The goal of the present study is to analyze the evolution of adolescent friendship network and daily activities. The research question is that what kinds of activity and network variables explain the changes over time within a friendship network? At what stages are these variables important? A network survey was carried out in a classroom of a high school. The subjects were 45 high school students of 28 boys and 17 girls. Sociometric data were collected by having each student nominate up to 16 intimate classmates. These nominations were measured their gender and common activities including chatting, participating students' club, going to cram, having dinner, discussing homework, playing game, heart to heart talking, going to movie, shopping, and outdoor sport. Panel data was collected 10 times across 3 semesters from Sep. 2008 to Jan. 2010. The program SIENA was applied to estimate the models for the evolution of social networks and daily activities. Results showed that heart to heart talk had effect on friendship formation in the beginning of the first 2 semesters, going to cram and sporting had effects on keeping friendship between 2 semesters, and going to cram, club, and sporting had effects at the end of observations. It is concluded that each daily activity has specific effect on friendship initiation, maintaining, and continue at different stage for adolescents, and the mechanism is discussed.
{"title":"Evolution of friendship network and daily activities of high school students","authors":"Hsieh-Hua Yang, Chyi-In Wu","doi":"10.1145/2492517.2500300","DOIUrl":"https://doi.org/10.1145/2492517.2500300","url":null,"abstract":"The goal of the present study is to analyze the evolution of adolescent friendship network and daily activities. The research question is that what kinds of activity and network variables explain the changes over time within a friendship network? At what stages are these variables important? A network survey was carried out in a classroom of a high school. The subjects were 45 high school students of 28 boys and 17 girls. Sociometric data were collected by having each student nominate up to 16 intimate classmates. These nominations were measured their gender and common activities including chatting, participating students' club, going to cram, having dinner, discussing homework, playing game, heart to heart talking, going to movie, shopping, and outdoor sport. Panel data was collected 10 times across 3 semesters from Sep. 2008 to Jan. 2010. The program SIENA was applied to estimate the models for the evolution of social networks and daily activities. Results showed that heart to heart talk had effect on friendship formation in the beginning of the first 2 semesters, going to cram and sporting had effects on keeping friendship between 2 semesters, and going to cram, club, and sporting had effects at the end of observations. It is concluded that each daily activity has specific effect on friendship initiation, maintaining, and continue at different stage for adolescents, and the mechanism is discussed.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125116306","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}
Organizations need to accurately understand the skills and competencies of their human resources in order to effectively respond to internal and external demands for expertise and make informed hiring decisions. In recent years, however, human resources have become highly mobile, making it more difficult for organizations to accurately learn their competencies. In such environment, organizations need to rely significantly on third parties to provide them with useful information about individuals. These sources and the information they provide, however, vary in degrees of trust and validity. In a previous paper, we developed an ontology for skills and competencies and modeled and analyzed the various sources of information used to derive the belief in an individual's level of competency. In this paper, we present an approach based on social network analysis for identifying unreliable sources of competency information. We explore the conditions under which evaluations given by an individual or a group about another can be trusted. We evaluate this approach using recommendation data gathered by crawling user profiles in LinkedIn.
{"title":"Identifying unreliable sources of skill and competency information","authors":"Maryam Fazel-Zarandi, M. Fox","doi":"10.1145/2492517.2500268","DOIUrl":"https://doi.org/10.1145/2492517.2500268","url":null,"abstract":"Organizations need to accurately understand the skills and competencies of their human resources in order to effectively respond to internal and external demands for expertise and make informed hiring decisions. In recent years, however, human resources have become highly mobile, making it more difficult for organizations to accurately learn their competencies. In such environment, organizations need to rely significantly on third parties to provide them with useful information about individuals. These sources and the information they provide, however, vary in degrees of trust and validity. In a previous paper, we developed an ontology for skills and competencies and modeled and analyzed the various sources of information used to derive the belief in an individual's level of competency. In this paper, we present an approach based on social network analysis for identifying unreliable sources of competency information. We explore the conditions under which evaluations given by an individual or a group about another can be trusted. We evaluate this approach using recommendation data gathered by crawling user profiles in LinkedIn.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114015630","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}
We model the emergence and propagation of reputations in social networks with a novel distributed algorithm. In social networks, reputations of agents (nodes) are emerged and propagated through interactions among the agents and through intrinsic and extrinsic consensus (voting) among neighbors influenced by the network topology. Our algorithm considers the degree information of nodes and of their neighbors to combine consensus in order to model how reputations travel within the network. In our algorithm, each node updates reputations on its neighbors by considering past interactions, computing the velocity of the interactions to measure how frequent the interactions have been occurring recently, and adjusting the feedback values according to the velocity of the interaction. The algorithm also captures the phenomena of accuracy of reputations decaying over time if interactions have not occurred recently. We present two contributions through experiments: (1) We show that an agent's reputation value is influenced by the position of the agent in the network and the neighboring topology; (2) We also show that our algorithm can compute more accurate reputations than existing algorithms especially when the topological information matters. The experiments are conducted in random social networks and Autonomous Systems Networks to find malicious nodes.
{"title":"A model for recursive propagations of reputations in social networks","authors":"Jooyoung Lee, J. Oh","doi":"10.1145/2492517.2492663","DOIUrl":"https://doi.org/10.1145/2492517.2492663","url":null,"abstract":"We model the emergence and propagation of reputations in social networks with a novel distributed algorithm. In social networks, reputations of agents (nodes) are emerged and propagated through interactions among the agents and through intrinsic and extrinsic consensus (voting) among neighbors influenced by the network topology. Our algorithm considers the degree information of nodes and of their neighbors to combine consensus in order to model how reputations travel within the network. In our algorithm, each node updates reputations on its neighbors by considering past interactions, computing the velocity of the interactions to measure how frequent the interactions have been occurring recently, and adjusting the feedback values according to the velocity of the interaction. The algorithm also captures the phenomena of accuracy of reputations decaying over time if interactions have not occurred recently. We present two contributions through experiments: (1) We show that an agent's reputation value is influenced by the position of the agent in the network and the neighboring topology; (2) We also show that our algorithm can compute more accurate reputations than existing algorithms especially when the topological information matters. The experiments are conducted in random social networks and Autonomous Systems Networks to find malicious nodes.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122934197","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}
Ingmar Weber, Venkata Rama Kiran Garimella, A. Batayneh
We use public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter. Starting with a set of prominent seed Twitter users from both camps, we follow retweeting edges to obtain an extended network of users with inferred political orientation. We present an in-depth description of the members of the two camps, both in terms of behavior on Twitter and in terms of offline characteristics such as gender. Through the identification of partisan users, we compute a valence on the secular vs. Islamist axis for hashtags and use this information both to analyze topical interests and to quantify how polarized society as a whole is at a given point in time. For the last 12 months, large values on this “polarization barometer” coincided with periods of violence. Tweets are furthermore annotated using hand-crafted dictionaries to quantify the usage of (i) religious terms, (ii) derogatory terms referring to other religions, and (ii) references to charitable acts. The combination of all the information allows us to test and quantify a number of stereo-typical hypotheses such as (i) that religiosity and political Islamism are correlated, (ii) that political Islamism and negative views on other religions are linked, (iii) that religiosity goes hand in hand with charitable giving, and (iv) that the followers of the Egyptian Muslim Brotherhood are more tightly connected and expressing themselves “in unison” than the secular opposition. Whereas a lot of existing literature on the Arab Spring and the Egyptian Revolution is largely of qualitative and descriptive nature, our contribution lies in providing a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.
{"title":"Secular vs. Islamist polarization in Egypt on Twitter","authors":"Ingmar Weber, Venkata Rama Kiran Garimella, A. Batayneh","doi":"10.1145/2492517.2492557","DOIUrl":"https://doi.org/10.1145/2492517.2492557","url":null,"abstract":"We use public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter. Starting with a set of prominent seed Twitter users from both camps, we follow retweeting edges to obtain an extended network of users with inferred political orientation. We present an in-depth description of the members of the two camps, both in terms of behavior on Twitter and in terms of offline characteristics such as gender. Through the identification of partisan users, we compute a valence on the secular vs. Islamist axis for hashtags and use this information both to analyze topical interests and to quantify how polarized society as a whole is at a given point in time. For the last 12 months, large values on this “polarization barometer” coincided with periods of violence. Tweets are furthermore annotated using hand-crafted dictionaries to quantify the usage of (i) religious terms, (ii) derogatory terms referring to other religions, and (ii) references to charitable acts. The combination of all the information allows us to test and quantify a number of stereo-typical hypotheses such as (i) that religiosity and political Islamism are correlated, (ii) that political Islamism and negative views on other religions are linked, (iii) that religiosity goes hand in hand with charitable giving, and (iv) that the followers of the Egyptian Muslim Brotherhood are more tightly connected and expressing themselves “in unison” than the secular opposition. Whereas a lot of existing literature on the Arab Spring and the Egyptian Revolution is largely of qualitative and descriptive nature, our contribution lies in providing a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133981882","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}
A. Uversky, Dusan Ramljak, Vladan Radosavljevic, Kosta Ristovski, Z. Obradovic
When faced with the task of forming predictions for nodes in a social network, it can be quite difficult to decide which of the available connections among nodes should be used for the best results. This problem is further exacerbated when temporal information is available, prompting the question of whether this information should be aggregated or not, and if not, which portions of it should be used. With this challenge in mind, we propose a novel utilization of variograms for selecting potentially useful relationship types, whose merits are then evaluated using a Gaussian Conditional Random Field model for node attribute prediction of temporal social networks with a multigraph structure. Our flexible model allows for measuring many kinds of relationships between nodes in the network that evolve over time, as well as using those relationships to augment the outputs of various unstructured predictors to further improve performance. The experimental results exhibit the effectiveness of using particular relationships to boost performance of unstructured predictors, show that using other relationships could actually impede performance, and also indicate that while variograms alone are not necessarily sufficient to identify a useful relationship, they greatly help in removing obviously useless measures, and can be combined with intuition to identify the optimal relationships.
{"title":"Which links should I use? A variogram-based selection of relationship measures for prediction of node attributes in temporal multigraphs","authors":"A. Uversky, Dusan Ramljak, Vladan Radosavljevic, Kosta Ristovski, Z. Obradovic","doi":"10.1145/2492517.2492529","DOIUrl":"https://doi.org/10.1145/2492517.2492529","url":null,"abstract":"When faced with the task of forming predictions for nodes in a social network, it can be quite difficult to decide which of the available connections among nodes should be used for the best results. This problem is further exacerbated when temporal information is available, prompting the question of whether this information should be aggregated or not, and if not, which portions of it should be used. With this challenge in mind, we propose a novel utilization of variograms for selecting potentially useful relationship types, whose merits are then evaluated using a Gaussian Conditional Random Field model for node attribute prediction of temporal social networks with a multigraph structure. Our flexible model allows for measuring many kinds of relationships between nodes in the network that evolve over time, as well as using those relationships to augment the outputs of various unstructured predictors to further improve performance. The experimental results exhibit the effectiveness of using particular relationships to boost performance of unstructured predictors, show that using other relationships could actually impede performance, and also indicate that while variograms alone are not necessarily sufficient to identify a useful relationship, they greatly help in removing obviously useless measures, and can be combined with intuition to identify the optimal relationships.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"135 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132219512","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}
Complexity is generally accepted to be the interrelatedness of components within a system. Treating the general practitioner (GP)-patient encounter as a complex system, we argue that complexity (resulting from the degree of interactions between GP, colleagues, patient) determines the performance of GPs, measured by attitudes to responsibility for their decisions about patient treatment. In this paper, we propose the use of social network measures of `density' and `inclusiveness' for computing the `interrelatedness' of components within a complex system. We also suggest the use of `number of components' (NoC) and `degree of interrelatedness' (DoI) to plot the complexity profiles for each GP. Results from a sample of 107 GPs show that GPs with simple profiles (i.e. low NoC & low DoI), compared to those in non-simple profiles, indicate a higher responsibility for the decisions they make in medical care. In conclusion, we argue that social networks-based complexity profiles are useful for understanding responsibility-taking in primary care. We highlight a number of interesting insights and practical implications for healthcare professionals.
{"title":"Towards a networks-enabled complexity profile for examining responsibility for decision-making by healthcare professionals","authors":"K. S. Chung, Jane M. Young, K. White","doi":"10.1145/2492517.2500324","DOIUrl":"https://doi.org/10.1145/2492517.2500324","url":null,"abstract":"Complexity is generally accepted to be the interrelatedness of components within a system. Treating the general practitioner (GP)-patient encounter as a complex system, we argue that complexity (resulting from the degree of interactions between GP, colleagues, patient) determines the performance of GPs, measured by attitudes to responsibility for their decisions about patient treatment. In this paper, we propose the use of social network measures of `density' and `inclusiveness' for computing the `interrelatedness' of components within a complex system. We also suggest the use of `number of components' (NoC) and `degree of interrelatedness' (DoI) to plot the complexity profiles for each GP. Results from a sample of 107 GPs show that GPs with simple profiles (i.e. low NoC & low DoI), compared to those in non-simple profiles, indicate a higher responsibility for the decisions they make in medical care. In conclusion, we argue that social networks-based complexity profiles are useful for understanding responsibility-taking in primary care. We highlight a number of interesting insights and practical implications for healthcare professionals.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1217 44","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133842363","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}
R. Zunino, F. Bisio, C. Peretti, Roberto Surlinelli, Eugenio Scillia, A. Ottaviano, Fabio Sangiacomo
The paper presents a general methodology to implement a flexible Focused Crawler for investigation purposes, monitoring, and Open Source Intelligence (OSINT). The resulting tool is specifically aimed to fit the operational requirements of law-enforcement agencies and intelligence analyst. The architecture of the semantic Focused Crawler features static flexibility in the definition of desired concepts, used metrics, and crawling strategy; in addition, the method is capable to learn (and adapt to) the analyst's expectations at runtime. The user may instruct the crawler with a binary feedback (yes/no) about the current performance of the surfing process, and the crawling engine progressively refines the expected targets accordingly. The method implementation is based on an existing text-mining environment, integrated with semantic networks and ontologies. Experimental results witness the effectiveness of the adaptive mechanism.
{"title":"An analyst-adaptive approach to Focused Crawlers","authors":"R. Zunino, F. Bisio, C. Peretti, Roberto Surlinelli, Eugenio Scillia, A. Ottaviano, Fabio Sangiacomo","doi":"10.1145/2492517.2500328","DOIUrl":"https://doi.org/10.1145/2492517.2500328","url":null,"abstract":"The paper presents a general methodology to implement a flexible Focused Crawler for investigation purposes, monitoring, and Open Source Intelligence (OSINT). The resulting tool is specifically aimed to fit the operational requirements of law-enforcement agencies and intelligence analyst. The architecture of the semantic Focused Crawler features static flexibility in the definition of desired concepts, used metrics, and crawling strategy; in addition, the method is capable to learn (and adapt to) the analyst's expectations at runtime. The user may instruct the crawler with a binary feedback (yes/no) about the current performance of the surfing process, and the crawling engine progressively refines the expected targets accordingly. The method implementation is based on an existing text-mining environment, integrated with semantic networks and ontologies. Experimental results witness the effectiveness of the adaptive mechanism.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130282922","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}
M. Berlingerio, Danai Koutra, Tina Eliassi-Rad, C. Faloutsos
Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a “signature” for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NetSimile, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NetSimile outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.
{"title":"Network similarity via multiple social theories","authors":"M. Berlingerio, Danai Koutra, Tina Eliassi-Rad, C. Faloutsos","doi":"10.1145/2492517.2492582","DOIUrl":"https://doi.org/10.1145/2492517.2492582","url":null,"abstract":"Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a “signature” for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NetSimile, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NetSimile outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122191596","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}
Diffusion of information in social networks takes more and more attention from marketers. New methods and algorithms are constantly developed towards maximizing reach of the campaigns and increasing their effectiveness. One of the important research directions in this area is related to selecting initial nodes of the campaign to result with maximizing its effects represented as total number of infections. To achieve this goal, several strategies were developed and they are based on different network measures and other characteristics of users. The problem is that most of these strategies base on static network properties while typical online networks change over time and are sensitive to varying activity of users. In this work a novel strategy is proposed which is based on multiple measures with additional parameters related to nodes availability in time periods prior to the campaign. Presented results show that it is possible to compensate users with high network measures by others having high frequency of system usage, which, instead, may be easier or cheaper to acquire.
{"title":"Compensatory seeding in networks with varying avaliability of nodes","authors":"Jarosław Jankowski, Radosław Michalski, Przemyslaw Kazienko","doi":"10.1145/2492517.2500256","DOIUrl":"https://doi.org/10.1145/2492517.2500256","url":null,"abstract":"Diffusion of information in social networks takes more and more attention from marketers. New methods and algorithms are constantly developed towards maximizing reach of the campaigns and increasing their effectiveness. One of the important research directions in this area is related to selecting initial nodes of the campaign to result with maximizing its effects represented as total number of infections. To achieve this goal, several strategies were developed and they are based on different network measures and other characteristics of users. The problem is that most of these strategies base on static network properties while typical online networks change over time and are sensitive to varying activity of users. In this work a novel strategy is proposed which is based on multiple measures with additional parameters related to nodes availability in time periods prior to the campaign. Presented results show that it is possible to compensate users with high network measures by others having high frequency of system usage, which, instead, may be easier or cheaper to acquire.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132171026","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}
The popularity of social media sites like Twitter and Facebook opens up interesting research opportunities for understanding the interplay of social media and e-commerce. Most research on online behavior, up until recently, has focused mostly on social media behaviors and e-commerce behaviors independently. In our study we choose a particular global e-commerce platform (eBay) and a particular global social media platform (Twitter). We quantify the characteristics of the two individual trends as well as the correlations between them. We provide evidences that about 5% of general eBay query streams show strong positive correlations with the corresponding Twitter mention streams, while the percentage jumps to around 25% for trending eBay query streams. Some categories of eBay queries, such as `Video Games' and `Sports', are more likely to have strong correlations. We also discover that eBay trend lags Twitter for correlated pairs and the lag differs across categories. We show evidences that celebrities' popularities on Twitter correlate well with their relevant search and sales on eBay. The correlations and lags provide predictive insights for future applications that might lead to instant merchandising opportunities for both sellers and e-commerce platforms.
{"title":"Chelsea won, and you bought a T-shirt: Characterizing the interplay between Twitter and e-commerce","authors":"Haipeng Zhang, Nish Parikh, Gyanit Singh, Neel Sundaresan","doi":"10.1145/2492517.2500302","DOIUrl":"https://doi.org/10.1145/2492517.2500302","url":null,"abstract":"The popularity of social media sites like Twitter and Facebook opens up interesting research opportunities for understanding the interplay of social media and e-commerce. Most research on online behavior, up until recently, has focused mostly on social media behaviors and e-commerce behaviors independently. In our study we choose a particular global e-commerce platform (eBay) and a particular global social media platform (Twitter). We quantify the characteristics of the two individual trends as well as the correlations between them. We provide evidences that about 5% of general eBay query streams show strong positive correlations with the corresponding Twitter mention streams, while the percentage jumps to around 25% for trending eBay query streams. Some categories of eBay queries, such as `Video Games' and `Sports', are more likely to have strong correlations. We also discover that eBay trend lags Twitter for correlated pairs and the lag differs across categories. We show evidences that celebrities' popularities on Twitter correlate well with their relevant search and sales on eBay. The correlations and lags provide predictive insights for future applications that might lead to instant merchandising opportunities for both sellers and e-commerce platforms.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609115","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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