This article explores the use of social networks by workers in Abidjan, Côte d’Ivoire, with particular emphasis on a descriptive or quantitative analysis aimed at understanding motivations and methods of use. Mo...This article explores the use of social networks by workers in Abidjan, Côte d’Ivoire, with particular emphasis on a descriptive or quantitative analysis aimed at understanding motivations and methods of use. More than five hundred and fifty questionnaires were distributed, highlighting workers’ preferred digital channels and platforms. The results indicate that the majority use social media through their mobile phones, with WhatsApp being the most popular app, followed by Facebook and LinkedIn. The study reveals that workers use social media for entertainment purposes and to develop professional and social relationships, with 55% unable to live without social media at work for recreational activities. In addition, 35% spend on average 1 to 2 hours on social networks, mainly between 12 p.m. and 2 p.m. It also appears that 46% believe that social networks moderately improve their productivity. These findings can guide marketing strategies, training, technology development and government policies related to the use of social media in the workplace.展开更多
为探讨大学生社交网络使用强度、网络社会支持与网络人际信任之间的关系,采用《社交网络使用强度量表》《网络人际信任问卷》和《大学生网络社会支持问卷》对786名大学生进行了测评。结果发现:(1)大学生社交网络使用强度、网络人际信任...为探讨大学生社交网络使用强度、网络社会支持与网络人际信任之间的关系,采用《社交网络使用强度量表》《网络人际信任问卷》和《大学生网络社会支持问卷》对786名大学生进行了测评。结果发现:(1)大学生社交网络使用强度、网络人际信任与网络社会支持之间均呈显著正相关(r=0.32~0.52, p <0.001);(2)社交网络使用强度对网络人际信任直接效应显著,效应值为0.14,相对效应值为50%,且社交网络使用强度通过网络社会支持影响网络人际信任的间接作用也显著,中介效应值为0.14,相对效应值为50%。结论:本研究揭示了社交网络使用强度对网络人际信任的双重影响路径,一方面,直接影响网络人际信任;另一方面,通过增强网络社会支持间接作用于网络人际信任。展开更多
In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involv...In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involves machine learning classifying techniques. To facilitate this, we propose a general framework that can be applied to any social media content to develop an intelligent system. The framework consists of three main parts: an interface, classifier and ana-lyzer. The analyzer uses media recognition to identify specific features. Then, the classifier uses these features and involves them in the classification process. The interface organizes the interaction between the system compo-nents. We tested the framework and developed a system to be applied to im-age-based social media networks (Instagram). The system was implemented as a mobile application (My Interests) that works as a recommendation and filtering system for Instagram users and reduces the time they spend on irre-levant information. It analyzes the images, categorizes them, identifies the in-teresting ones, and finally, reports the results. We used the Cloud Vision API as a tool to analyze the images and extract their features. Furthermore, we adapted support vector machine (SVM), a machine learning method, to classify images and to predict the preferred ones.展开更多
Traditional Folk relations of debit and credit have existed for thousands of years in Chinese society. In the rapidly develop of the context of mobile internet and social network, the borrowing that relies on the rela...Traditional Folk relations of debit and credit have existed for thousands of years in Chinese society. In the rapidly develop of the context of mobile internet and social network, the borrowing that relies on the relationship among people is not just a financial domain scope discussion topic. In the rapidly developing Chinese mobile Internet, a new anonymous mechanism which is based on interpersonal credit extension and evaluation ultimately form borrowing is continuously formed.?In this paper, the author researches and analyzes on what is relationship lending mechanism, the basic operation modes of relationship lending mechanism, a part of theoretical supporting and values.展开更多
文摘This article explores the use of social networks by workers in Abidjan, Côte d’Ivoire, with particular emphasis on a descriptive or quantitative analysis aimed at understanding motivations and methods of use. More than five hundred and fifty questionnaires were distributed, highlighting workers’ preferred digital channels and platforms. The results indicate that the majority use social media through their mobile phones, with WhatsApp being the most popular app, followed by Facebook and LinkedIn. The study reveals that workers use social media for entertainment purposes and to develop professional and social relationships, with 55% unable to live without social media at work for recreational activities. In addition, 35% spend on average 1 to 2 hours on social networks, mainly between 12 p.m. and 2 p.m. It also appears that 46% believe that social networks moderately improve their productivity. These findings can guide marketing strategies, training, technology development and government policies related to the use of social media in the workplace.
文摘为探讨大学生社交网络使用强度、网络社会支持与网络人际信任之间的关系,采用《社交网络使用强度量表》《网络人际信任问卷》和《大学生网络社会支持问卷》对786名大学生进行了测评。结果发现:(1)大学生社交网络使用强度、网络人际信任与网络社会支持之间均呈显著正相关(r=0.32~0.52, p <0.001);(2)社交网络使用强度对网络人际信任直接效应显著,效应值为0.14,相对效应值为50%,且社交网络使用强度通过网络社会支持影响网络人际信任的间接作用也显著,中介效应值为0.14,相对效应值为50%。结论:本研究揭示了社交网络使用强度对网络人际信任的双重影响路径,一方面,直接影响网络人际信任;另一方面,通过增强网络社会支持间接作用于网络人际信任。
文摘In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involves machine learning classifying techniques. To facilitate this, we propose a general framework that can be applied to any social media content to develop an intelligent system. The framework consists of three main parts: an interface, classifier and ana-lyzer. The analyzer uses media recognition to identify specific features. Then, the classifier uses these features and involves them in the classification process. The interface organizes the interaction between the system compo-nents. We tested the framework and developed a system to be applied to im-age-based social media networks (Instagram). The system was implemented as a mobile application (My Interests) that works as a recommendation and filtering system for Instagram users and reduces the time they spend on irre-levant information. It analyzes the images, categorizes them, identifies the in-teresting ones, and finally, reports the results. We used the Cloud Vision API as a tool to analyze the images and extract their features. Furthermore, we adapted support vector machine (SVM), a machine learning method, to classify images and to predict the preferred ones.
文摘Traditional Folk relations of debit and credit have existed for thousands of years in Chinese society. In the rapidly develop of the context of mobile internet and social network, the borrowing that relies on the relationship among people is not just a financial domain scope discussion topic. In the rapidly developing Chinese mobile Internet, a new anonymous mechanism which is based on interpersonal credit extension and evaluation ultimately form borrowing is continuously formed.?In this paper, the author researches and analyzes on what is relationship lending mechanism, the basic operation modes of relationship lending mechanism, a part of theoretical supporting and values.
