Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.20
Akshay Patil, Golnaz Ghasemiesfeh, Roozbeh Ebrahimi, Jie Gao
In many eCommerce websites and consumer review websites, users can review products they purchased as well as the reviews others wrote. Users can also rate each other as trusted or untrusted relationships. By studying a data set from Epinions, we examine and quantify the correlation between trust/distrust relationships among the users and their ratings of the reviews. We discover that there is a strong alignment between the opinions of one's friends and his/her ratings. Our findings also suggest that there is a strong alignment between the collective opinion of a user's friends and the formation of his/her future relationships.
{"title":"Quantifying Social Influence in Epinions","authors":"Akshay Patil, Golnaz Ghasemiesfeh, Roozbeh Ebrahimi, Jie Gao","doi":"10.1109/SocialCom.2013.20","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.20","url":null,"abstract":"In many eCommerce websites and consumer review websites, users can review products they purchased as well as the reviews others wrote. Users can also rate each other as trusted or untrusted relationships. By studying a data set from Epinions, we examine and quantify the correlation between trust/distrust relationships among the users and their ratings of the reviews. We discover that there is a strong alignment between the opinions of one's friends and his/her ratings. Our findings also suggest that there is a strong alignment between the collective opinion of a user's friends and the formation of his/her future relationships.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"19 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":"134455846","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}
Emergency resources are often insufficient to satisfy fully the demands for professional help and supplies after a public disaster. Furthermore, in a mass casualty situation, the emphasis shifts from ensuring the best possible outcome for each individual patient to ensuring the best possible outcome for the greatest number of patients. Historically, various manual and electronic medical triage systems have been used both under civil and military conditions to determine the order and priority of emergency treatment, transport, and best possible destination for the patients [12][13][15][16][17][18]. Unfortunately, none of those solutions has proven flexible, accurate, scalable or unobtrusive enough to meet the public's expectations [7]. In this paper, we provide insights into the trends, innovations, and challenges of contemporary crowdsourced e-Health and medical informatics applications in the context of emergency preparedness and response. Additionally, we demonstrate a system, called CrowdHelp, for real-time patient assessment which uses mobile electronic triaging accomplished via crowdsourced information. With the use of our system, emergency management professionals receive most of the information they need for preparing themselves to provide timely and accurate treatments of their patients even before dispatching a response team to the event.
{"title":"Applications of Social Networks and Crowdsourcing for Disaster Management Improvement","authors":"Liliya I. Besaleva, A. Weaver","doi":"10.1109/MC.2016.133","DOIUrl":"https://doi.org/10.1109/MC.2016.133","url":null,"abstract":"Emergency resources are often insufficient to satisfy fully the demands for professional help and supplies after a public disaster. Furthermore, in a mass casualty situation, the emphasis shifts from ensuring the best possible outcome for each individual patient to ensuring the best possible outcome for the greatest number of patients. Historically, various manual and electronic medical triage systems have been used both under civil and military conditions to determine the order and priority of emergency treatment, transport, and best possible destination for the patients [12][13][15][16][17][18]. Unfortunately, none of those solutions has proven flexible, accurate, scalable or unobtrusive enough to meet the public's expectations [7]. In this paper, we provide insights into the trends, innovations, and challenges of contemporary crowdsourced e-Health and medical informatics applications in the context of emergency preparedness and response. Additionally, we demonstrate a system, called CrowdHelp, for real-time patient assessment which uses mobile electronic triaging accomplished via crowdsourced information. With the use of our system, emergency management professionals receive most of the information they need for preparing themselves to provide timely and accurate treatments of their patients even before dispatching a response team to the event.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"1209 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":"131516348","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.2013.117
Hsin-Tsung Peng, William W. Y. Hsu, Chih-Hung Chen, F. Lai, Jan-Ming Ho
Predicting prices and risk measures of assets and derivatives and rating of financial products have been studied and widely used by financial institutions and individual investors. In contrast to the centralized and oligopoly nature of the existing financial information services, in this paper, we advocate the notion of a Financial Cloud, i.e., an open distributed framework based cloud computing architecture to host modularize financial services such that these modularized financial services may easily be integrated flexibly and dynamically to meet users' needs on demand. This new cloud based architecture of modularized financial services provides several advantages. We may have different types of service providers in the ecosystem on top of the framework. For example, market data resellers may collect and sell long-term historical market data. Statistical analyses of macroeconomic indices, interest rates, and correlation of a set of assets may also be purchased online. Some agencies might be interested in providing services based on rating or pricing values of financial products. Traders may use the statistically estimated parameters to fine-tune their trading algorithm to maximize the profit of their clients. Providers of each service module may focus on effectiveness, performance, robustness, and security of their innovative products. On the other hand, a user pays for exactly what one uses to optimally manage their assets. A user may also acquire services through an online agent who is an expert in assessing the structural model and quality of existing products and thus assembles service modules matching users risk taking behavior. In this paper, we will also present a survey of related existing technologies and a prototype we developed so far.
{"title":"FinancialCloud: Open Cloud Framework of Derivative Pricing","authors":"Hsin-Tsung Peng, William W. Y. Hsu, Chih-Hung Chen, F. Lai, Jan-Ming Ho","doi":"10.1109/SocialCom.2013.117","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.117","url":null,"abstract":"Predicting prices and risk measures of assets and derivatives and rating of financial products have been studied and widely used by financial institutions and individual investors. In contrast to the centralized and oligopoly nature of the existing financial information services, in this paper, we advocate the notion of a Financial Cloud, i.e., an open distributed framework based cloud computing architecture to host modularize financial services such that these modularized financial services may easily be integrated flexibly and dynamically to meet users' needs on demand. This new cloud based architecture of modularized financial services provides several advantages. We may have different types of service providers in the ecosystem on top of the framework. For example, market data resellers may collect and sell long-term historical market data. Statistical analyses of macroeconomic indices, interest rates, and correlation of a set of assets may also be purchased online. Some agencies might be interested in providing services based on rating or pricing values of financial products. Traders may use the statistically estimated parameters to fine-tune their trading algorithm to maximize the profit of their clients. Providers of each service module may focus on effectiveness, performance, robustness, and security of their innovative products. On the other hand, a user pays for exactly what one uses to optimally manage their assets. A user may also acquire services through an online agent who is an expert in assessing the structural model and quality of existing products and thus assembles service modules matching users risk taking behavior. In this paper, we will also present a survey of related existing technologies and a prototype we developed so far.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"29 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":"129544434","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.2013.56
Ammar Hassan, A. Abbasi, D. Zeng
Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.
{"title":"Twitter Sentiment Analysis: A Bootstrap Ensemble Framework","authors":"Ammar Hassan, A. Abbasi, D. Zeng","doi":"10.1109/SocialCom.2013.56","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.56","url":null,"abstract":"Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"7 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":"129723841","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.2013.84
Amira Bradai, H. Afifi
Host-Based Intrusion Detection Systems (HIDS)have been widely used to detect malicious behaviors of nodes in heterogenous networks. Collaborative intrusion detection can be more secure with a framework using reputation aggregation as an incentive. The problem of incentives and efficiency are well known problems that can be addressed in such collaborative environment. In this paper, we propose to use game theory to improve detection and optimize intrusion detection systems used in collaboration. The main contribution of this paper is that the reputation of HIDS is evaluated before modeling the game between the HIDS and attackers. Our proposal has three phases: the first phase builds reputation evaluation between HIDS and estimates the reputation for each one. In the second phase, a proposed algorithm elects a leader using reputation value to make decisions. In the last phase, using game theory the leader decides to activate or not the HIDS for optimization reasons.
{"title":"Game Theoretic Framework for Reputation-Based Distributed Intrusion Detection","authors":"Amira Bradai, H. Afifi","doi":"10.1109/SocialCom.2013.84","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.84","url":null,"abstract":"Host-Based Intrusion Detection Systems (HIDS)have been widely used to detect malicious behaviors of nodes in heterogenous networks. Collaborative intrusion detection can be more secure with a framework using reputation aggregation as an incentive. The problem of incentives and efficiency are well known problems that can be addressed in such collaborative environment. In this paper, we propose to use game theory to improve detection and optimize intrusion detection systems used in collaboration. The main contribution of this paper is that the reputation of HIDS is evaluated before modeling the game between the HIDS and attackers. Our proposal has three phases: the first phase builds reputation evaluation between HIDS and estimates the reputation for each one. In the second phase, a proposed algorithm elects a leader using reputation value to make decisions. In the last phase, using game theory the leader decides to activate or not the HIDS for optimization reasons.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"23 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":"125093532","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.2013.41
Laura M. Smith, Linhong Zhu, Kristina Lerman, Zornitsa Kozareva
In recent years, social media has revolutionized how people communicate and share information. Twitter and other blogging sites have seen an increase in political and social activism. Previous studies on the behaviors of users in politics have focused on electoral candidates and election results. Our paper investigates the role of social media in discussing and debating controversial topics. We apply sentiment analysis techniques to classify the position (for, against, neutral) expressed in a tweet about a controversial topic and use the results in our study of user behavior. Our findings suggest that Twitter is primarily used for spreading information to like-minded people rather than debating issues. Users are quicker to rebroadcast information than to address a communication by another user. Individuals typically take a position on an issue prior to posting about it and are not likely to change their tweeting opinion.
{"title":"The Role of Social Media in the Discussion of Controversial Topics","authors":"Laura M. Smith, Linhong Zhu, Kristina Lerman, Zornitsa Kozareva","doi":"10.1109/SocialCom.2013.41","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.41","url":null,"abstract":"In recent years, social media has revolutionized how people communicate and share information. Twitter and other blogging sites have seen an increase in political and social activism. Previous studies on the behaviors of users in politics have focused on electoral candidates and election results. Our paper investigates the role of social media in discussing and debating controversial topics. We apply sentiment analysis techniques to classify the position (for, against, neutral) expressed in a tweet about a controversial topic and use the results in our study of user behavior. Our findings suggest that Twitter is primarily used for spreading information to like-minded people rather than debating issues. Users are quicker to rebroadcast information than to address a communication by another user. Individuals typically take a position on an issue prior to posting about it and are not likely to change their tweeting opinion.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"53 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":"129184077","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.2013.135
J. Akaichi
In recent years, text mining and sentiment analysis have received great attention due to the abundance of opinion data that exist in social networks such as Facebook, Twitter, etc. Sentiments are projected on these media using texts for expressing feelings such as friendship, social support, anger, happiness, etc. Existing sentiment analysis studies tend to identify user behaviors and state of minds but remain insufficient due to complexities in conveyed texts. In this research paper, we focus on the usage of text mining for sentiment classification. Illustration is performed on Tunisian users' statuses on "Facebook" posts during the "Arabic Spring" era. Our aim is to extract useful information, about users' sentiments and behaviors during this sensitive and significant period. For that purpose, we propose a method based on Support Vector Machine (SVM) and Naïve Bayes. We also construct a sentiment lexicon, based on the emoticons, interjections and acronyms', from extracted statuses updates. Moreover, we perform some comparative experiments between two machine learning algorithms SVM and Naïve Bayes through a training model for sentiment classification.
{"title":"Social Networks' Facebook' Statutes Updates Mining for Sentiment Classification","authors":"J. Akaichi","doi":"10.1109/SocialCom.2013.135","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.135","url":null,"abstract":"In recent years, text mining and sentiment analysis have received great attention due to the abundance of opinion data that exist in social networks such as Facebook, Twitter, etc. Sentiments are projected on these media using texts for expressing feelings such as friendship, social support, anger, happiness, etc. Existing sentiment analysis studies tend to identify user behaviors and state of minds but remain insufficient due to complexities in conveyed texts. In this research paper, we focus on the usage of text mining for sentiment classification. Illustration is performed on Tunisian users' statuses on \"Facebook\" posts during the \"Arabic Spring\" era. Our aim is to extract useful information, about users' sentiments and behaviors during this sensitive and significant period. For that purpose, we propose a method based on Support Vector Machine (SVM) and Naïve Bayes. We also construct a sentiment lexicon, based on the emoticons, interjections and acronyms', from extracted statuses updates. Moreover, we perform some comparative experiments between two machine learning algorithms SVM and Naïve Bayes through a training model for sentiment classification.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"69 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":"117321183","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.2013.28
C. Musat, B. Faltings, Philippe Rousille
In this paper we investigate the impact of antagonism in online discussions. We define antagonism as a new class of textual opinions - direct sentiment towards the authors of previous comments. We detect the negative sentiment using aspect-based opinion mining techniques. We create a model of human behavior in online communities, based on the network topology and on the communication content. The model contains seven hypotheses, which validate two intuitions. The first intuition is that the content of the messages exchanged in an online community can separate good and insightful contributions from the rest. The second intuition is that there is a delay until the network stabilizes and until standard measures, such as betweenness centrality, can be used accurately. Taken together, these intuitions are a solid case for using the content of the communication along with network measures. We show that the sentiment within the messages, especially antagonism, can significantly alter the community perception. We use real world data, taken from the Slash dot discussion forum to validate our model. All the findings are accompanied by extremely significant t-test p-values.
{"title":"Direct Negative Opinions in Online Discussions","authors":"C. Musat, B. Faltings, Philippe Rousille","doi":"10.1109/SocialCom.2013.28","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.28","url":null,"abstract":"In this paper we investigate the impact of antagonism in online discussions. We define antagonism as a new class of textual opinions - direct sentiment towards the authors of previous comments. We detect the negative sentiment using aspect-based opinion mining techniques. We create a model of human behavior in online communities, based on the network topology and on the communication content. The model contains seven hypotheses, which validate two intuitions. The first intuition is that the content of the messages exchanged in an online community can separate good and insightful contributions from the rest. The second intuition is that there is a delay until the network stabilizes and until standard measures, such as betweenness centrality, can be used accurately. Taken together, these intuitions are a solid case for using the content of the communication along with network measures. We show that the sentiment within the messages, especially antagonism, can significantly alter the community perception. We use real world data, taken from the Slash dot discussion forum to validate our model. All the findings are accompanied by extremely significant t-test p-values.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"2009 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120846090","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}
This paper analyzes the relationship between asset return, volatility and the centrality indicators of a corporate news network conducting a longitudinal network analysis. We build a sequence of daily corporate news network for the period 2005-2011 using companies of the STOXX 50 index as nodes, the weights of the edges are the sum of the number of news items with the same topic by every pair of companies identified by the topic model methodology. The STOXX 50 includes the top 50 European companies by level of capitalization. We performed the Granger causality test and the Brownian distance covariance test of independence among several measures of centrality, return and volatility. We found that the average eigenvector centrality of the corporate news networks at different points of time has an impact on return and volatility of the STOXX 50 index. Likewise, return and volatility of the STOXX 50 index also has an effect on average eigenvector centrality. These results are more significant during the most important period of the recent financial crisis (January 2008-March 2009). So, we observe that there is a dynamic process that affects and is affected by return, volatility, and centrality. The causality tests suggest it is possible to improve the prediction of return and volatility by extracting and analyzing a network based on the common topics of news stories.
{"title":"Impact of Dynamic Corporate News Networks on Asset Return and Volatility","authors":"Germán G. Creamer, Yong Ren, J. Nickerson","doi":"10.2139/ssrn.2196572","DOIUrl":"https://doi.org/10.2139/ssrn.2196572","url":null,"abstract":"This paper analyzes the relationship between asset return, volatility and the centrality indicators of a corporate news network conducting a longitudinal network analysis. We build a sequence of daily corporate news network for the period 2005-2011 using companies of the STOXX 50 index as nodes, the weights of the edges are the sum of the number of news items with the same topic by every pair of companies identified by the topic model methodology. The STOXX 50 includes the top 50 European companies by level of capitalization. We performed the Granger causality test and the Brownian distance covariance test of independence among several measures of centrality, return and volatility. We found that the average eigenvector centrality of the corporate news networks at different points of time has an impact on return and volatility of the STOXX 50 index. Likewise, return and volatility of the STOXX 50 index also has an effect on average eigenvector centrality. These results are more significant during the most important period of the recent financial crisis (January 2008-March 2009). So, we observe that there is a dynamic process that affects and is affected by return, volatility, and centrality. The causality tests suggest it is possible to improve the prediction of return and volatility by extracting and analyzing a network based on the common topics of news stories.","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":"121539332","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.2013.35
Bogdan Vasilescu, V. Filkov, Alexander Serebrenik
Stack Overflow is a popular on-line programming question and answer community providing its participants with rapid access to knowledge and expertise of their peers, especially benefitting coders. Despite the popularity of Stack Overflow, its role in the work cycle of open-source developers is yet to be understood: on the one hand, participation in it has the potential to increase the knowledge of individual developers thus improving and speeding up the development process. On the other hand, participation in Stack Overflow may interrupt the regular working rhythm of the developer, hence also possibly slow down the development process. In this paper we investigate the interplay between Stack Overflow activities and the development process, reflected by code changes committed to the largest social coding repository, GitHub. Our study shows that active GitHub committers ask fewer questions and provide more answers than others. Moreover, we observe that active Stack Overflow askers distribute their work in a less uniform way than developers that do not ask questions. Finally, we show that despite the interruptions incurred, the Stack Overflow activity rate correlates with the code changing activity in GitHub.
{"title":"StackOverflow and GitHub: Associations between Software Development and Crowdsourced Knowledge","authors":"Bogdan Vasilescu, V. Filkov, Alexander Serebrenik","doi":"10.1109/SOCIALCOM.2013.35","DOIUrl":"https://doi.org/10.1109/SOCIALCOM.2013.35","url":null,"abstract":"Stack Overflow is a popular on-line programming question and answer community providing its participants with rapid access to knowledge and expertise of their peers, especially benefitting coders. Despite the popularity of Stack Overflow, its role in the work cycle of open-source developers is yet to be understood: on the one hand, participation in it has the potential to increase the knowledge of individual developers thus improving and speeding up the development process. On the other hand, participation in Stack Overflow may interrupt the regular working rhythm of the developer, hence also possibly slow down the development process. In this paper we investigate the interplay between Stack Overflow activities and the development process, reflected by code changes committed to the largest social coding repository, GitHub. Our study shows that active GitHub committers ask fewer questions and provide more answers than others. Moreover, we observe that active Stack Overflow askers distribute their work in a less uniform way than developers that do not ask questions. Finally, we show that despite the interruptions incurred, the Stack Overflow activity rate correlates with the code changing activity in GitHub.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"202 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":"123224026","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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