ARTÍCULO
TITULO

ECharacterize: A Novel Feature Selection-Based Framework for Characterizing Entrepreneurial Influencers in Arabic Twitter

Bodor Moheel Almotairy    
Manal Abdullah    
Rabeeh Abbasi    

Resumen

Abstract? Social media are widely used as communication platforms in the world of business. Twitter, in particular, offers valuable opportunities for collaboration due to its open nature. For that, many entrepreneurs employ Twitter for different reasons, such as mobilizing financial resources, get funding, and increase their innovation capabilities. Therefore, they keep looking for local entrepreneurial accounts to help them. Messages from entrepreneurial influencers -opinion leader- increase the information diffusion to entrepreneurs, helping them to find more opportunities. Discovering the characteristics of entrepreneurial influencers in Twitter networks becomes extremely important since it reflects the way to reach entrepreneurs. In the present paper, we propose a novel framework called ECharacterize based on feature selections techniques to discover the characteristics of the entrepreneurial influencer in the Saudi context in a robust manner. The framework extracts abundant influencers? features and then employs seven state-of-the-art ranking methods to determine the characteristics of the most relevant influencer. It robustly aggregates the lists to come out with the accurate final list using Robust Rank Aggregation. The framework examined on 233,018 real-life Arabic tweets. The results show the ability of the proposed method to distinguish between the influencers by their popularity, reliability and activity level.   

 Artículos similares

       
 
Umberto Saetti, Jonathan Rogers, Mushfiqul Alam and Michael Jump    
A novel trajectory generation and control architecture for fully autonomous autorotative flare that combines rapid path generation with model-based control is proposed. The trajectory generation component uses optical Tau theory to compute flare trajecto... ver más
Revista: Aerospace

 
Zongshun Wang, Ce Li, Jialin Ma, Zhiqiang Feng and Limei Xiao    
In this study, we introduce a novel framework for the semantic segmentation of point clouds in autonomous driving scenarios, termed PVI-Net. This framework uniquely integrates three different data perspectives?point clouds, voxels, and distance maps?exec... ver más
Revista: Information

 
Weiming Fan, Jiahui Yu and Zhaojie Ju    
Endoscopy, a pervasive instrument for the diagnosis and treatment of hollow anatomical structures, conventionally necessitates the arduous manual scrutiny of seasoned medical experts. Nevertheless, the recent strides in deep learning technologies proffer... ver más
Revista: Information

 
Hang Li, Shengjie Zhao and Hao Deng    
The extraction of community-scale green infrastructure (CSGI) poses challenges due to limited training data and the diverse scales of the targets. In this paper, we reannotate a training dataset of CSGI and propose a three-stage transfer learning method ... ver más
Revista: Information

 
Wenhao Sun, Yidong Zou, Yunhe Wang, Boyi Xiao, Haichuan Zhang and Zhihuai Xiao    
In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address th... ver más
Revista: Water