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).     

Towards estimating computer users’ mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study (n = 26) and a controlled study (n = 16) were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response (GSR) data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the results of the studies found significant average correlations between mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is possible from interaction behaviour with keyboard and mouse.     

A tourism destination recommender system using users’ sentiment and temporal dynamics

With the development and popularity of social networks, an increasing number of consumers prefer to order tourism products online, and like to share their experiences on social networks. Searching for tourism destinations online is a difficult task on account of its more restrictive factors. Recommender system can help these users to dispose information overload. However, such a system is affected by the issue of low recommendation accuracy and the cold-start problem. In this paper, we propose a tourism destination recommender system that employs opinion-mining technology to refine user sentiment, and make use of temporal dynamics to represent user preference and destination popularity drifting over time. These elements are then fused with the SVD+ + method by combining user sentiment and temporal influence. Compared with several well-known recommendation approaches, our method achieves improved recommendation accuracy and quality. A series of experimental evaluations, using a publicly available dataset, demonstrates that the proposed recommender system outperforms the existing recommender systems.     

The interplay between users’ intraorganizational social media use and social capital

The wide acceptance of social media by the public has caused companies try to use intraorganizational social media to increase employee work performance. However, simply implementing a platform is insufficient for success. Companies must encourage employees to use social media for work-related purposes. This study divided the use of intraorganizational social media into social- and work-related use and proposed a model based on the theory of social capital to explore the effects of social-related use on work-related use. The model was tested using a survey of users of intraorganizational microblog systems in China. The results indicate the relationships among two types of intraorganizational use and the dimensions of social capital, and that social-related use fosters work-related use directly and indirectly by enhancing social capital. These results facilitate an understanding of the value of social activities conducted using intraorganizational social media in organizations.     

Ranking entities on the basis of users’ opinions

Online opinions are one of the most important sources of information on which users base their purchasing decisions. Unfortunately, the large quantity of opinions makes it difficult for an individual to consume in a reasonable amount of time. Unlike standard information retrieval problems, the task here is to retrieve entities whose relevance is dependent upon other people’s opinions regarding the entities and how well those sentiments match the user’s own preferences. We propose novel techniques that incorporate aspect subjectivity measures into weighting the relevance of opinions of entities based on a user’s query keywords. We calculate these weights using sentiment polarity of terms found proximity close to keywords in opinion text. We have implemented our techniques, and we show that these improve the overall effectiveness of the baseline retrieval task. Our results indicate that on entities with long opinions our techniques can perform as good as state-of-the-art query expansion approaches.     

Suspended because of social media? Students’ knowledge and opinions of university social media policies and practices

In this exploratory study, we examined undergraduates’ (N = 298) knowledge of their university’s social media policies, understanding of free speech and privacy protections, opinions about university monitoring and discipline for personal social media posts, and perceptions of fairness regarding recent cases of student discipline for personal social media use. The results of our study indicate that most undergraduates are highly underinformed as to whether or not their university has a social media policy, particularly if the students are early in their academic careers and do not engage in many online privacy protection behaviors. Most participants were also misinformed as to whether free speech and/or privacy protections will shield them from university discipline. In addition, most participants (78%) were opposed to the idea of universities monitoring students’ personal social media accounts, though significantly fewer (68%) were opposed to monitoring student athletes’ social media. Finally, when asked about several recent cases involving student discipline, most participants were generally opposed to a variety of university disciplinary actions regarding students’ social media posts. We discuss these findings as they relate to the need for better social media policy training for students, as well as the potential impact on students’ academic and future careers.     

The analysis of expert opinions’ consensus quality

In many situations we need to obtain one, common decision (which can be understood as a consistent state of knowledge) out of opinions collected from many experts or any other external sources. This entails a problem concerning the reliability of such decision. We would like to know that decisions based on experts’ opinions are trustworthy. Unfortunately, in many cases the determination of such decision is difficult and expensive, especially when big sets of input data are involved in the process. This paper presents a framework which allows to assess the quality of the aforementioned final decision. Its output is based solely on the analysis of its input (e.g. an assumed representation of experts’ opinions). Moreover, the paper contains an overview of several types of possible approaches to the considered topic.     

MORE – a multimodal observation and analysis system for social interaction research

The MORE system is designed for observation and machine-aided analysis of social interaction in real life situations, such as classroom teaching scenarios and business meetings. The system utilizes a multichannel approach to collect data whereby multiple streams of data in a number of different modalities are obtained from each situation. Typically the system collects a 360-degree video and audio feed from multiple microphones set up in the space. The system includes an advanced server backend component that is capable of performing video processing, feature extraction and archiving operations on behalf of the user. The feature extraction services form a key part of the system and rely on advanced signal analysis techniques, such as speech processing, motion activity detection and facial expression recognition in order to speed up the analysis of large data sets. The provided web interface weaves the multiple streams of information together, utilizes the extracted features as metadata on the audio and video data and lets the user dive into analyzing the recorded events. The objective of the system is to facilitate easy navigation of multimodal data and enable the analysis of the recorded situations for the purposes of, for example, behavioral studies, teacher training and business development. A further unique feature of the system is its low setup overhead and high portability as the lightest MORE setup only requires a laptop computer and the selected set of sensors on site.     

Empowering the users? A critical textual analysis of the role of users in open source software development

This paper outlines a critical, textual approach for the analysis of the relationship between different actors in information technology (IT) production, and further concretizes the approach in the analysis of the role of users in the open source software (OSS) development literature. Central concepts of the approach are outlined. The role of users is conceptualized as reader involvement aiming to contribute to the configuration of the reader (to how users and the parameters for their work practices are defined in OSS texts). Afterwards, OSS literature addressing reader involvement is critically reviewed. In OSS context, the OSS writers as readers configure the reader and other readers are assumed to be capable of and interested in commenting the texts. A lack of OSS research on non-technical reader involvement is identified. Furthermore, not only are the OSS readers configured, but so are OSS writers. In OSS context while writers may be empowered, this clearly does not apply to the non-technical OSS readers. Implication for research and practice are discussed.

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Towards estimating computer users＇ mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study （n = 26） and a controlled study （n = 16） were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response （GSR） data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the re- suits of the studies found significant average correlations be- tween mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is pos- sible from interaction behaviour with keyboard and mouse.     

Investigating designers’ and users’ cognitive representations of products to assist inclusive interaction design

There is strong evidence of the importance of good interaction design in the creation of intuitive-use products. However, there is also a strong indication, both in the literature and in the study with designers documented in this paper, that despite this evidence designers get little support in adequately representing, analysing and comparing design and user information. Since designers require a practical and relatively easy-to-use support tool that would enable them to better understand cognitive processes of users and evaluate the accessibility and usability of different product features, this paper proposes the Goals-Actions-Beliefs-Objects (GABO) modelling approach that can form the basis of such a tool for designers. The four distinct stages of the GABO approach are designed to assess and compare designers and users’ understanding and usage of everyday products. The evaluation results of the GABO approach with eight product designers have indicated that designers find it useful and effective in identifying the key similarities and differences in the understanding of designers and users.     

Exploring users’ social responses to computer counseling interviewers’ behavior

We explore the effect of behavioral realism and reciprocal self-disclosure from computer interviewers on the social responses of human users in simulated psychotherapeutic counseling interactions. To investigate this subject, we designed a 3 × 3 factorial between-subjects experiment involving three conditions of behavioral realism: high realism, low realism, and audio-only (displaying no behavior at all) and three conditions of reciprocal self-disclosure: high disclosure, low disclosure, and no disclosure. We measured users’ feelings of social presence (Copresence, Social Attraction, and Emotional Credibility), rapport, perception of the quality of users’ own responses (Embarrassment and Self-Performance), emotional state (PANAS), perception of an interaction partner (Person Perception), self-reported self-disclosure, speech fluency (Pause Fillers and Incomplete Words), and Verbal Self-Disclosure. We observed some contradictory outcomes in users’ subjective reports. However, the results of objective data analysis demonstrated that users disclosed greater Verbal Self-Disclosure (medium level of intimacy) when interacting with computer interviewers that displayed high behavioral realism and high self-disclosure. Users also delivered more fluent speech when interacting with computer interviewers that displayed high behavioral realism.     

Understanding flow experience from users’ requirements

This paper argues that the concept of flow can be useful in gaining a better understanding of information system users’ flow experience. This paper applies this concept in the requirements development phase of an Internet protocol television e-learning service development project. The findings show that flow experience can be visualised and measured, and that there are differences in how users see and perceive different features. Some features are driven by more experiential needs, whereas others are driven by more task-oriented goals. The findings indicate that the users’ perceived flow experiences can already be measured during the early phases of information systems (IS) development projects without having a working prototype of the IS for testing. This enables practitioners to design IS that better facilitate flow experience for the users, which in turn will potentially lead to shortened development time and cost savings for firms. This paper calls for further research in IS development and design methods that take into account the different ways of understanding user experiences, particularly their flow experiences.     

Exploring users’ switching intention and behavior on social networking sites: Linear and nonlinear perspectives

The increasing integration of computers and networks into society has profoundly affected our lives in recent years. In Taiwan, use of SNSs – especially Facebook – has become the main action for the Internet users; accordingly, a number of scholars have explored people's intention to use, or actual use of, SNSs. However, few studies have investigated how and why users stop using one SNS and begin using another from the linear perspective. The findings are incomplete for the human behaviors which are complex and nonlinear systems. The purpose of this study is therefore to explore the relationship between SNS users’ switching intention and switching behavior based on structural equation modeling (SEM) and cusp catastrophe model (CCM). The key factors for the switching intentions and behaviors of SNS users were examined through empirical research, and a structural model was established. The model, which involved habits as a mediating factor, was used to conduct a qualitative and quantitative analysis on the switching behaviors of SNS users from a nonlinear perspective. This model will provide a new method for future researchers to analyze nonlinear behaviors as well as a dynamic decision support model for service providers to improve their business operation.     

Understanding taxi drivers’ routing choices from spatial and social traces

Most of our learning comes from other people or from our own experience. For instance, when a taxi driver is seeking passengers on an unknown road in a large city, what should the driver do? Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows (e.g., hotel taxi rank)? This is an interesting problem that arises everyday in cities all over the world. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood of finding passengers on a road that is new to him (as he has not picked up or dropped off passengers there before). Our observation from large scale taxi driver trace data is that a driver not only learns from his own experience but through interactions with other drivers. In this paper, we first formally define this problem as socialized information learning (SIL), second we propose a framework including a series of models to study how a taxi driver gathers and learns information in an uncertain environment through the use of his social network. Finally, the large scale real life data and empirical experiments confirm that our models are much more effective, efficient and scalable that prior work on this problem.     

Understanding the determinants of users’ subjective well-being in social networking sites: an integration of social capital theory and social presence theory

In order to understand the factors affecting users’ well-being perception derived from social networking sites usage, this study integrates the perspectives of social presence theory and social capital theory to develop a research model. Data were collected from 305 users of Facebook in Taiwan to test the model. The findings reveal that structural capital, relational capital, and cognitive capital exert significant influences on subjective well-being. The results also reveal that structural capital and cognitive capital have positive influence on relational capital, while structural capital influences cognitive capital significantly. The results, on the other hand, show that awareness and affective social presence are the antecedents of structural capital, whereas cognitive social presence and affective social presence influence cognitive capital significantly. Finally, affective social presence is associated with relational capital positively. Theoretical and practical implications are discussed.     

Zipf’s law analysis on the leaked Iranian users’ passwords

Journal of Computer Virology and Hacking Techniques - Textual passwords are one of the most common methods of authentication and an important factor in systems security. Knowing the correct...     

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.     

Wi-Fi based city users’ behaviour analysis for smart city

Monitoring, understanding and predicting city user behaviour (hottest places, trajectories, flows, etc.) is one the major topics in the context of Smart City management. People flow surveillance provides valuable information about city conditions, useful not only for monitoring and controlling the environmental conditions, but also to optimize the deliverying of city services (security, clean, transport,..). In this context, it is mandatory to develop methods and tools for assessing people behaviour in the city. This paper presents a methodology to instrument the city via the placement of Wi-Fi Access Points, AP, and to use them as sensors to capture and understand city user behaviour with a significant precision rate (the understanding of city user behaviour is concretized with the computing of heat-maps, origin destination matrices and predicting user density). The first issue is the positioning of Wi-Fi AP in the city, thus a comparative analyses have been conducted with respect to the real data (i.e., cab traces) of the city of San Francisco. Several different positioning methodologies of APs have been proposed and compared, to minimize the cost of AP installation with the aim of producing the best origin destination matrices. In a second phase, the methodology was adopted to select suitable AP in the city of Florence (Italy), with the aim of observing city users behaviour. The obtained instrumented Firenze Wi-Fi network collected data for 6 months. The data has been analysed with data mining techniques to infer similarity patterns in AP area and related time series. The resulting model has been validated and used for predicting the number of AP accesses that is also related to number of city users. The research work described in this paper has been conducted in the scope of the EC funded Horizon 2020 project Resolute (http://www.resolute-eu.org), for early warning and city resilience.