Characterizing videos, audience and advertising in Youtube channels for kids

Online video services, messaging systems, games and social media services are tremendously popular among young people and children in many countries. Most of the digital services offered on the internet are advertising funded, which makes advertising ubiquitous in children's everyday life. To understand the impact of advertising-based digital services on children, we study the collective behavior of users of YouTube for kids channels and present the demographics of a large number of users. We collected data from 12,848 videos from 17 channels in US and UK and 24 channels in Brazil. The channels in English have been viewed more than 37 billion times. We also collected more than 14 million comments made by users. Based on a combination of text-analysis and face recognition tools, we show the presence of racial and gender biases in our large sample of users. We also identify children actively using YouTube, although the minimum age for using the service is 13 years in most countries. We provide comparisons of user behavior among the three countries, which represent large user populations in the global North and the global South

A customer segmentation framework for targeted marketing in telecommunication

© 2017 IEEE. Telecommunication industry is highly competitive, and mass marketing is not applicable anymore. Moreover, Mobile customers have different behaviors that urge telecom industries to differentiate their strategies to meet customers' needs. At the same time, mobile operators have an enormous amount of customer records, and data-driven approaches can help them to draw insights from this huge amount of data. Therefore, a data-driven segmentation approach can support marketing strategies to tailor their marketing plans. In this research, we adopt behavior and beneficial segmentation in a two-dimensional framework to segment customers. The results indicate that our method has an outstanding performance for customer segmentation. Moreover, we have recommended some marketing strategies based on each segment's behavior with the aim of increasing in Average Revenue Per User (ARPU) and decreasing in marketing expenses

Business Modeling Framework For Personalization In Mobile Business Services

Is presented the structure of a formal framework for personalizationfeatures for mobile business services, which can be used to drive thebusiness modeling of M-business services from a service provider pointof view. It also allows to compute the revenue as linked topersonalization levels and features. A case study has been performedin the area of personalized location based mobile servicespersonalization;individual profiles;location based services;mobile business;mobile services

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

Factors affecting online banking adoption based on unified theory of acceptance and use of technology (UTAUT)

Internet revolutions have brought a huge impact on banking industry. It had also created a novel way for handling banking transactions via online banking channel. There was approximately 23.6% increase in online banking subscribers in Malaysia since 2005 to 2010. However, past literature reviews claimed that online banking was not favorable in Malaysia. Hence, this study attempts to examine the factors that affecting the intention to adopt online banking based on unified theory of acceptance and use of technology (UTAUT). Other than the four constructs from UTAUT, one additional construct has been added into this model for the purpose of this study. The five determinants were performance expectancy, efforts expectance, facilitating conditions, social influence and personal innovativeness in IT were examined in this study. A total of 400 questionnaires were distributed among UTM students, where respondents were randomly selected. Principle component analysis and Cronbach’s alpha were used to test the validity and reliability of the measurement scale. Pearson correlation was employed to examine the relationships between variables, and multiple regressions were used to test the hypothesis of this study. Regression model in this study found that 48.5% of the variance had been significantly explained by the predictors. As a conclusion, the findings of this study revealed that all the factors except facilitating conditions are significantly affecting the intention to adopt online bankin

Mobile Internet Service Preferences of Young Customers: Evidence from Bangladesh

Purpose: With the global wireless communication growth, the launching of commercial fourth-generation (4G) services in Bangladesh, including high transmission speed, the number of mobile internet subscribers has grown considerably during the last five years. During the same time, business models have become increasingly complex in the cellular industry because internet subscribers perceive value differently. This situation originates challenges among the mobile service operators in Bangladesh to create internet subscribers and retain the existing ones, especially the young mobile data users with more switching trends. Therefore, the objective of the study is to identify the determining factors that influence young customers to choose mobile internet services. Methods: This study is based on primary data collected from 440 young mobile internet users below 30 years old in Bangladesh. The study investigated ten factors of customer preferences for mobile internet, comparing male and female customers using the statistical tools: mean, standard deviation, and two-tailed t-test. Results: The study results indicate significant differences between female and male customers in mobile internet preference factors, including maximum coverage area, network quality and speed, security and privacy, and customer care. The female customers choose internet packages for security and privacy and a variety of packages, while the male customers choose mobile internet for maximum area of coverage and company image. Implications: The study findings have critical managerial implications for mobile internet providers to tailor their network and services to create new customers and gain customer retention resulting in significant growth and substantial earnings

Strategic understanding of Malaysian online customers’ service quality preference through demographic customer profiling and e-product bundling

In order to stay competitive in the arena of e-commerce, conventional e-marketing research have provided solutions to online businesses and marketing practitioners by understanding online purchasing behavior and thereby proposing various determinants influencing online purchasing behavior. Little research has been done in order to assist marketing practitioners to identify the precise online customer segmentation, making market targeting and positioning and use of effective marketing campaigns complex. Hence, this study aims to identify the appropriate online customer segmentation (product bundles) based on three determinants of online purchasing behavior, i.e. e-service quality, demographic profiles and types of product purchased. 680 useful data was collected from existing online shoppers and data mining technique was employed to identify the product bundles and decision trees were used for customer profiling. Findings have identified Tickets, Clothing and Travel product bundles as the basis of segmentation. Result from this study will assist online marketing practitioners to be conscious of online customers needs and astutely create marketing campaigns aiming at their targeted online customers segment

Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics

Business Modeling Framework For Personalization In Mobile Business Services

Is presented the structure of a formal framework for personalization features for mobile business services, which can be used to drive the business modeling of M-business services from a service provider point of view. It also allows to compute the revenue as linked to personalization levels and features. A case study has been performed in the area of personalized location based mobile service