SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA

Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts. The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification

Connectivity Contribution to Urban Hub Network Based on Super Network Theory – Case Study of Beijing

With the rapid development of urbanization in China, the number of travel modes and urban passenger transportation hubs has been increasing, gradually forming multi-level and multi-attribute transport hub networks in the cities. At the same time, Super Network Theory (SNT) has advantages in displaying the multi-layer transport hubs. The aim of this paper is to provide a new perspective to study connectivity contribution of potential hubs. Urban transport hubs are ranked through topological features based on Hub Super Network (HSN). This paper proposes two indexes based on Super-Edge (SE), Zero Hub Degree of SE (ZHDoSE) and a number of shared SEes (NSSE), respectively. Then, a case study was conducted in Beijing, which considers four combinations to study the influence of transport modes and subway lines on connectivity. The results show that no-normalization strengthens the contribution of transport modes and subway lines on connectivity. Besides, the transport mode contributes a lot to the connectivity. However, elements normalization strengthens the subway lines under ZHDoSE reciprocal. In addition, various weights of ZHDoSE and NSSE have different influences on the recognition results of SEes in HSN

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Gestión de la marca profesional del emprendedor en LinkedIn

Tras el creciente interés que despierta en los jóvenes la creación de empresas y la frecuente utilización de las redes sociales profesionales, este estudio investiga los efectos de la gestión activa de la red social LinkedIn. De manera específica, se trata de conocer el impacto del volumen de publicaciones sobre el número de contactos y reacciones de dichos contactos. Además, este trabajo analiza las diferencias entre mujeres y hombres en la gestión, y el efecto moderador de variables relacionadas con el tipo de publicaciones, el género y el tipo de dirección del emprendedor y la empresa en la relación entre el volumen de publicaciones y el número de contactos y reacciones.Para ello, se ha realizado un estudio empírico a través de un cuestionario online dirigido a emprendedores que utilizan LinkedIn. En total, se obtuvieron 127 respuestas válidas que fueron analizadas a través de comparativas de grupos entre mujeres y hombres, así como mediante diversos análisis de regresión.Los resultados indican que el volumen de publicaciones explica el número de contactos, el número medio de reacciones y el número medio de comentarios. El tipo de contenido ejerce un papel moderador en las relaciones entre el volumen de las publicaciones y el número de contactos y número medio de comentarios por publicación, y el liderazgo ejerce una moderación sobre la relación entre el volumen de publicaciones y el número medio de comentarios recibido en las publicaciones. En este estudio no se han encontrado diferencias significativas entre mujeres y hombres. En base a estos resultados se proponen una serie de recomendaciones para la gestión de los emprendedores, y se extraen una serie de implicaciones de interés para la literatura académica.<br /