment analysis of feedbacks from KOL audiens. Data was gathered from social media Instagram and TikTok social platform used by the KOL to post their marketing contents. This paper found that the endorsement is quite effective to promote the SBR011 and yields mostly positive feedback on the marketing campaign. Definisi Key Opinion Leader (KOL) Menurut influencermarketinghub, KOL dideskripsikan sebagai "person or organization who has expert product knowledge and influence in a respective field. They are trusted by relevant interest groups and have significant effects on consumer behavior" [7].

The diffusion of innovations is an important topic for the consumer markets. Early research focused on how innovations spread on the level of the whole society. To get closer to the real world scenarios agent based models (ABM) started focusing on individual-level agents. In our work we will translate an existing ABM that investigates the role of opinion leaders in the process of diffusion of innovations to a new, more expressive platform designed for agent based modeling, GAMA. We will do it to show that taking advantage of new features of the chosen platform should be encouraged when making models in the field of social sciences in the future, because it can be beneficial for the explanatory power of simulation results.

The phrase "opinion leader" became popular after Paul Lazarsfeld and Elihu Katz wrote about it in their 1955 book Personal Influence: The Part Played by People in the Flow of Mass Communications. In their book, the authors describe opinion leaders as influential people who further circulate media messages through face-to-face communication within their own personal networks. In today's pharmaceutical landscape, KOLs (Key Opinion Leaders) are primarily valued for their level of influence. Pharma companies leverage the influence of expert physicians and researchers in order to recruit patients for clinical trials and influence the adoption of drugs at regional, national, and global scales. KOLs are so important that they cost pharma companies just under one-third of their total marketing expenditures.

Psychological theories suggest that emotion represents the state of mind and instinctive responses of one’s cognitive system (Cannon 1927). Emotions are a complex state of feeling that results in physical and psychological changes that influence our behavior. In this paper, we study an interesting problem of emotion contagion in social networks. In particular, by employing an image social network (Flickr) as the basis of our study, we try to unveil how users’ emotional statuses influence each other and how users’ positions in the social network affect their influential strength on emotion. We develop a probabilistic framework to formalize the problem into a role-aware contagion model. The model is able to predict users’ emotional statuses based on their historical emotional statuses and social structures. Experiments on a large Flickr dataset show that the proposed model significantly outperforms (+31% in terms of F1-score) several alternative methods in predicting users’ emotional status. We also discover several intriguing phenomena. For example, the probability that a user feels happy is roughly linear to the number of friends who are also happy; but taking a closer look, the happiness probability is superlinear to the number of happy friends who act as opinion leaders (Page et al. 1999) in the network and sublinear in the number of happy friends who span structural holes (Burt 2001). This offers a new opportunity to understand the underlying mechanism of emotional contagion in online social networks.