Exploiting users’ social relations to forward data in opportunistic networks: The HiBOp solution

Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paper we propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks. We show performance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS.     

Valuation of online social networks taking into account users’ interconnectedness

Online social networks have been gaining increasing economic importance in light of the rising number of their users. Numerous recent acquisitions priced at enormous amounts have illustrated this development and revealed the need for adequate business valuation models. The value of an online social network is largely determined by the value of its users, the relationships between these users, and the resulting network effects. Therefore, the interconnectedness of a user within the network has to be considered explicitly to get a reasonable estimate for its economic value. Established standard business valuation models, however, do not account for these aspects sufficiently. Thus, we propose an economic model for the valuation of online social networks, which takes into account the users’ interconnectedness within the network. Furthermore, we analyze different centrality measures, which can be used to quantify users’ interconnectedness in online social networks and propose a measure which is based on the PageRank-algorithm. Finally, the practical application of the model is illustrated by an example of the European online social network XING.com.     

Analyzing users’ activity in online social networks over time through a multi-agent framework

The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users’ activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system that automates the process of gathering data from users’ activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in online events based on network theory metrics. We evaluated its functionality analyzing users’ activity in events on Twitter.     

Predicting the influence of users’ posted information for eWOM advertising in social networks

Many social network websites have been aggressively exploring innovative electronic word-of-mouth (eWOM) advertising strategies using information shared by users, such as posts and product reviews. For example, Facebook offers a service allowing marketers to utilize users’ posts to automatically generate advertisements. The effectiveness of this practice depends on the ability to accurately predict a post’s influence on its readers. For an advertising strategy of this nature, the influence of a post is determined jointly by the features of the post, such as contents and time of creation, and the features of the author of the post. We propose two models for predicting the influence of a post using both sources of influence, post- and author-related features, as predictors. An empirical evaluation shows that the proposed predictive features improve prediction accuracy, and the models are effective in predicting the influence score.     

Correction to: Can search result summaries enhance the web search efficiency and experiences of the visually impaired users?

There are limited studies that are addressing the challenges of visually impaired (VI) users when viewing search results on a search engine interface by using a screen reader. This study investigates the effect of providing an overview of search results to VI users. We present a novel interactive search engine interface called InteractSE to support VI users during the results exploration stage in order to improve their interactive experience and web search efficiency. An overview of the search results is generated using an unsupervised machine learning approach to present the discovered concepts via a formal concept analysis that is domain-independent. These concepts are arranged in a multi-level tree following a hierarchical order and covering all retrieved documents that share maximal features. The InteractSE interface was evaluated by 16 legally blind users and compared with the Google search engine interface for complex search tasks. The evaluation results were obtained based on both quantitative (as task completion time) and qualitative (as participants’ feedback) measures. These results are promising and indicate that InteractSE enhances the search efficiency and consequently advances user experience. Our observations and analysis of the user interactions and feedback yielded design suggestions to support VI users when exploring and interacting with search results.

     

Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model

This study explores users’ continuance intention in online social networks by synthesizing Bhattacherjee’s IS continuance theory with flow theory, social capital theory, and the unified theory of acceptance and use of technology (UTAUT) to consider the special hedonic, social and utilitarian factors in the online social network environment. The integrated model was empirically tested with 320 online social network users in China. The results indicated that continuance intention was explained substantially by all hypothesized antecedents including perceived enjoyment, perceived usefulness, usage satisfaction, effort expectancy, social influence, tie strength, shared norms and trust. Based on the research findings, we offer discussions of both theoretical and practical implications.     

Detecting and tagging users’ social circles in social media

As a media and communication platform, microblog becomes more popular around the world. Most users follow a large number of celebrities and public medias on microblog; however, these celebrities do not necessarily follow all their fans. Such one-way relationship abounds in ego network and is displayed by the forms of users’ followees and followers, which make it difficult to identify users’ real friends who are contained in merged lists of followees and followers. The aim of this paper is to propose a general algorithm for detecting users’ real friends in social media and dividing them into different social circles automatically according to the closeness of their relationships. Then we analyze these social circles and detect social attributes of these social circles. To verify the effectiveness of the proposed algorithm, we build a microblog application which displays algorithm results of social circles for users and enables users to adjust proposed results according to her/his real social circles. We demonstrate that our algorithm is superior to the traditional clustering method in terms of F value and mean average precision. Furthermore, our method of tagging social attributes of social circles gets high performance by NDCG (normalized discounted cumulative gain).     

Exploring the feasibility of web form adaptation to users’ cultural dimension scores

With many daily tasks now performed on the Internet, productivity and efficiency in working with web pages have become transversal necessities for all users. Many of these tasks involve the inputting of user information, obligating the user to interact with a webform. Research has demonstrated that productivity depends largely on users’ personal characteristics, implying that it will vary from user to user. The webform development process must therefore, include modeling of its intended users to ensure the interface design is appropriate. Taking all potential users into account is difficult, however, primarily because their identity is unknown, and some may be effectively excluded by the final design. Such discrimination can be avoided by incorporating rules that allow webforms to adapt automatically to the individual user’s characteristics, the principal one being the person’s culture. In this paper we report two studies that validate this option. We begin by determining the relationships between a user’s cultural dimension scores and their behavior when faced with a webform. We then validate the notion that rules based on these relationships can be established for the automatic adaptation of a webform in order to reduce the time taken to complete it. We conclude that the automatic webform adaptation to the cultural dimensions of users improves their performance.     

Using hyperbolic trees to visualize data generated by patent-centric and group-oriented data processing

<美国专利>6,499,026Rivette, et al.December 24,2002<发明人>     

Automatic extraction of user’s search intention from web search logs

Web search users complain of the inaccurate results produced by current search engines. Most of these inaccurate results are due to a failure to understand the user??s search goal. This paper proposes a method to extract users?? intentions and to build an intention map representing these extracted intentions. The proposed method makes intention vectors from clicked pages from previous search logs obtained on a given query. The components of the intention vector are weights of the keywords in a document. It extracts user??s intentions by using clustering the intention vectors and extracting intention keywords from each cluster. The extracted the intentions on a query are represented in an intention map. For the efficiency analysis of intention map, we extracted user??s intentions using 2,600 search log data a current domestic commercial search engine. The experimental results with a search engine using the intention maps show statistically significant improvements in user satisfaction scores.     

Exploring factors related to users’ experience of public transport route choice: influence of context and users profiles

This exploratory study aims to achieve a better understanding of the users-related factors that affect the choice of routes in public transport (PT). We also look at what can motivate route and modes changes towards alternatives in a real situation. We investigated the experience of 19 users of PTs, using the critical incident technique (Flanagan in Psychol Bull 51(4):327, 1954). We asked participants to report incidents (i.e. situations) in cases they were very satisfied or dissatisfied with their choice. For both situations, the case of their usual route and case of an alternative were considered. A total of 91 incidents were collected and analysed using a multiple correspondences analysis. Additionally, users’ profiles were characterized and superposed to the analysis of incidents content. The main results are as follows. First, the user’s choice of PT route depends on the context (i.e. aim of the travel, time of day). Second, taking an alternative to the usual PT route or using a route combining different transport modes is determined by the context and by factors related to the pleasantness of the travel (e.g. to accompany a friend along the travel). Finally, depending on the user’s profile (i.e. combination of attitude towards PT and demographic variables), the factors taken into account to make the choice of a PT route are related to the efficiency or the pleasantness of the trip. These results show the importance of the contextual factors and the users’ profiles in route choice. They suggest that these factors should be further taken into account in new tools and services for mobility.     

Discovering mobile users’ moving behaviors in wireless networks

Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to discover personal mobility patterns for helping systems provide personalized service in a wireless network. The classification and the duration of each location area visited by a mobile user are used as important attributes in representing the results. A data mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways. Experiments are also made to show the effect of the proposed algorithm.     

Using think-aloud and psychometrics to explore users’ experience with a news Web site

The present study is part of a research programme that aims to develop and test a psychological model of end-users’ experience with news sites. An exploratory study of interaction experience with a news Web site was conducted. An online questionnaire was used to collect information on demographics, Internet-use and news-site use behaviour of users of a particular news site, and to recruit participants for a think-aloud study. The protocol analysis of screen-capture and audio recordings of participants, who used a news site while thinking aloud, yielded five categories of experience: impression, content, layout, information architecture and diversion. These categories are regarded as spontaneous, self-reported aspects of users’ experience with a news site. A set of interaction-experience questionnaires revealed significant differences between regular users and non-users of a news site. Correlation and regression analyses demonstrated support for Hassenzahl’s model of interaction experience. The study presents a first attempt to empirically investigate the aspects of interaction experience in relation to online news sites.     

User study on older adults’ use of the Web and search engines

Ten older adults were interviewed about the motivational factors behind learning to use computers and the negative and positive aspects related to it. They were then given search tasks and were observed as they used search engines for finding information from the Web. All of the participants completed several search tasks during the search session. Although their performance in the search tasks was adequate, they faced several problems in the interaction. For example, text editing was difficult and understanding the structure of the Web and terminology used caused problems. Based on the observations, an elderly friendly search user interface is proposed.     

Is visual content in textual search interfaces beneficial to dyslexic users?

Dyslexia is a learning disability characterised by problems with accurate or fluent word recognition, poor decoding, and poor spelling abilities. Although several studies have addressed dyslexia and Web accessibility, less is known about how dyslexia affects information search. This study investigated whether the inclusion of icons in search user interfaces enhances performance among dyslexics. A total of 21 dyslexics and 21 controls completed 52 search tasks in 4 conditions: icons only, words only, and both icons and words in a grid layout and a list layout, while eye movements were recorded. Dyslexics took significantly longer than controls to locate targets in tasks containing text, but not in the icon-only condition. Dyslexics had longer fixation durations than controls in both icon and text based search arrays, suggesting higher mental load associated with search tasks generally. The addition of words to icon arrays led to faster search times within controls, but not dyslexics. Dyslexics also exhibited more fixations on dual-modality tasks, and longer scanpaths than controls in list layout. Both groups were fastest searching the list layout, with icons and words listed in columns. Results are discussed in terms of the design of accessible search interfaces for dyslexic users, taking into account mental load of dual-modality information display, and the arrangement of search items. Empirical data is provided for the design of accessible search results interfaces for dyslexics.     

Understanding the intentions of users to ‘stick’ to social networking sites: a case study in Taiwan

Social networking sites (SNSs) are the most popular social platforms for developing personal networks. They provide multiple interactive functions for users to create and use large social networks. To determine why people exhibit ‘stickiness’ to SNSs, this study uses the uses and gratifications theory as an underlying structure and builds the research model with factors of motivational needs and interactivity. Our results showed that social needs, information needs, human–message interaction, and human–human interaction are crucial factors that affect the ‘stickiness’ of users to SNSs. The implications of these findings are presented in this paper.     

A trace-based approach to identifying users’ engagement and qualifying their engaged-behaviours in interactive systems: application to a social game

Analysing and monitoring users’ engaged-behaviours continuously and under ecologically valid conditions can reveal valuable information for designers and practitioners, allowing them to analyse, design and monitor the interactive mediated activity, and then to adapt and personalise it. An interactive mediated activity is a human activity supported by digital interactive technologies. While classical metric methods fall within quantitative approaches, this paper proposes a qualitative approach to identifying users’ engagement and qualifying their engaged-behaviours from their traces of interaction. Traces of interaction represent the users’ activities with an interactive environment. The basis of our approach is to transform low-level traces of interaction into meaningful information represented in higher-level traces. For this, our approach combines three theoretical frameworks: the Self-Determination Theory, the Activity Theory and the Trace Theory. Our approach has been implemented and tested in the context of the QUEJANT Projet. QUEJANT targets the development of a system allowing the actors of Social Gaming to analyse players’ engagement from an analysis of their activity traces. In order to demonstrate the feasibility of our approach, we implemented the whole process in a prototype and applied it to 12 players’ interaction data collected over four months. Based on these interaction data, we were able to identify engaged and non-engaged users and to qualify their types of engaged-behaviours. We also conducted a user study based on a validation of our results by experts. The high prediction rate obtained confirms the performance of our approach. We finally discuss the limitations of our approach, the potential fields of application and the implications for digital behavioural interventions.     

Towards understanding the gamification upon users’ scores in a location-based social network

Online social platform, such as Wikipedia and Foursquare, has been increasingly exploded due to not only various useful services provided but also social gaming mechanisms that can keep users actively engaged. For example, users are awarded ”virtual goods” like badges and points when they contribute to the community in the network by voluntarily sharing ideas and other information. In this paper, we aim to examine the effectiveness of a social gamification mechanism, named user scores, designed in Foursquare which is one of most popular location-based social networks. A user’s score in Foursquare is an aggregate measure based on recent check-in activities of the user, which reflects a snapshot summary of the user’s temporal and spatial behaviors. Whenever a user checks in to a venue, a list of scores of the user’s friends are visible to the user via a ”leaderboard” which ranks these users’ scores in a descending order. Given a pair of friends who participate in a score competition in such a gimification mechanism, we identify if one user’s scores have significant influence on the other user’s scores by utilizing the Granger Causality Test. To understand what types of users and what types of friends tend to participate in the score competition (i.e., their check-ins are more likely driven by such a gamification mechanism), we extract users’ features (e.g. user’s degree) as well as the features of pairs of friends (e.g., number of common friends, score similarity and ranking difference) to examine whether these features have correlations with those pairs of users who are identified as being involved in the score game. The identified influence on user scores has the important implication on applications including friend and venue recommendations in location-based social networks.