Using IQA to Extract Mental Models Concerning Quality Evaluation Factors for Social Networking Services on Smartphones

Although today’s users of mobile phones are afforded increasing control as the popularity and richness of information provided by smartphones increases, how these users make choices and evaluations cannot be explicitly expressed. This study uses an acute observation of designers of technology and aesthetics to conduct a mental-model analysis of smartphones with the purpose of explicitly identifying the differences in the schemas of users. This study also modifies the mental model to evaluate the practicability of the mental model. Hence, this study investigates groups of subjects, comparing freshmen and graduating students froma college of design, and adopts a schematized data collection and analysis approach, Interactive Qualitative Analysis (IQA), to ascertain the relationships between factors and establish a complete user mental model. The study results present 11 primary affinity factors: “Pricing,” “Advertisement,” “User Interface,” “Innovation Functions,” “System Maintenance,” “Privacy,” “Utility Functions,” “Personalization,” “Network Performance,” “Customer Service,” and “Multimedia Contents.” The primary driver in evaluating smartphones is found to be “Pricing” for graduating students and “Privacy” for freshmen. The two groups share the same final effect factor, “Multimedia Contents.” This study has successfully identified differences between the different mental models of the two groups, supporting the method that using IQA to perform a quality evaluation of social networking for smartphones is effective. Consequently, platform developers can understand user demand through a mental model and can design good platform functions to effectively improve users’ experience of smartphones.

Design and implementation of user information sharing system using location-based services for social network services

Internet takes a role as a place for communication between people beyond a space simply for the acquisition of information.Recently,social network service(SNS)reflecting human’s basic desire for talking and communicating with others is focused on around the world.And location-based service(LBS)is a service that provides various life conveniences like improving productivity through location information,such as GPS and WiFi.This paper suggests an application combining LBS and SNS based on Android OS.By using smart phone which is personal mobile information equipment,it combines location information with user information and SNS so that the service can be developed.It also maximizes sharing and use of information via twit based on locations of friends.This proposed system is aims for users to show online identity more actively and more conveniently.

SNS model for providing social network channels through queries

Users can obtain the information through a basic web searching and find the answer to the questions directly,but maybe the expected answer does not exist.Besides,we do not know the update of new information in time.The online social networking services spread quickly and store many user data,but these data are worth less and may be unreliable answer to users’ questions.Users can obtain the simple answer but can not expect more additional information in knowledge question-answering(QA)system.In this paper,we design the system with the advantages of knowledge QA system,web searching and characteristics of social networking service for providing social network channel based on the query and answer without users’ contact network.The user can obtain real-time answers by the user network interested in users’ querires through the network channel of this system,get the additional information effectively and share it with others in the social network channel in this system.

Modeling and Predicting of News Popularity in Social Media Sources

The popularity of news,which conveys newsworthy events which occur during day to people,is substantially important for the spectator or audience.People interact with news website and share news links or their opinions.This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources.These techniques consist of basically two phrases:a)the training data is sent as input to the classifier algorithm,b)the performance of prelearned algorithm is tested on the testing data.And so,a knowledge discovery from the data is performed.In this context,firstly,twelve datasets from a set of data are obtained within the frame of four categories:Economic,Microsoft,Obama and Palestine.Second,news popularity prediction in social network services is carried out by utilizing Gradient Boosted Trees,Multi-Layer Perceptron and Random Forest learning algorithms.The prediction performances of all algorithms are examined by considering Mean Absolute Error,Root Mean Squared Error and the R-squared evaluation metrics.The results show that most of the models designed by using these algorithms are proved to be applicable for this subject.Consequently,a comprehensive study for the news prediction is presented,using different techniques,drawing conclusions about the performances of algorithms in this study.

On-Foot Content Tourism Support System in Urban Tourist Destinations of Japan

It has also become possible for people to easily acquire tourism information that matches their own particular tastes using the internet, so tourism has taken a more diverse range of forms. One of these is “content tourism” that became popular in recent Japan. Sgains such a backdrop, the present study aimed to design, develop, operate and evaluate a system that integrated three subsidiary systems including Web-geographic information systems (GIS), a tourism information system and social networking service (SNS) in order to support walking-based content tourism. This made it possible to create sightseeing routes and to submit, store, and view tourist attraction information. Additionally, the system was operated for one month in Chofu City, Tokyo Metropolis, and the total number of users was 42. During the operation period, 12 new sightseeing routes were created and 30 new tourist attractions were submitted. Based on the results of the web questionnaire survey, users highly evaluated the key functions of the system and the overall system. However, of those key functions, evaluations were somewhat low for the ease of operation of the creation function of sightseeing route, so the improvements must be made. From the results of the access log analysis of users’ log data, the total number of sessions in this system was 134, 64% used PCs, 36% used mobile devices, Therefore, the approach of designing the system such that the same functions could be used regardless of the type of device one is using was an effective design approach.