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This work investigates how to bridge the gap between offline and online behaviors at a conference and how the physical resources in the conference (the physical objects used in the conference for gathering attendees together in engaging an activity such as rooms, sessions, and papers) can be used to help facilitate social networking. We build Find and Connect, a system that integrates offline activities and interactions captured in real time with online connections in a conference environment, to provide a list of potential people one should connect to for forming an ephemeral social network. We investigate how social connections can be established and integrated with physical resources through positioning technology, and the relationship between physical proximity encounters and online social connections. Results from our two datasets of two trials, one at the UIC/ATC 2010 conference and GCJK internal marketing event, show that social connections that are reciprocal in relationship, such as friendship and exchanged contacts, have tighter, denser, and highly clustered networks compared to unidirectional relationships such as follow. We discover that there is a positive relationship between physical proximity encounters and online social connections before the social connection is made for friends, but a negative relationship for after the social connection is made. The first indicates social selection is strong, and the second indicates social influence is weak. Even though our dataset is sparse, nonetheless we believe our work is promising and novel which is worthy of future research.

We study the extent to which social ties between people can be inferred in large social network, in particular via active user interactions. In most online social networks, relationships are lack of meaning labels (e.g., "colleague" and "intimate friends") due to various reasons. Understanding the formation of different types of social relationships can provide us insights into the micro-level dynamics of the social network. In this work, we precisely define the problem of inferring social ties and propose a Partially-Labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social relationships. The model formalizes the problem of inferring social ties into a flexible semi-supervised framework. We test the model on three different genres of data sets and demonstrate its effectiveness. We further study how to leverage user interactions to help improve the inferring accuracy. Two active learning algorithms are proposed to actively select relationships to query users for their labels. Experimental results show that with only a few user corrections, the accuracy of inferring social ties can be significantly improved. Finally, to scale the model to handle real large networks, a distributed learning algorithm has been developed.

Web 2.0 tools in general, and Web video in particular, provide new ways for activists to express their viewpoints to a broad audience. In this paper we deployed tools that have been used to find subgroups automatically in social networks and applied them to the problem of distinguishing between two sides of a controversial issue based on patterns of online interaction. We explored the problem of distinguishing between anti- and pro-vaccination activists based on a social network of videos and associated comments posted on YouTube. Videos for the analysis were selected by submitting the term “vaccination” to a search on YouTube. A content analysis of the selected videos was then performed (Keelan et al, 2007) to classify videos as pro- or anti-vaccination. Then, a modified version of the SCAN method (Chin and Chignell, 2008) for identifying cohesive subgroups in social networks was applied to the social network inferred from the discussions about the videos. Results showed that a cohesive subgroup of anti-vaccination people existed in discussions around anti-vaccination videos, whereas discussions around pro-vaccination videos included both anti-vaccination and pro-vaccination people. Implications of the method and results for more general delineation of types of medical activism and the opposing camps within those camps are discussed.

As a first step in the development of community trackers for large-scale online interaction, this paper shows how cohesive subgroup analysis using the Social Cohesion Analysis of Networks (SCAN; Chin and Chignell 2008) and Data-Intensive Socially Similar Evolving Community Tracker (DISSECT; Chin and Chignell 2010) methods can be applied to the problem of identifying cohesive subgroups and tracking them over time. Three case studies are reported, and the findings are used to evaluate how well the SCAN and DISSECT methods work for different types of data. In the largest of the case studies, variations in temporal cohesiveness are identified across a set of subgroups extracted from the inferred social network. Further modifications to the DISSECT methodology are suggested based on the results obtained. The paper concludes with recommendations concerning further research that would be beneficial in addressing the community tracking problem for online data.

The problem of identifying cohesive subgroups in social hypertext is reviewed. A computationally efficient three-step framework for identifying cohesive subgroups is proposed, referred to as the Social Cohesion Analysis of Networks (SCAN) method. In the first step of this method (Select), people within a social network are screened using a level of network centrality to select possible subgroup members. In the second step (Collect), the people selected in the first step are collected into subgroups identified at each point in time using hierarchical cluster analysis. In the third step (Choose), similarity modeling is used to choose cohesive subgroups based on the similarity of subgroups when compared across different points in time. The application of this SCAN method is then demonstrated in a case study where a subgroup is automatically extracted from a social network formed based on the online interactions of a group of about 150 people that occurred over a two-year period. In addition, this paper also demonstrates that similarity-based cohesion can provide a different, and in this case more compelling, subgroup representation than a method based on splitting a hierarchical clustering dendrogram using an optimality criterion.

International audience; With the development of mobile sensing and mobile social networking techniques, mobile crowd sensing and computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale sensing, has become a leading paradigm. Built on top of the participatory sensing vision, MCSC has two characteristic features: it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and it presents the fusion of human and machine intelligence in both the sensing and computing processes. This article characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data

Many people share photos and their locations using applications such as Foursquare, Facebook and Twitter when attending an event or activity, as a means for promoting an activity, for conversation and as an archive to create a sense of community. People find events to attend using applications such as Event Brite and Meet Up, and exchange contact information using applications such as Whats App or We Chat. The problem exists in that the content, contact and context captured in the activity are managed by separate applications, which makes it cumbersome to find and share. How can we record, share and connect easily in the offline activity to relive that activity experience online? We create an application called LeXiang to allow users to easily create ad-hoc activities in real time, record all the participants that actually attended, and record and share content from the activity for reliving the experience. We explain our concept of an ephemeral social network, study how people find and record activities, and then describe our user interface to achieve this.

Online social networks provide us an unprecedented volume of available data, which results from the pervasive adoption of online social applications. In particular, for the unique characteristics on promoting content sharing, microblogging social networks offer us a new proxy for detecting and tracking the events being taken place in the real world. In spite of large amount of social babble involved, the microblog data contains fresh news coming from human sensors at a humungous rate. As the online social network is a platform that is able to process fast changing streaming data, however it is hard to discover meaningful events in such noisy circumstances in time. In this paper, we study the keywords determining problem in event detection and propose a novel and much more effective method for discovering bursty words in microblogging social networks by leveraging temporal dynamics information. Based on this, we propose an efficient event detection framework applied in Sina Weibo---a Chinese microblogging site similar to Twitter. With experiments conducted on real data sourced from Sina Weibo, we show the effectiveness and feasibility of our proposed method and framework.

People that we encounter and connect with at conferences, provide opportunities for extending our social networks from offline to online. How can users easily find and connect with the relevant people in the conference? We present Find & Connect, an application that combines proximity and homophily information. We conducted a user study and deployment of our application at the UbiComp 2011 conference, and discovered that most users found it useful for finding nearby people and making new connections. Studying the offline and online user behavior reveal that certain sessions are more conducive to greater offline and online interactions, and that users add contacts primarily due to proximity (i.e. they know each other in real life and have encountered before). These results can form the stepping stone to creating a user behavior model for offline and online.

Location-based mobile applications such as Foursquare help bridge the gap between offline and online. People that we encounter and connect with, around physical resources such as conferences, provide opportunities for extending our social networks from offline to online. We call these proximity-based networks that revolve around encounters and activities ephemeral social networks because they are created at a specific point in time for a specific duration at a specific event. Ephemeral social networking is the next evolution of mobile social networking, which aims to help us to connect with and recall the people we meet in our daily lives. However, there are many questions that need to be answered. What are the characteristics of ephemeral social networks? How to record and identify an ephemeral social network? In this chapter, we explain the theory behind ephemeral social networks, and create a platform called Find & Connect to show its properties. We then describe our application and system for connecting offline to online, then finally study the influence on user behavior of offline on online and vice versa by deploying Find & Connect at three conference events.

Social networking sites (SNS) such as Facebook and Twitter are popular for finding, promoting and attending offline events like meetings, concerts and parties. Much work has looked into characterizing these social networks and their user behavior, but less has been explored about the events. With the emergence of event and activity-based applications such as Facebook Events, Google+ Events, EventBrite, Meet up, and Douban Events (a similar service in China), we can connect with other people online and meet them offline. In this paper, we study the user behavior around people attending offline events which are promoted online using Douban. We present results around the event properties, user behavior around the event, and social influence to an event. Our results show that event distribution by participants and wishers follow the typical power-law distribution of social networks, most users attend or like short events that last several days, and that there is an exponential relationship between follow probability and number of common events attended between two users and a linear relationship for common events interested in. All these findings can ultimately help to provide a better understanding on how SNS could affect user behavior in events, and provide guidelines on how to improve the design of event-based applications, particularly for mobile social network services.

Participating in social events or activities in the physical world is an important way for us to make new friends and build social networks. We aim to explore the role of event size and interactivity in affecting social networking behaviors. In this paper, we obtained data from an event-based social network site and conducted a quantitative analysis that reveals a relationship between online following behavior and characteristics of real-world events. We also employ behavior setting theory, social role theory, and user interview data to help us understand the quantitative results. Our finding that small events on average promote more new connections between individuals than large events has important implications for event organizers, event participants, and social media designers.

Social networking sites (SNS) such as Facebook and Twitter are becoming popular forms for finding, promoting and attending offline events and activities. Much work has looked into characterizing these social networks and their user behavior. With the emergence of event and activity-based applications such as Facebook Events, Linked In Events, Xinghui, Zaizher, and Douban, it is easier to connect with other people online and meet them offline. However, few have looked into analyzing these offline events and activities that are shared online. This paper seeks to gain insights into the user behavior around people attending offline events which are promoted online. By studying the events in Douban, we present results around the event properties, user behavior of participants and wishers to an event, and social influence to an event. We show that event distribution by participants and wishers follow the typical power-law distribution, most users attend or like short events that last several days or regular events that last less than 3 months, participants attend an event within one day after the publish time, and that there is an exponential relationship between follow probability and number of common events attended between two users and a linear relationship for common events interested in. These findings provide a better understanding on how SNS could affect user behavior in attending events, and provide guidelines on how to improve the design of event-based applications.

This paper studies the encounter patterns and online connections of 76 users in an indoor location sharing and online interaction social network application, to seek underlying relationship between users' offline behaviors and online social behaviors. We create an indoor location and proximity-based mobile social network system called Find & Connect that collects and records current indoor location of users and then enables users to generate and maintain their online social relationships. We obtain quantitative data which presents properties of users' online behaviors and offline behaviors, and then perform a user study to verify the utility of Find & Connect applied in large meeting events. Using quantified parameters, particularly, encounters regarded as representation of offline behaviors, and exchange contacts and follow as main kinds of online behaviors, our study shows a positive effect by offline behaviors on online social behavior, while a negative effect by online social connection establishing on afterwards offline social interaction. We also find that according to different kinds of online behaviors, their correlation with offline interaction resembles but has diversity in intensity.

In online social networks such as Face book, people receive friend recommendations that are based usually on common friends or similar profile such as having the same interest or coming from the same company. However, people receive friend spam in which they do not know why they should add this friend. If we can record the physical context then we can determine how you met that person, and use that for recommending that person to you. In this paper, we create a friend recommendation system using proximity encounters and meetings as physical context called Encounter Meet. We conduct a user study to examine whether physical context-based friend recommendations is better than common friends. Results show that on average, the Encounter Meet algorithm recommended more friends to participants that they added and more recommendations were ranked as good, compared with the common friend algorithm. The results can be used to help design context-aware recommendations in physical environments.

With the rising popularity of social networks, people's locations are being used for providing rich mobile social services. We present a mobile social service in our office environment called Find & Connect. We use WiFi to record a user's position and allow users to efficiently find, reserve and manage office resources, like meeting rooms and desks, and easily connect to other colleagues through scheduled interactions like having a meeting and/or unscheduled yet implicit interactions like ephemeral encounters. We then describe how we manage office resources and connect with people, followed by a user study that we conducted in our office.

Social networks based on free plus premium (Freemium) business model offer basic services for free, while charging premium for advanced features with added value to a small subset of users. While a large loyal user base is indispensable, social connections and interactions with people paying for premium services may, to a great extent, influence the probability of free users to become pay users. We perform an empirical study on the probability of being a pay user under the social influence of pay users in the 59 million friend pairs of Flickr and 61 million friend pairs of Last. FM users, from the influence of pay users in the 40 million Flickr photo favorite interactions and from the influence of pay users in the 59 million Flickr photo comment interactions. With the same number of pay users in the three types of social influence, we find that influence from pay users in one's friend circle is overwhelmingly higher, followed by that from pay users in photo favorite interactions and that from pay users in photo comment interactions. The greater the number of pay users in the three types of influence, the more likely that a user would be a pay user, however there seems to be a limit upon which no additional benefit is reaped. The results can be used to help freemium-based social networks improve and promote the premium features, to attract more users to pay for it.

With growing popularity of social networks, people’s locations are being used for providing rich mobile social services, mostly with outdoor positioning using GPS. We present a mobile social service deployed in an indoor office environment called “Nokia Find & Connect” (NF&C). In NF&C, employees’ locations are tracked using Wi-Fi to efficiently find, reserve and manage office resources like meeting rooms and desks and to easily connect other colleagues through scheduled interactions like having a meeting together and/or unscheduled yet implicit interactions like ephemeral encounters between two people. We present the user interface and functional architecture of NF&C and discuss a user study that we conducted in our office.

With the ubiquity of mobile devices, GPS and WiFi, location-based social networking services are developing rapidly. The rise of services such as Foursquare and Google Buzz, enable users to instantaneously in real-time report their activity to online social networks around the places they visit. The people that we encounter and connect with around physical resources such as meetings, provide opportunities for extending our social networks which are rarely captured. In this paper, we designed and deployed an indoor location-based resource management and social networking system called Nokia Find & Connect for managing workplace resources and enabling social networking in office environments. Central to our system is the concept of an ephemeral social network, temporary networks that are created for a specific time and event like a meeting. We carried out a trial to study the impact of our system on managing office resources and creating ephemeral social networks. Results show that Nokia Find & Connect inspired people to make new friends and provided great convenience when people book or are about to have a meeting.

This chapter examines the problem of tracking community in social networks inferred from online interactions by tracking evolution of known subgroups over time. Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. A variety of approaches have been suggested to address this problem and the corresponding research literature on centrality, clustering, and optimization methods for finding subgroupings is reviewed. This review will include a critical analysis of the limitations of past approaches. The focus of the chapter will then turn to novel methods for tracking online community interaction. First, the method proposed by Chin and Chignell called SCAN will be briefly introduced, where a combination of heuristic methods is used to identify subgroups in a manner that can potentially scale up to very large social networks. Then, we present the DISSECT method where multiple known subgroups within a social network are tracked in terms of similarity-based cohesiveness over time. The DISSECT method relies on cluster analysis of snapshots of network activity at different points in time followed by similarity analysis of subgroup evolution over successive time periods. The DISSECT method can be supplemented with behavioral measures of sense of community where administration of a questionnaire is feasible. Finally, we conclude the chapter with a discussion on possible applications and use of the DISSECT method.

The mobile phone can be used as a ubiquitous internet device to help users connect with the relevant people instantaneously to grow their social networks and provide quality friendship. In this paper, we present usage statistics from Nokia Friend View and identify its relevant users, by describing and applying our SCAN method adapted from \cite{chin2008} for finding cohesive subgroups to the friend and interaction networks. Our results show that the connection patterns of the top 10 relevant users in the interaction network reflect the friend relationships of those users, and that members of cohesive subgroups send more messages and have more friends than others in the interaction network. These results can be used to improve friend recommendations, search, and user interaction.

Web pages can be modeled as nodes in a social network, and hyperlinks between pages form links (relationships) between the nodes. Links may take the form of comments, for example on blogs, creating explicit connections between authors and readers. In this paper, we describe a novel methodology and framework for identifying subcommunities as cohesive subgroups of n-cliques and k-plexes within social hypertext. We apply our methodology to a group of computer technologists in Toronto called TorCamp who communicate using a Google group. K-plex analysis is then used to identify a group of people that forms a subcommunity within the larger community. The results are then validated against the experienced sense of community of people inside and outside the subcommunity. Statistically significant differences in experienced sense of community are found, with people within the subcommunity showing higher levels of perceived influence and emotional connection.

Blogging has become the newest communication medium for creating a virtual community, a set of blogs linking back and forth to one another's postings, while discussing common topics. In this paper, we examine how communities can be discovered through interconnected blogs as a form of social hypertext [14]. We propose a method and model that detects structures of community in the social network of blogs by integrating McMillan and Chavis' sense of community [26] along with network analysis [8, 11]. From the model, we measure community in the blogs by aligning centrality measures from social network analysis [17] with measures of sense of community obtained using behavioural surveys. We then illustrate the use of this approach with a case study built around an independent music blog. The strength of community measures were found to be well aligned with the network structure, based on centrality measures. Even though the sample size from the case study was small, once the structure and measure of communities are calibrated according to our social hypertext model, communities can be automatically found and measured for other blogs without the need for behavioural surveys.

Social network services (SNSs) are developing at an explosive speed, which makes it easy for people to be closely connected. As a result, a new SNS type, ephemeral social network (ESN), is emerging to capture the ephemeral interactions and meetings that occur in environments such as conferences and workplaces. Most ESNs require the positioning for capturing the physical proximity between people, which impacts much the scalability, privacy protection, and the cost of the system. In this paper, we propose a scalable location-sensing model based on RFID-sensing architecture for ESN in consideration of four aspects of requirements, that is, the usability, QoS, scalability, and privacy. The model includes the perspectives of the privacy, architecture, deployment, and positioning algorithms, which can meet the four key requirements. A prototyping conference-type ESN system was also developed in this paper and was tested in the ACM UbiComp 2011 conference, which verified that the model works well with good scalability, low cost, and customized privacy protection.

We examine the problem of identifying, measuring and evaluating communities in blogs. In our approach, we use sense of community (McMillan and Chavis, 1986) and centrality measures derived from social network analysis. We then describe a method for identifying communities in blogs using both sense of community measurement and Social Network Analysis (SNA). We apply this framework to a blog on Canadian independent music. Hypotheses are developed concerning the specific structural properties of social networks formed by blogging relationships and how those properties are related to the sense of community experienced by the participants (authors and readers of the blogs). Finally, we discuss the implications for blog implementation and use.