Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.95
Yair Diaz-Tellez, E. Bodanese, F. El-Moussa, T. Dimitrakos
A mechanism that takes into account the combination of security requirements from independent administrative entities over a set of interacting resources on a smart device requires the ability to provide some sort of execution context control. The proposed framework consists of an architecture and a policy model. The architecture detects different events and activities (i.e. user, system, applications) and based on them enforces applicable policies and constrains the execution context for a given set of resources. The policy model offers a method to dynamically create a secure execution context by combining different types of policies (e.g. access, usage, execution) issued by different entities on protected resources.
{"title":"Secure Execution Context Enforcement Framework Based on Activity Detection on Data and Applications Hosted on Smart Devices","authors":"Yair Diaz-Tellez, E. Bodanese, F. El-Moussa, T. Dimitrakos","doi":"10.1109/SocialCom.2013.95","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.95","url":null,"abstract":"A mechanism that takes into account the combination of security requirements from independent administrative entities over a set of interacting resources on a smart device requires the ability to provide some sort of execution context control. The proposed framework consists of an architecture and a policy model. The architecture detects different events and activities (i.e. user, system, applications) and based on them enforces applicable policies and constrains the execution context for a given set of resources. The policy model offers a method to dynamically create a secure execution context by combining different types of policies (e.g. access, usage, execution) issued by different entities on protected resources.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115037969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.147
Weizhong Zhao, M. VenkataSwamy, Gang Chen, Xiaowei Xu
Similarity search is a key function for many applications including databases, pattern recognition and recommendation systems to name a few. In this paper, we first propose ε-query, a similarity search based on the popular cosine similarity for information retrieval and social network analysis. In contrast to traditional similarity search ε-query returns results whose cosine similarities with the query are larger than a threshold ε. The major contribution of this paper is an efficient ε-query processing algorithm by using an upper bound for binary data. Our evaluation using two of the largest publicly available real datasets, ClueWeb09 and Twitter, demonstrated that the proposed method could achieve several orders of magnitude speedup in comparison with the traditional approach. Last but not least, we applied the proposed method for information retrieval from ClueWeb and finding community structures from Twitter. The outcome further proved the effectiveness of the proposed method.
{"title":"Fast Information Retrieval and Social Network Mining via Cosine Similarity Upper Bound","authors":"Weizhong Zhao, M. VenkataSwamy, Gang Chen, Xiaowei Xu","doi":"10.1109/SocialCom.2013.147","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.147","url":null,"abstract":"Similarity search is a key function for many applications including databases, pattern recognition and recommendation systems to name a few. In this paper, we first propose ε-query, a similarity search based on the popular cosine similarity for information retrieval and social network analysis. In contrast to traditional similarity search ε-query returns results whose cosine similarities with the query are larger than a threshold ε. The major contribution of this paper is an efficient ε-query processing algorithm by using an upper bound for binary data. Our evaluation using two of the largest publicly available real datasets, ClueWeb09 and Twitter, demonstrated that the proposed method could achieve several orders of magnitude speedup in comparison with the traditional approach. Last but not least, we applied the proposed method for information retrieval from ClueWeb and finding community structures from Twitter. The outcome further proved the effectiveness of the proposed method.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115523223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.173
G. Nadarajan, Cheng-Lin Yang, Y. Chen-Burger, Yu-Jung Cheng, S. Lin, Fang-Pang Lin
We present data collection and storage utilities and a workflow management system for handling the processing of large volumes of videos collected from an ecological source over several years and still growing. They lie in the heart of an integrated system that brings together expertise from various disciplines, including marine science, image processing, high performance computing and user interface. A real-time data streaming architecture was developed for efficient collection and storage of videos. In the analysis part, a workflow management system with two main components was deployed, i) a workflow engine and ii) a workflow monitor. The workflow engine deals with on-demand user queries and batch queries, selection of suitable computing platform and invocation of optimal software modules, while the workflow monitor handles the seamless execution and intelligent error handling of workflow jobs on a heterogeneous computing platform. We discuss the challenges that lie ahead for the workflow system such as the demand for more sophisticated scheduling and monitoring.
{"title":"Real-Time Data Streaming Architecture and Intelligent Workflow Management for Processing Massive Ecological Videos","authors":"G. Nadarajan, Cheng-Lin Yang, Y. Chen-Burger, Yu-Jung Cheng, S. Lin, Fang-Pang Lin","doi":"10.1109/SocialCom.2013.173","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.173","url":null,"abstract":"We present data collection and storage utilities and a workflow management system for handling the processing of large volumes of videos collected from an ecological source over several years and still growing. They lie in the heart of an integrated system that brings together expertise from various disciplines, including marine science, image processing, high performance computing and user interface. A real-time data streaming architecture was developed for efficient collection and storage of videos. In the analysis part, a workflow management system with two main components was deployed, i) a workflow engine and ii) a workflow monitor. The workflow engine deals with on-demand user queries and batch queries, selection of suitable computing platform and invocation of optimal software modules, while the workflow monitor handles the seamless execution and intelligent error handling of workflow jobs on a heterogeneous computing platform. We discuss the challenges that lie ahead for the workflow system such as the demand for more sophisticated scheduling and monitoring.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122723620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.9
Sujoy Sikdar, Byungkyu Kang, J. O'Donovan, Tobias Höllerer, Sibel Adali
Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibility benchmark. As a result, it is hard to compare different studies and generalize from their findings. In this paper, we argue for a methodology for making such studies more useful to the research community. First, the underlying ground truth values of credibility must be reliable. The specific constructs used to define credibility must be carefully defined. Secondly, the underlying network context must be quantified and documented. To illustrate these two points, we conduct a unique credibility study of two different data sets on the same topic, but with different network characteristics. We also conduct two different user surveys, and construct two additional indicators of credibility based on retweet behavior. Through a detailed statistical study, we first show that survey based methods can be extremely noisy and results may vary greatly from survey to survey. However, by combining such methods with retweet behavior, we can incorporate two signals that are noisy but uncorrelated, resulting in ground truth measures that can be predicted with high accuracy and are stable across different data sets and survey methods. Newsworthiness of tweets can be a useful frame for specific applications, but it is not necessary for achieving reliable credibility ground truth measurements.
{"title":"Understanding Information Credibility on Twitter","authors":"Sujoy Sikdar, Byungkyu Kang, J. O'Donovan, Tobias Höllerer, Sibel Adali","doi":"10.1109/SocialCom.2013.9","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.9","url":null,"abstract":"Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibility benchmark. As a result, it is hard to compare different studies and generalize from their findings. In this paper, we argue for a methodology for making such studies more useful to the research community. First, the underlying ground truth values of credibility must be reliable. The specific constructs used to define credibility must be carefully defined. Secondly, the underlying network context must be quantified and documented. To illustrate these two points, we conduct a unique credibility study of two different data sets on the same topic, but with different network characteristics. We also conduct two different user surveys, and construct two additional indicators of credibility based on retweet behavior. Through a detailed statistical study, we first show that survey based methods can be extremely noisy and results may vary greatly from survey to survey. However, by combining such methods with retweet behavior, we can incorporate two signals that are noisy but uncorrelated, resulting in ground truth measures that can be predicted with high accuracy and are stable across different data sets and survey methods. Newsworthiness of tweets can be a useful frame for specific applications, but it is not necessary for achieving reliable credibility ground truth measurements.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125377406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.39
Tobias Baur, Ionut Damian, Patrick Gebhard, K. Porayska-Pomsta, E. André
This paper presents an approach that makes use of a virtual character and social signal processing techniques to create an immersive job interview simulation environment. In this environment, the virtual character plays the role of a recruiter which reacts and adapts to the user's behavior thanks to a component for the automatic recognition of social cues (conscious or unconscious behavioral patterns). The social cues pertinent to job interviews have been identified using a knowledge elicitation study with real job seekers. Finally, we present two user studies to investigate the feasibility of the proposed approach as well as the impact of such a system on users.
{"title":"A Job Interview Simulation: Social Cue-Based Interaction with a Virtual Character","authors":"Tobias Baur, Ionut Damian, Patrick Gebhard, K. Porayska-Pomsta, E. André","doi":"10.1109/SocialCom.2013.39","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.39","url":null,"abstract":"This paper presents an approach that makes use of a virtual character and social signal processing techniques to create an immersive job interview simulation environment. In this environment, the virtual character plays the role of a recruiter which reacts and adapts to the user's behavior thanks to a component for the automatic recognition of social cues (conscious or unconscious behavioral patterns). The social cues pertinent to job interviews have been identified using a knowledge elicitation study with real job seekers. Finally, we present two user studies to investigate the feasibility of the proposed approach as well as the impact of such a system on users.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125783894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.153
Bowen Ni, Yu-Han Chang, R. Maheswaran
This paper introduces the networked resource game with human players, implemented by a graphical online game where players can play their cards and accumulate rewards. We analyze social welfare and inequality through human players experiments, and show how these relate to the results of simulations using algorithms and robots.
{"title":"Social Welfare and Inequality in a Networked Resource Game with Human Players","authors":"Bowen Ni, Yu-Han Chang, R. Maheswaran","doi":"10.1109/SocialCom.2013.153","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.153","url":null,"abstract":"This paper introduces the networked resource game with human players, implemented by a graphical online game where players can play their cards and accumulate rewards. We analyze social welfare and inequality through human players experiments, and show how these relate to the results of simulations using algorithms and robots.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127270134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.46
Anisha Mazumder, Arun Das, Nyunsu Kim, Sedat Gokalp, Arunabha Sen, H. Davulcu
Online social network community now provides an enormous volume of data for analyzing human sentiment about people, places, events and political activities. It is becoming increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspects of the participants of these networks. As part of the Minerva project, currently underway at Arizona State University, we have analyzed a large volume of Twitter data to understand radical political activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree of radical activities in various provinces of Indonesia. We create the Heat Map of Indonesia by computing (i) the Radicalization Index and (ii) the Location Index of each Twitter user from Indonesia, who has expressed some radical sentiment in her tweets. The conclusions derived from our analysis matches significantly with the analysis of Wahid Institute, a leading political think tank of Indonesia, thus validating our results.
{"title":"Spatio-temporal Signal Recovery from Political Tweets in Indonesia","authors":"Anisha Mazumder, Arun Das, Nyunsu Kim, Sedat Gokalp, Arunabha Sen, H. Davulcu","doi":"10.1109/SocialCom.2013.46","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.46","url":null,"abstract":"Online social network community now provides an enormous volume of data for analyzing human sentiment about people, places, events and political activities. It is becoming increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspects of the participants of these networks. As part of the Minerva project, currently underway at Arizona State University, we have analyzed a large volume of Twitter data to understand radical political activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree of radical activities in various provinces of Indonesia. We create the Heat Map of Indonesia by computing (i) the Radicalization Index and (ii) the Location Index of each Twitter user from Indonesia, who has expressed some radical sentiment in her tweets. The conclusions derived from our analysis matches significantly with the analysis of Wahid Institute, a leading political think tank of Indonesia, thus validating our results.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.90
Johannes Braun, Gregor Rynkowski
The security of most Internet applications relies on underlying public key infrastructures (PKIs) and thus on an ecosystem of certification authorities (CAs). The pool of PKIs responsible for the issuance and the maintenance of SSL certificates, called the Web PKI, has grown extremely large and complex. Herein, each CA is a single point of failure, leading to an attack surface, the size of which is hardly assessable. This paper approaches the issue if and how the attack surface can be reduced in order to minimize the risk of relying on a malicious certificate. In particular, we consider the individualization of the set of trusted CAs. We present a tool called Rootopia, which allows to individually assess the respective part of the Web PKI relevant for a user. Our analysis of browser histories of 22 Internet users reveals, that the major part of the PKI is completely irrelevant to a single user. On a per user level, the attack surface can be reduced by more than 90%, which shows the potential of the individualization of the set of trusted CAs. Furthermore, all the relevant CAs reside within a small set of countries. Our findings confirm that we unnecessarily trust in a huge number of CAs, thus exposing ourselves to unnecessary risks. Subsequently, we present an overview on our approach to realize the possible security gains.
{"title":"The Potential of an Individualized Set of Trusted CAs: Defending against CA Failures in the Web PKI","authors":"Johannes Braun, Gregor Rynkowski","doi":"10.1109/SocialCom.2013.90","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.90","url":null,"abstract":"The security of most Internet applications relies on underlying public key infrastructures (PKIs) and thus on an ecosystem of certification authorities (CAs). The pool of PKIs responsible for the issuance and the maintenance of SSL certificates, called the Web PKI, has grown extremely large and complex. Herein, each CA is a single point of failure, leading to an attack surface, the size of which is hardly assessable. This paper approaches the issue if and how the attack surface can be reduced in order to minimize the risk of relying on a malicious certificate. In particular, we consider the individualization of the set of trusted CAs. We present a tool called Rootopia, which allows to individually assess the respective part of the Web PKI relevant for a user. Our analysis of browser histories of 22 Internet users reveals, that the major part of the PKI is completely irrelevant to a single user. On a per user level, the attack surface can be reduced by more than 90%, which shows the potential of the individualization of the set of trusted CAs. Furthermore, all the relevant CAs reside within a small set of countries. Our findings confirm that we unnecessarily trust in a huge number of CAs, thus exposing ourselves to unnecessary risks. Subsequently, we present an overview on our approach to realize the possible security gains.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134330190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.138
Yang Song, Zheng Hu, Haifeng Liu, Yu Shi, Hui Tian
Traditional recommender systems are designed to provide suggestions for individuals. However, there are scenarios in which groups of people are in need of decision support. For example, a group of friends want to choose a restaurant to have a dinner or to watch a movie together. In this paper, we propose a novel group recommendation algorithm for providing suggestions to groups. The proposed algorithm can be divided into two steps: the first step is to predict the preference of the unwatched items for each group members, which is a personalized prediction progress, then, it provides the recommendations for the group by aggregating group members' preferences, which mainly concerns the preferences of members who haven't seen the items. Without complex computation, the proposed algorithm can make accurate predictions of each item for group members. We demonstrate our algorithm on a famous dataset called Movie Lens and use the recall, the precision metrics and a combination of them to evaluate its performance. The experimental results show that the proposed algorithm can provide high quality group recommendations.
{"title":"A Novel Group Recommendation Algorithm with Collaborative Filtering","authors":"Yang Song, Zheng Hu, Haifeng Liu, Yu Shi, Hui Tian","doi":"10.1109/SocialCom.2013.138","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.138","url":null,"abstract":"Traditional recommender systems are designed to provide suggestions for individuals. However, there are scenarios in which groups of people are in need of decision support. For example, a group of friends want to choose a restaurant to have a dinner or to watch a movie together. In this paper, we propose a novel group recommendation algorithm for providing suggestions to groups. The proposed algorithm can be divided into two steps: the first step is to predict the preference of the unwatched items for each group members, which is a personalized prediction progress, then, it provides the recommendations for the group by aggregating group members' preferences, which mainly concerns the preferences of members who haven't seen the items. Without complex computation, the proposed algorithm can make accurate predictions of each item for group members. We demonstrate our algorithm on a famous dataset called Movie Lens and use the recall, the precision metrics and a combination of them to evaluate its performance. The experimental results show that the proposed algorithm can provide high quality group recommendations.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113949889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.150
Jerzy Surma
When using mobile phones, credit cards, electronic mail, browsing social networks etc., contemporary consumers leave behind thousands of digital footprints. Each footprint reflects actual actions that we take in given place and time. The analysis of thousands of such footprints conducted among large groups of people allows us to examine human behaviour on a scale that has never been imagined in scientific studies concerning psychology and sociology. The results of those analyses already have a significant influence on contemporary management, especially when it comes to new business opportunities in companies that employ business models based on the one-to-one relations with their customers. Nevertheless, this outstanding opportunity implies an enormous privacy problem. We will illustrate this issue by an empirical research based on the data gathered from Facebook, where users are using privacy controls that allow displaying their content only to a selected group of people. Users of such controls will likely continue positing more, even as their network grows or becomes sparser. We test these predictions using a dataset from Facebook gathered from a sample of college students and find statistical support for them. Our conclusions are that individuals are relatively prudent and are actually very aware of the social norms.
{"title":"The Privacy Problem in Big Bata Applications: An Empirical Study on Facebook","authors":"Jerzy Surma","doi":"10.1109/SocialCom.2013.150","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.150","url":null,"abstract":"When using mobile phones, credit cards, electronic mail, browsing social networks etc., contemporary consumers leave behind thousands of digital footprints. Each footprint reflects actual actions that we take in given place and time. The analysis of thousands of such footprints conducted among large groups of people allows us to examine human behaviour on a scale that has never been imagined in scientific studies concerning psychology and sociology. The results of those analyses already have a significant influence on contemporary management, especially when it comes to new business opportunities in companies that employ business models based on the one-to-one relations with their customers. Nevertheless, this outstanding opportunity implies an enormous privacy problem. We will illustrate this issue by an empirical research based on the data gathered from Facebook, where users are using privacy controls that allow displaying their content only to a selected group of people. Users of such controls will likely continue positing more, even as their network grows or becomes sparser. We test these predictions using a dataset from Facebook gathered from a sample of college students and find statistical support for them. Our conclusions are that individuals are relatively prudent and are actually very aware of the social norms.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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