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SocInfo Tables of Contents: 1011121313w14

Proceedings of the 2011 International Conference on Social Informatics

Fullname:SocInfo 2011: Third International Conference on Social Informatics
Editors:Anwitaman Datta; Stuart Shulman; Baihua Zheng; Shou-De Lin; Aixin Sun; Ee-Peng Lim
Dates:2011-Oct-06 to 2011-Oct-08
Publisher:Springer Berlin Heidelberg
Series:Lecture Notes in Computer Science 6984
Standard No:DOI: 10.1007/978-3-642-24704-0 hcibib: SocInfo11; ISBN: 978-3-642-24703-3 (print), 978-3-642-24704-0 (online)
Links:Online Proceedings | Conference Website
  1. Keynotes
  2. Invited Talks
  3. Network Analysis
  4. eGovernance and Knowledge Management
  5. Applications of Network Analysis
  6. Community Dynamics
  7. Case Studies
  8. Trust, Privacy, and Security
  9. Peer-Production
  10. Posters/Demos
  11. Tutorials


Digital Media and the Relational Revolution in Social Science BIBAFull-Text 1-2
  Michael W. Macy
Social science paradigms are invariably grounded in the available methods of data collection. Beginning with administrative records in the late 19th Century, social scientists have collected stores of data on individual attributes, using surveys and records kept by governments and employers. Individual-level data is also aggregated as population statistics for groups of varying size, from households to nation states, and these data are analyzed using multivariate linear models that require the implausible assumption that the observations are independent, as if each respondent was the sole resident of a small island. In comparison, until recently, we have had very limited data about the interactions between people -- such as influence, sanctioning, exchange, trust, attraction, avoidance, and imitation. Yet social relations and interactions are the foundation of social life. The entities that we most need to learn about are the things about which we know the least. The reason is simple: It is much easier to observe friends than to observe a friendship. Social interactions are fleeting and mostly private -- one needs to be present at precisely the right moment. Moreover, relations are tedious and error-prone to hand-code and record, given the nuances of interaction, the need for repeated observations as relations unfold over time, and the rapid increase in the number of relations as the size of the group increases. As a consequence, studies of social interactions tend to be static, limited to the structures of interaction without regard to content, and based on very small groups. That is why social science has generally been limited mainly to the study of individuals with individual data aggregated for groups and populations. Except in very small groups, social relations have been just too hard to observe.
   All this is rapidly changing as human interactions move increasingly online. Interactions that for the history of humankind have been private and ephemeral in nature now leave a silicon record -- literally footprints in the sand -- in the form of publicly available digital records that allow automatic data collection on an unprecedented scale. However, social scientists have been reluctant to embrace the study of what is often characterized as the "virtual world," as if human interaction somehow becomes metaphysical the moment it is mediated by information technologies. While great care must be exercised in generalizing to the offline world, the digital traces of computer-mediated interactions are unique in human history, providing an exceptional opportunity for research on the dynamics of social interaction, in which individuals influence selected others in response to the influences they receive. In my presentation, I will survey recent studies using digital records of interpersonal interaction to address questions ranging from social inequality to diurnal and seasonal mood changes to the spread of protest in the Arab Spring, including contributions by Rob Claxton, Nathan Eagle, Scott Golder, Jon Kleinberg, Noona Oh, Patrick Park, Michael Siemens, Silvana Toska, and Shaomei Wu.
Using Web Science to Understand and Enable 21st Century Multidimensional Networks BIBAFull-Text 3
  Noshir Contractor
Recent advances in Web Science provide comprehensive digital traces of social actions, interactions, and transactions. These data provide an unprecedented exploratorium to model the socio-technical motivations for creating, maintaining, dissolving, and reconstituting multidimensional social networks. Multidimensional networks include multiple types of nodes (people, documents, datasets, tags, etc.) and multiple types of relationships (co-authorship, citation, web links, etc). Using examples from research in a wide range of activities such as disaster response, public health and massively multiplayer online games, Contractor will argue that Web Science serves as the foundation for the development of social network theories and methods to help advance our ability to understand and enable multidimensional networks.
Data Mining as a Key Enabler of Computational Social Science BIBAFull-Text 4
  Jaideep Srivastava
Observation and analysis of a phenomenon at unprecedented levels of granularity not only furthers our understanding of it, but also transforms the way it is studied. For instance, invention of gene-sequencing and computational analysis transformed the life sciences, creating fields of inquiry such as genomics, proteomics, etc.; and the Hubble space telescope has furthered the ability of humanity to look much farther beyond what we could otherwise. With the mass adoption of the Internet in our daily lives, and the ability to capture high resolution data on its use, we are at the threshold of a fundamental shift not only in our understanding of the social and behavioral sciences (i.e. psychology, sociology, and economics), but also the ways in which we study them. Massively Multiplayer Online Games (MMOGs) and Virtual Worlds (VWs) have become increasingly popular and have communities comprising tens of millions. They serve as unprecedented tools to theorize and empirically model the social and behavioral dynamics of individuals, groups, and networks within large communities. The preceding observation has led to a number of multi-disciplinary projects, involving teams of behavioral scientists and computational scientists, working together to develop novel methods and tools to explore the current limits of behavioral sciences.
   This talk consists of four parts. First, we describe findings from the Virtual World Exploratorium; a multi-institutional, multi-disciplinary project which uses data from commercial MMOGs and VWs to study many fields of social science, including sociology, social psychology, organization theory, group dynamics, macro-economics, etc. Results from investigations into various behavioral sciences will be presented. Second, we provide a survey of new approaches for behavioral informatics that are being developed by multi-disciplinary teams, and their successes. We will also introduce novel tools and techniques that are being used and/or developed as part of this research. Third, we will discuss some novel applications that are not yet there, but are just around the corner, and their associated research issues. Finally, we present commercial applications of Game Analytics research, based on our experiences with a startup company that we've created.
Predicting Market Movements: From Breaking News to Emerging Social Media BIBAFull-Text 5
  Hsinchun Chen
In this talk I will present several studies conducted at the AI Lab of the University of Arizona that aim to understand and predict market movements using text mining, breaking news, and social media.
   In "User-Generated Content on Social Media: Predicting New Product Market Success from Online Word-of-Mouth," we explore the predictive validity of various text and sentiment measures of online WOM for the market success of new products. The context of our study is the Hollywood movie industry where the forecast of movie sales is highly challenging and has started to incorporate online WOM. We first examine the evolvement patterns of online WOM over time, followed by correlation analysis of how various sentiment measures are related to the metrics of new product success. Overall, the number of WOM messages was found to be the most useful predictor of the five new product metrics.
   In "AZ SmartStock: Stock Prediction with Targeted Sentiment and Life Support," we develop a text-based stock prediction engine with targeted sentiment and life support considerations in a real world financial setting. We focus on inter-day trading experiments, with the 5-, 10-, 20-, and 40-day trading windows. We focus on S&P 500 firms in order to minimize the potential illiquid problem associated with thinly traded stocks. News articles from major newswires were extracted from Yahoo! Finance. Life support of a company is extracted from aggregated energy (novelty) of terms used in the news articles where the company is mentioned. The combined Life-Support model was shown to out-perform other models in the 10-day trading window setting.
   In "A Stakeholder Approach to Stock Prediction using Finance Social Media," we utilize firm-related finance web forum discussions for the prediction of stock return and trading of firm stock. Considering forum participants uniformly as shareholders of the firm, suggested by prior studies, and extracting forum-level measures provided little improvement over the baseline set of fundamental and technician variables. Recognizing the true diversity among forum participants, segmenting them into stakeholder groups based upon their interactions in the forum social network and assessing them independently, refined the measures extracted from the forum and improved stock return prediction. The superior performance of the stakeholder-level model represented a statistically significant improvement over the baseline in directional accuracy, and provided an annual return of 44% in simulated trading of firm stock.

Invited Talks

Learning Information Diffusion Models from Observation and Its Application to Behavior Analysis BIBAFull-Text 6
  Hiroshi Motoda
We investigate how well different information diffusion models can explain observation data by learning their parameters and discuss which model is more appropriate to which topic. We use two models, one from push type diffusion (AsIC) and the other from pull type diffusion (AsLT), both of which are extended versions of the well known Independent Cascade (IC) and the Linear Threshold (LT) models that incorporate asynchronous time delay. The model parameters are learned by maximizing the likelihood of the observed data being generated by an EM like iterative search, and the model selection is performed by choosing the one with better predictive power. We first show by using four real networks that the proposed learning algorithm correctly learns the model parameters both accurately and stably, and the proposed selection method identifies the correct diffusion model from which the data are generated. We then apply these methods to behavioral analysis of topic propagation using a real blog diffusion sequence, and show that although the inferred relative diffusion speed and range for each topic is rather insensitive to the model selected, there is a clear indication of which topic to better follow which model. The correspondence between the topic and the model selected is indeed interpretable.
Analysis of Twitter Unfollow: How often Do People Unfollow in Twitter and Why? BIBAFull-Text 7
  Sue Moon
Unfollow in Twitter offers a unique opportunity to researchers to study the dissolution of relationship. We collected daily snapshots of follow relationship of 1.2 million Korean-speaking users for 51 days and their all tweets. From careful statistical analysis, we confirm that unfollow is prevalent and irrelevant to the volume of interaction. We find that other factors such as link reciprocity, tweet burstiness and informativeness are crucial for unfollow decision. We conduct interview with 22 users to supplement the results and figure out motivations behind unfollow behavior. From those quantitative and qualitative research we draw significant implications in both theory and practice. Then we use a multiple logistic regression model to analyze the impacts of the structural and interactional properties on unfollow in Twitter. Our model with 42 dependent variables demonstrates that both structural and interactional properties are important to explain the unfollow behavior. Our findings are consistent with previous literature about multiple dimensions of tie strength in sociology but also add unique aspects of unfollow decision that people appreciate receiving attention rather than giving.

Network Analysis

Robustness of Social Networks: Comparative Results Based on Distance Distributions BIBAFull-Text 8-21
  Paolo Boldi; Marco Rosa; Sebastiano Vigna
Given a social network, which of its nodes have a stronger impact in determining its structure? More formally: which node-removal order has the greatest impact on the network structure? We approach this well-known problem for the first time in a setting that combines both web graphs and social networks, using datasets that are orders of magnitude larger than those appearing in the previous literature, thanks to some recently developed algorithms and software tools that make it possible to approximate accurately the number of reachable pairs and the distribution of distances in a graph. Our experiments highlight deep differences in the structure of social networks and web graphs, show significant limitations of previous experimental results, and at the same time reveal clustering by label propagation as a new and very effective way of locating nodes that are important from a structural viewpoint.
Endogenous Control of DeGroot Learning BIBAKFull-Text 22-35
  Sridhar Mandyam; Usha Sridhar
The DeGroot update cycle for belief learning in social networks models beliefs as convex combinations of older beliefs using a stochastic matrix of social influence weights. In this paper, we explore a new endogenous control scenario for this type of learning, where an agent on her own initiative, adjusts her private social influence to follow another agent, say, one which receives higher attention from other agents, or one with higher beliefs. We develop an algorithm which we refer to as BLIFT, and show that this type of endogenous perturbation of social influence can lead to a 'lifting' or increasing of beliefs of all agents in the network. We show that the per-cycle perturbations produce improved variance contractions on the columns of the stochastic matrix of social influences, resulting in faster convergence, as well as consensus in beliefs. We also show that this may allow belief values to be increased beyond the DeGroot beliefs, which we show are the lower bounds for BLIFT. The result of application of BLIFT is illustrated with a simple synthetic example.
Keywords: DeGroot Model; Belief Learning; Social Networks; Endogenous Control
Mathematical Continuity in Dynamic Social Networks BIBAFull-Text 36-50
  John L. Pfaltz
A rigorous concept of continuity for dynamic networks is developed. It is based on closed, rather than open, sets. It is local in nature, in that if the network change is discontinuous it will be so at a single point and the discontinuity will be apparent in that point's immediate neighborhood. Necessary and sufficient criteria for continuity are provided when the change involves only the addition or deletion of individual nodes or connections (edges). Finally, we show that an effective network process to reduce large networks to their fundamental cycles is continuous.

eGovernance and Knowledge Management

Government 2.0 Collects the Wisdom of Crowds BIBAKFull-Text 51-58
  Taewoo Nam; Djoko Sigit Sayogo
An emerging trend is noteworthy that government agencies tap on citizens' innovative ideas. Government 2.0 -- governmental adoption of Web 2.0 technologies -- enables and empowers citizens to participate in various functions and processes of government such as service provision, information production, and policy making. Government 2.0 is a tool for government to collect the wisdom of crowds, which helps improve service, information, and policy. Crowdsourcing is not only for businesses but is now being implemented in the public sector. Currently government agencies chiefly use four strategies for crowdsourcing: contest, wiki, social networking, and social voting. This paper takes a close look at how government agencies utilize those strategies.
Keywords: Government 2.0; Web 2.0; Crowdsourcing; Wisdom of crowds
Web Searching for Health: Theoretical Foundations for Analyzing Problematic Search Engine Use BIBAKFull-Text 59-66
  Pallavi Rao; Marko M. Skoric
Increasingly, consumers are searching online for health information. This rise in Web searching for health calls for a theoretical approach that explains the problems associated with consumers' use of search engines for health information retrieval. In this context, this paper provides an exploratory framework for understanding problematic search engine use in the context of online health information retrieval. It extends Caplan's (2005) theoretical framework of problematic Internet use by integrating users' cognitive shift in the search process. The framework highlights the cognitive, behavioural and affective symptoms leading to negative outcomes of improper search engine use. Finally, the paper discusses implications of adopting the framework for understanding consumers' search behaviour in health information retrieval.
Keywords: Web Search; Online Health Information Retrieval; Cognitive Shift; Problematic Internet Use; Problematic Search Engine Use
The Role of Trust and ICT Proficiency in Structuring the Cross-Boundary Digital Government Research BIBAFull-Text 67-74
  Djoko Sigit Sayogo; Taewoo Nam; Jing Zhang
This paper aims to ascertain the significant role of trust and communication in structuring the formation of digital government research collaboration. The data shows that trust has prominent role in structuring collaboration manifest in three instances of interpersonal linkages, namely: network closure, reputation, and similarity of country of origin. This study also found that multi-cultural collaboration requires communication medium for richer interpretation and discussion, including online tools. This result suggests that venturing on multi-cultural or cross-boundary collaboration requires well thought-out and carefully planned approach with closeness, interaction, and trust emerge as the major considerations.
Integration and Warehousing of Social Metadata for Search and Assessment of Scientific Knowledge BIBAFull-Text 75-83
  Daniil Mirylenka; Fabio Casati; Maurizio Marchese
With the advancement of Web, novel types of scientific-related data and metadata are emerging from a growing number of various sources. Alongside traditional bibliographic data provided by digital libraries great amounts of social metadata (such as bookmarks, "reads", tags, comments and "likes") are created and accumulated by social networking services. We believe that these metadata can be fruitfully used for improving search and assessment of scientific knowledge. The individual sources of scientific metadata differ largely in their focus, functionality, data coverage and data quality, and are currently limited to their own databases and data types. We suggest that we can enhance the current individual services by integrating their data and metadata. In this paper we discuss the opportunities and challenges of such integration for the purpose of facilitating the discovery and evaluation of scientific knowledge, and present a framework for integration and warehousing of both bibliographic and social scientific metadata.

Applications of Network Analysis

Comparing Linkage Graph and Activity Graph of Online Social Networks BIBAKFull-Text 84-97
  Yuan Yao; Jiufeng Zhou; Lixin Han; Feng Xu; Jian
In the context of online social networks, the linkage graph -- a graph composed of social links -- has been studied for several years, while researchers have recently suggested studying the activity graph of real user interactions. Understanding these two types of graphs is important since different online applications might rely on different underlying structures. In this paper, we first analyze two specific online social networks, one of which stands for a linkage graph and the other for an activity graph. Based on our analysis, we find that the two networks exhibit several static and dynamic properties in common, but show significant difference in degree correlation. This property of degree correlation is further confirmed as a key distinction between these two types of graphs. To further understand this difference, we propose a network generator which could as well capture the other examined properties. Finally, we provide some potential implications of our findings and generator.
Keywords: Linkage Graph; Activity Graph; Online Social Networks; Degree Correlation; Network Generator
Context-Aware Nearest Neighbor Query on Social Networks BIBAFull-Text 98-112
  Yazhe Wang; Baihua Zheng
Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design a hyper-graph based index structure to support approximated CANN search efficiently.
Using Tag Recommendations to Homogenize Folksonomies in Microblogging Environments BIBAFull-Text 113-126
  Eva Zangerle; Wolfgang Gassler; Günther Specht
Microblogging applications such as Twitter are experiencing tremendous success. Twitter users use hashtags to categorize posted messages which aim at bringing order to the chaos of the Twittersphere. However, the percentage of messages including hashtags is very small and the used hashtags are very heterogeneous as hashtags may be chosen freely and may consist of any arbitrary combination of characters. This heterogeneity and the lack of use of hashtags lead to significant drawbacks in regards of the search functionality as messages are not categorized in a homogeneous way. In this paper we present an approach for the recommendation of hashtags suitable for the tweet the user currently enters which aims at creating a more homogeneous set of hashtags. Furthermore, users are encouraged to using hashtags as they are provided with suitable recommendations for hashtags.

Community Dynamics

A Spectral Analysis Approach for Social Media Community Detection BIBAKFull-Text 127-134
  Xuning Tang; Christopher C. Yang; Xiajing Gong
Online forums are ideal platforms for worldwide Internet users to share ideas, raise discussions and disseminate information. It is of great interest to gain a better understanding on the dynamic of user interactions and identify user communities in online forums. In this paper, we propose a temporal coherence analysis approach to detect user communities in online forum. Users are represented by vectors of activeness and communities are extracted by a soft community detection algorithm with the support of spectral analysis.
Keywords: Spectral Analysis; Community Detection; Soft Clustering
Design of a Reputation System Based on Dynamic Coalition Formation BIBAFull-Text 135-144
  Yuan Liu; Jie Zhang; Quanyan Zhu
Reputation systems bear some challenging problems where buyers have different subjectivity in evaluating their experience with sellers and they may not have incentives to share their experience. In this paper, we propose a novel reputation system based on dynamic coalition formation where buyers with similar subjectivity and rich experience will be awarded virtual credits for helping others find trustworthy sellers to successfully conduct business. Our theoretical analysis confirms that the coalitions formed in this way are stable.
Guild Play in MMOGs: Rethinking Common Group Dynamics Models BIBAKFull-Text 145-152
  Muhammad Aurangzeb Ahmad; Zoheb Borbora; Cuihua Shen; Jaideep Srivastava; Dmitri Williams
Humans form groups and congregate into groups for a variety of reasons and in a variety of contexts e.g., corporations in offline space and guilds in Massively Multiplayer Online Games (MMOGs). In recent years a number of models of group formation have been proposed. One such model is Johnson et al's [10] model of group evolution. The model is motivated by commonalities observed in evolution of street gangs in Los Angeles and guilds in an MMOG (World of Warcraft). In this paper we first apply their model to guilds in another MMOG (EQ2)¹ and found results inconsistent from the model's predictions, additionally we found support for the role of homophily in guild formation, which was ruled out in previous results, Alternatively, we explore alternative models for guild formation and evolution in MMOGs by modifying earlier models to account for the existence of previous relationships between people.
Keywords: Guilds; MMOGs; Groups; Models of group evolution
Tadvise: A Twitter Assistant Based on Twitter Lists BIBAKFull-Text 153-160
  Peyman Nasirifard; Conor Hayes
Micro-blogging is yet another dynamic information channel where the user needs assistance to manage incoming and outgoing information streams. In this paper, we present our Twitter assistant called Tadvise that aims to help users to know their followers / communities better. Tadvise recommends well-connected topic-sensitive followers, who may act as hubs for broadcasting a tweet to a larger relevant audience. Each piece of advice given by Tadvise is supported by declarative explanations. Our evaluation shows that Tadvise helps users to know their followers better and also to find better hubs for propagating community-related tweets.
Keywords: Micro-blog; Twitter; People-Tag; Information Sharing

Case Studies

A Case Study of the Effects of Moderator Posts within a Facebook Brand Page BIBAKFull-Text 161-170
  Irena Pletikosa Cvijikj; Florian Michahelles
Social networks have become an additional marketing channel that could be integrated with the traditional ones, such as news and television media, as well as online channels. User participation as a main feature of the social networks imposes challenges to the traditional one-way marketing, resulting in companies experimenting with many different approaches, thus shaping a successful social media approach based on the trial-and-error experiences. Our study analyses the effects of moderator posts characteristics such as post type, category and posting day, on the user interaction in terms of number of comments and likes, and interaction duration for the domain of a sponsored Facebook brand page. Our results show that there is a significant effect of the post type and category on likes and comments (p < 0.0001) as well as on interaction duration (p < 0.01). The posting day has effect only over the comments ratio (p < 0.05). We discuss the implications of our findings for social media marketing.
Keywords: Web mining; Facebook; social media marketing
Cognition or Affect? -- Exploring Information Processing on Facebook BIBAKFull-Text 171-183
  Ksenia Koroleva; Hanna Krasnova; Oliver Günther
Recognizing the increasing amount of information shared on Social Networking Sites (SNS), in this study we aim to explore the information processing strategies of users on Facebook. Specifically, we aim to investigate the impact of various factors on user attitudes towards the posts on their Newsfeed. To collect the data, we program a Facebook application that allows users to evaluate posts in real time. Applying Structural Equation Modeling to a sample of 857 observations we find that it is mostly the affective attitude that shapes user behavior on the network. This attitude, in turn, is mainly determined by the communication intensity between users, overriding comprehensibility of the post and almost neglecting post length and user posting frequency.
Keywords: information processing; cognitive heuristics; attitude; cognitive and affective dimensions; social networking sites; Facebook

Trust, Privacy, and Security

Trend Analysis and Recommendation of Users' Privacy Settings on Social Networking Services BIBAFull-Text 184-197
  Toshikazu Munemasa; Mizuho Iwaihara
Social networking services (SNSs) are regarded as an indispensable social media for finding friends and interacting with them. However, their search capabilities often raise privacy concerns. Usually, an SNS provides privacy settings for each user, so that he/she can specify who can access his/her online contents. But these privacy settings often become either too simplistic or too complicated. To assist SNS users to discover their own appropriate settings, we propose a privacy-setting recommendation system, which utilizes privacy settings on public access, collected from over 66,000 real Facebook users and settings donated by participating users. We show privacy scores of the collected settings according to user categories. Our recommendation system utilizes these analysis results as well as correlations within privacy settings, and visualizes distribution of collected user's settings. Our evaluations on test users show effectiveness of our approach.
Semantics-Enabled Policies for Information Sharing and Protection in the Cloud BIBAKFull-Text 198-211
  Yuh-Jong Hu; Win-Nan Wu; Jiun-Jan Yang
The cloud computing platform provides utility computing allowing people to have convenient and flexible information sharing services on the web. We investigate the inter-disciplinary area of information technology and law and use semantics-enabled policies for modeling legal regulations in the cloud. The semantics-enabled policies of information sharing and protection are represented as a combination of ontologies and rules to capture the concept of security and privacy laws. Ontologies are abstract knowledge representations of information sharing and protection which extracted manually from the data sharing and protection laws. Rules provide further enforcement power after ontologies have been constructed. The emerging challenges of legalizing semantics-enabled policies for laws in the cloud include mitigating the gap between semantics-enabled policy and laws to avoid any ambiguity in the policy representation, and resolving possible conflicts among policies when they are required to integrate the laws from multiple jurisdictions.
Keywords: semantics-enabled policies; information sharing; data protection; national security; cloud computing; privacy for social network cloud
Social Mechanism of Granting Trust Basing on Polish Wikipedia Requests for Adminship BIBAKFull-Text 212-225
  Piotr Turek; Justyna Spychala; Adam Wierzbicki; Piotr Gackowski
The purpose of this paper is the description of research about Polish Wikipedia administrators and their behavior during Request for Adminship votings. Administrator is regarded as trustworthy individual, and thus social aspects of deciding about granting and revoking administrative permissions becomes relevant for the sustained growth of Wikipedia. We have conducted two kinds of experiments: First is gathering of several statistics about current administrators and their contribution to the project. Second experiment is based on an implicit social network created from the edit history and compares contributors' collaborative efforts with the votes actually cast during Request for Adminship procedure.
Keywords: Wikipedia; Collaboration; Trust
Revealing Beliefs Influencing Trust between Members of the Czech Informatics Community BIBAFull-Text 226-239
  Tomáš Knap; Irena Mlýnková
In the project "Social Network of the Computer Scientists in the Regions of the Czech Republic" (SoSIReCR), our aim is to build a social network of Czech informatics community, so that its members can better cooperate and exchange information. In such a social network, the aspect of trust of a member of the informatics community willing to depend on another member is of crucial importance. Unfortunately, trust -- a rather complex concept -- is typically comprehended as a black box and indivisible concept, leading to confusion of the social network members what trust actually is. To minimize that confusion, we choose in this paper a different approach -- trust is comprehended as a set of trusting beliefs (the simpler and more intuitive concepts than trust), such as a belief that a trustee is honest or that (s)he is an expert in the given domain. To identify these beliefs we conduct a survey of the trust literature. Consequently, we select a suitable set of these beliefs relevant for the SoSIReCR project and evaluate the selection process by consulting it (mainly) with the members of the informatics community. We believe that the presented approach is a general promising way to properly define trust in social networking applications.


High-Throughput Crowdsourcing Mechanisms for Complex Tasks BIBAKFull-Text 240-254
  Guido Sautter; Klemens Böhm
Crowdsourcing is popular for large-scale data processing endeavors that require human input. However, working with a large community of users raises new challenges. In particular, both possible misjudgment and dishonesty threaten the quality of the results. Common countermeasures are based on redundancy, giving way to a tradeoff between result quality and throughput. Ideally, measures should (1) maintain high throughput and (2) ensure high result quality at the same time. Existing work on crowdsourcing mostly focuses on result quality, paying little attention to throughput or even to that tradeoff. One reason is that the number of tasks (individual atomic units of work) is usually small. A further problem is that the tasks users work on are small as well. In consequence, existing result-improvement mechanisms do not scale to the number or complexity of tasks that arise, for instance, in proofreading and processing of digitized legacy literature. This paper proposes novel result-improvement mechanisms that (1) are independent of the size and complexity of tasks and (2) allow to trade result quality for throughput to a significant extent. Both mathematical analyses and extensive simulations show the effectiveness of the proposed mechanisms.
Keywords: Crowdsourcing; Data Quality; Throughput
Designing for Motivation: Focusing on Motivational Values in Two Case Studies BIBAKFull-Text 255-268
  Fahri Yetim; Torben Wiedenhoefer; Markus Rohde
This paper presents our investigations in how value sensitive design of interactive systems could motivate people to contribute to semantic web applications. In two case studies we adopted the Value Sensitive Design (VSD) framework (Friedman et al., 2006), relying on three levels of investigation. Conceptual investigation focused on the literature analysis and identified a set of motivational values. Empirical investigation involved understanding the motivations of users within two cases. Finally, technical investigation was conducted to determine design features which may support and facilitate these values. This study illustrates the use of the VSD framework for investigating motivational values and provides a review of design features to support end users' motivation to contribute to public goods.
Keywords: Motivation; value sensitive design; user participation; annotation; case studies
A Bounded Confidence Approach to Understanding User Participation in Peer Production Systems BIBAFull-Text 269-282
  Giovanni Luca Ciampaglia
Commons-based peer production does seem to rest upon a paradox. Although users produce all contents, at the same time participation is commonly on a voluntary basis, and largely incentivized by achievement of project's goals. This means that users have to coordinate their actions and goals, in order to keep themselves from leaving. While this situation is easily explainable for small groups of highly committed, like-minded individuals, little is known about large-scale, heterogeneous projects, such as Wikipedia.
   In this contribution we present a model of peer production in a large online community. The model features a dynamic population of bounded confidence users, and an endogenous process of user departure. Using global sensitivity analysis, we identify the most important parameters affecting the lifespan of user participation. We find that the model presents two distinct regimes, and that the shift between them is governed by the bounded confidence parameter. For low values of this parameter, users depart almost immediately. For high values, however, the model produces a bimodal distribution of user lifespan. These results suggest that user participation to online communities could be explained in terms of group consensus, and provide a novel connection between models of opinion dynamics and commons-based peer production.


Modelling Social Network Evolution BIBAKFull-Text 283-286
  Radoslaw Michalski; Sebastian Palus; Piotr Bródka; Przemyslaw Kazienko; Krzysztof Juszczyszyn
Most of the real social networks extracted from various data sources evolve and change their profile over time. For that reason, there is a great need to model evolution of networks in order to enable complex analyses of theirs dynamics. The model presented in the paper focuses on definition of differences between following network snapshots by means of Graph Differential Tuple.
Keywords: social network evolution; graph distance measures
Towards High-Quality Semantic Entity Detection over Online Forums BIBAFull-Text 287-291
  Juan Du; Weiming Zhang; Peng Cai; Linling Ma; Weining Qian; Aoying Zhou
User-generated content (UGC) implies user-behaviors. Mining on such data helps understanding the relationship between social media and the real world. However, UGC is usually of low quality, which results in the difficulty of semantic entity extraction. In this paper, we propose a method towards high-quality semantic entity refinement on forums by employing external resources. Experiments on real-life Chinese online forums show the effectiveness of our method.
"I'm Not an Alcoholic, I'm Australian": An Exploration of Alcohol Discourse in Facebook Groups BIBAFull-Text 292-295
  Sarah Posner; Dennis Wollersheim
This paper discusses alcohol discourse characteristics in alcohol related Facebook groups, through a discourse analysis of their wall posts and discussion sections. We created an analytical framework to analyse the content. Our findings on alcohol culture and binge drinking were similar to that stated in existing literature. This study raises important questions about how online discussions on alcohol are creating unhealthy online drinking communities, and how this impacts actual drinking patterns.
Impact of Expertise, Social Cohesiveness and Team Repetition for Academic Team Recommendation BIBAKFull-Text 296-299
  Anthony Ventresque; Jackson Tan Teck Yong; Anwitaman Datta
Forming multidisciplinary teams is a key to carry out complex tasks, which is increasingly the case higher up in the knowledge value chain. In this paper, we study academic teams, by proposing a representation of the information available from various data sources, through (i) competence, (ii) social and (iii) team networks. Each of these projections of the interactions between individuals and concepts have specific characteristics. We then empirically evaluate the impact of these notions on team formation process. The objective is to guide team recommendation systems design.
Keywords: Team Recommendation; Expertise; Cohesiveness; Team Repetition
CEO's Apology in Twitter: A Case Study of the Fake Beef Labeling Incident by E-Mart BIBAKFull-Text 300-303
  Jaram Park; Hoh Kim; Meeyoung Cha; Jaeseung Jeong
We present a preliminary study on how followers and non-followers of a popular CEO respond differently to a public apology by the CEO in Twitter. Sentiment analysis tool was used to measure the effect of the apology. We find that CEO's apology had clear benefits in this case. As expected, it was more effective to followers than non-followers. However, followers showed a higher degree of change in both positive and negative sentiments. We also find that negative sentiments have stronger dynamics than positive sentiments, in terms of the degree of change. We provide insights on the potential for efficient crisis communication in online social media and we discuss future research agenda.
Keywords: Twitter; Apology; Corporate mistakes; Sentiment analysis
GViewer: GPU-Accelerated Graph Visualization and Mining BIBAFull-Text 304-307
  Jianlong Zhong; Bingsheng He
Visualization is an effective way of identifying the patterns of interests (such as communities) in graphs including social networks and Web [8,6]. There have been a number of tools developed for graph visualizations, e.g., Tulip, Gephi and GMine [8]. All of these tools use the CPU as the main power to calculate the graph layouts for visualization, such as force-directed layout [2]. However, the layout calculation is usually computation intensive, for example, the force-directed layout has the complexity of O(N³), where N is the number of vertexes in the graph. In our experiments, the CPU-based solution takes more than half one hours on the CPU to layout a graph with 14.5 thousand vertexes.
Sharing Scientific Knowledge with Knowledge Spaces BIBAKFull-Text 308-311
  Marcos Baez; Fabio Casati; Maurizio Marchese
This paper presents a set of models and an extensible social web platform (namely, Knowledge Spaces) that supports novel and agile social scientific dissemination processes. Knowledge Spaces is based on a model for structured, evolving, and multi-facet scientific resources that allows the representation of structured, evolving, and multi-facet scientific knowledge and meta-knowledge, of effective "viral" algorithms for helping scientists find the knowledge they need, and of interaction metaphors that facilitate its usage.
Keywords: knowledge dissemination; social web; scientific publications
Analysis of Multiplayer Platform Users Activity Based on the Virtual and Real Time Dimension BIBAKFull-Text 312-315
  Jaroslaw Jankowski
The paper proposes an approach to modelling the behaviour and segmentation of online multiplayer systems' users, based on frequency and patterns of visits. The results presented are based on the analysis of time series both in real and virtual time, with the objective to quantitatively capture the characteristics of usage of online multiplayer platforms.
Keywords: multiplayer platforms; time series analysis; web users' behavior
Tracking Group Evolution in Social Networks BIBAKFull-Text 316-319
  Piotr Bródka; Stanislaw Saganowski; Przemyslaw Kazienko
Easy access and vast amount of data, especially from long period of time, allows to divide social network into timeframes and create temporal social network. Such network enables to analyse its dynamics. One aspect of the dynamics is analysis of social communities evolution, i.e., how particular group changes over time. To do so, the complete group evolution history is needed. That is why in this paper the new method for group evolution extraction called GED is presented.
Keywords: social network; community evolution; GED
Gathering in Digital Spaces: Exploring Topical Communities on Twitter BIBAKFull-Text 320-323
  Cate Huston; Michael Weiss
On Twitter, hashtags allow users to gather around a topic in a digital space, something that has been common since early IRC and internet chat rooms. However there are three important differences when gathering on Twitter: persistence, invitation, and device independence. In this paper, we search for patterns in these digital spaces through the use of visualization to explore the temporal rhythms that emerge.
Keywords: social networking; communities; visualization; twitter
"Eco-MAME": Ecology Activity Promotion System Based on Human Psychological Characteristics BIBAKFull-Text 324-327
  Rie Tanaka; Shinichi Doi; Taku Konishi; Naoki Yoshinaga; Satoko Itaya; Keiji Yamada
This study addresses constructing an activity promotion system called "Eco-MAME: Ecological platform for Motivating Activities with Mutual Effect" to let people start to carry out or continue environmentally conscious activities in local communities. We use a major psychological model for explaining the factors of intention for ecological behaviors and focus on people who already have intentions for goals but don't have intentions for execution, namely, people who cannot carry out activities even though they understand their importance. The Eco-MAME system visualizes one's own and others' activities to activate factors of intention. We emphasize showing activities of others to activate factors related to social norms or responsibility. We implemented the proposed system as a web site to conduct a local experiment, and the result showed that the more the user viewed the page the more he/she reduced the usage of the energy, namely, carry out more activities.
Keywords: Activity Promotion; Visualization; Personalization
SPLASH: Blending Gaming and Content Sharing in a Location-Based Mobile Application BIBAKFull-Text 328-331
  Dion Hoe-Lian Goh; Chei Sian Lee; Alton Y. K. Chua; Khasfariyati Razikin; Keng-Tiong Tan
In this demonstration, we introduce SPLASH (Seek, PLAy, SHare), a mobile application which blends gaming with content sharing and socializing activities. SPLASH is a human computation game that generates location-based content as a byproduct of gameplay. The entertainment derived from gameplay is harnessed to motivate users to contribute content. A detailed description of the features in SPLASH and its distinctive characteristics will also be presented.
Keywords: Mobile application; content sharing; human computation game
An Interactive Social Boarding System Using Home Infotainment Platform BIBAKFull-Text 332-337
  Sounak Dey; Avik Ghose
The authors propose a customer interactive and connected boarder experience for the hospitality industry using the in-room TV set and a Home Infotainment Platform (HIP) connected to the same. This aims at building an interactive and social platform for interacting with the Hotel services and facilities. It also facilitates the replacement of the EPABX in the room with more interactive means for providing services including but not restricted to restaurant table booking, conference room booking, wake-up call service and external communications. This will lead to a more cost-effective deployment of services with a richer feature-set.
Keywords: Home Infotainment Platform; VOIP; Social Network; TV; Hospitality


From Computational to Human Trust: Problems, Methods and Applications of Trust Management BIBAFull-Text 338
  Adam Wierzbicki
The tutorial will be devoted to trust management mechanisms and their practical applications, such as reputation systems for online auctions and recommendation systems.
Text Analytics for Social Research BIBAKFull-Text 339
  Stuart W. Shulman
This tutorial provides software training in "DiscoverText," which is text analytic software developed by Professor Shulman. His work advances text mining and natural language processing research. The training links these worlds via straightforward and easy to understand explanations of software features that can be tailored for all experience levels and industries.
Keywords: software; text analytics; archiving; classification; metadata