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}
Citation analysis is a popular area of research, which has been usually used to rank the authors and the publication venues of research papers. With huge number of publications every year, it has become difficult for the users to find relevant publication materials. One simple solution to this problem is to detect communities from the citation network and recommend papers based on the common membership in communities. But, in today's research scenario, many researchers' fields of interest spread into multiple research directions resulting in an increasing number of interdisciplinary publications. Therefore, it is necessary to detect overlapping communities for relevant recommendation. In this paper, we represent publication information as a tripartite `Publication Hypergraph' consisting of authors, papers and publication venues (conferences/journals) in three partitions. We then propose an algorithm called `OverCite', which can detect overlapping communities of authors, papers and venues simultaneously using the publication hypergraph and the citation network information. We compare OverCite with two existing overlapping community detection algorithms, Clique Percolation Method (CPM) and iLCD, applied on citation network. The experiments on a large real-world citation dataset show that OverCite outperforms other two algorithms. We also present a simple paper search and recommendation system. Based on the relevance judgements of the users, we further prove the effectiveness of OverCite over other two algorithms.
{"title":"OverCite: Finding overlapping communities in citation network","authors":"Tanmoy Chakraborty, Abhijnan Chakraborty","doi":"10.1145/2492517.2500255","DOIUrl":"https://doi.org/10.1145/2492517.2500255","url":null,"abstract":"Citation analysis is a popular area of research, which has been usually used to rank the authors and the publication venues of research papers. With huge number of publications every year, it has become difficult for the users to find relevant publication materials. One simple solution to this problem is to detect communities from the citation network and recommend papers based on the common membership in communities. But, in today's research scenario, many researchers' fields of interest spread into multiple research directions resulting in an increasing number of interdisciplinary publications. Therefore, it is necessary to detect overlapping communities for relevant recommendation. In this paper, we represent publication information as a tripartite `Publication Hypergraph' consisting of authors, papers and publication venues (conferences/journals) in three partitions. We then propose an algorithm called `OverCite', which can detect overlapping communities of authors, papers and venues simultaneously using the publication hypergraph and the citation network information. We compare OverCite with two existing overlapping community detection algorithms, Clique Percolation Method (CPM) and iLCD, applied on citation network. The experiments on a large real-world citation dataset show that OverCite outperforms other two algorithms. We also present a simple paper search and recommendation system. Based on the relevance judgements of the users, we further prove the effectiveness of OverCite over other two algorithms.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"18 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":"114196272","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 Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.
{"title":"Ant colony based approach to predict stock market movement from mood collected on Twitter","authors":"S. Bouktif, M. Awad","doi":"10.1145/2492517.2500282","DOIUrl":"https://doi.org/10.1145/2492517.2500282","url":null,"abstract":"The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.","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":"114606909","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}
Tilman Göhnert, A. Harrer, Tobias Hecking, H. Hoppe
In this paper we introduce the concept of a web-based analytics workbench to support researchers of social networks in their analytic processes. Making explicit these processes allows for sound design, re-use, and automated execution using an authoring system for visual representations of these analytic workflows. The workbench is implemented according to a flexible technical framework in which external and newly-defined analytic components can be integrated and used in conjunction with other analytic components. As a showcase we discuss a complex analytic process.
{"title":"A workbench to construct and re-use network analysis workflows - Concept, implementation, and example case","authors":"Tilman Göhnert, A. Harrer, Tobias Hecking, H. Hoppe","doi":"10.1145/2492517.2492596","DOIUrl":"https://doi.org/10.1145/2492517.2492596","url":null,"abstract":"In this paper we introduce the concept of a web-based analytics workbench to support researchers of social networks in their analytic processes. Making explicit these processes allows for sound design, re-use, and automated execution using an authoring system for visual representations of these analytic workflows. The workbench is implemented according to a flexible technical framework in which external and newly-defined analytic components can be integrated and used in conjunction with other analytic components. As a showcase we discuss a complex analytic process.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"52 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":"114848008","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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