Social news and content aggregation Web sites have become massive repositories of valuable knowledge on a diverse range of topics. Millions of Web-users are able to leverage these platforms to submit, view and discuss nearly anything. The users themselves exclusively curate the content with an intricate system of submissions, voting and discussion. Furthermore, the data on social news Web sites is extremely well organized by its user-base, which opens the door for opportunities to leverage this data for other purposes just like Wikipedia data has been used for many other purposes. In this paper we study a popular social news Web site called Reddit. Our investigation looks at the dynamics of its discussion threads, and asks two main questions: (1) to what extent do discussion threads resemble a topical hierarchy? and (2) Can discussion threads be used to enhance Web search? We show interesting results for these questions on a very large snapshot several sub-communities of the Reddit Web site. Finally, we discuss the implications of these results and suggest ways by which social news Web site's can be used to perform other tasks.
{"title":"An exploration of discussion threads in social news sites: A case study of the Reddit community","authors":"Tim Weninger, X. A. Zhu, Jiawei Han","doi":"10.1145/2492517.2492646","DOIUrl":"https://doi.org/10.1145/2492517.2492646","url":null,"abstract":"Social news and content aggregation Web sites have become massive repositories of valuable knowledge on a diverse range of topics. Millions of Web-users are able to leverage these platforms to submit, view and discuss nearly anything. The users themselves exclusively curate the content with an intricate system of submissions, voting and discussion. Furthermore, the data on social news Web sites is extremely well organized by its user-base, which opens the door for opportunities to leverage this data for other purposes just like Wikipedia data has been used for many other purposes. In this paper we study a popular social news Web site called Reddit. Our investigation looks at the dynamics of its discussion threads, and asks two main questions: (1) to what extent do discussion threads resemble a topical hierarchy? and (2) Can discussion threads be used to enhance Web search? We show interesting results for these questions on a very large snapshot several sub-communities of the Reddit Web site. Finally, we discuss the implications of these results and suggest ways by which social news Web site's can be used to perform other tasks.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"47 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":"133539157","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}
Social networks are usually drawn from the interactions between individuals, and therefore are temporal and dynamic in essence. Examining how the structure of these networks changes over time provides insights into their evolution patterns, factors that trigger the changes, and ultimately predict the future structure of these networks. One of the key structural characteristics of networks is their community structure -groups of densely interconnected nodes. Communities in a dynamic social network span over periods of time and are affected by changes in the underlying population, i.e. they have fluctuating members and can grow and shrink over time. In this paper, we introduce a new incremental community mining approach, in which communities in the current time are obtained based on the communities from the past time frame. Compared to previous independent approaches, this incremental approach is more effective at detecting stable communities over time. Extensive experimental studies on real datasets, demonstrate the applicability, effectiveness, and soundness of our proposed framework.
{"title":"Incremental local community identification in dynamic social networks","authors":"M. Takaffoli, Reihaneh Rabbany, Osmar R Zaiane","doi":"10.1145/2492517.2492633","DOIUrl":"https://doi.org/10.1145/2492517.2492633","url":null,"abstract":"Social networks are usually drawn from the interactions between individuals, and therefore are temporal and dynamic in essence. Examining how the structure of these networks changes over time provides insights into their evolution patterns, factors that trigger the changes, and ultimately predict the future structure of these networks. One of the key structural characteristics of networks is their community structure -groups of densely interconnected nodes. Communities in a dynamic social network span over periods of time and are affected by changes in the underlying population, i.e. they have fluctuating members and can grow and shrink over time. In this paper, we introduce a new incremental community mining approach, in which communities in the current time are obtained based on the communities from the past time frame. Compared to previous independent approaches, this incremental approach is more effective at detecting stable communities over time. Extensive experimental studies on real datasets, demonstrate the applicability, effectiveness, and soundness of our proposed framework.","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":"129333878","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 rapid development of information and communication technologies, especially the invention of digital computers and the Internet, pushed almost a whole world into information age, where information became so powerful that it can be directly used as a weapon of destruction. We can expect that beside conventional warfare, information warfare and cyber warfare are becoming more and more a possible option in future conflicts. Few cyber conflicts have shown that digital weapons can be successfully used to achieve attacker goals. Specially, the Stuxnet worm has proven, that digital weapons can also have kinetic effects and that the wars in the future will not be held only in a cyberspace, but rather the cyberspace will be exploited to achieve some advantages over the enemy. This paper contains a short analysis of the possible applications of modern information and communication technologies in the future conflicts.
{"title":"eWar - Reality of future wars","authors":"Gorazd Praprotnik, T. Ivanusa, I. Podbregar","doi":"10.1145/2492517.2500321","DOIUrl":"https://doi.org/10.1145/2492517.2500321","url":null,"abstract":"The rapid development of information and communication technologies, especially the invention of digital computers and the Internet, pushed almost a whole world into information age, where information became so powerful that it can be directly used as a weapon of destruction. We can expect that beside conventional warfare, information warfare and cyber warfare are becoming more and more a possible option in future conflicts. Few cyber conflicts have shown that digital weapons can be successfully used to achieve attacker goals. Specially, the Stuxnet worm has proven, that digital weapons can also have kinetic effects and that the wars in the future will not be held only in a cyberspace, but rather the cyberspace will be exploited to achieve some advantages over the enemy. This paper contains a short analysis of the possible applications of modern information and communication technologies in the future conflicts.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"66 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":"116189255","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 propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications.
{"title":"Academic network analysis: A joint topic modeling approach","authors":"Zaihan Yang, Liangjie Hong, Brian D. Davison","doi":"10.1145/2492517.2492524","DOIUrl":"https://doi.org/10.1145/2492517.2492524","url":null,"abstract":"We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"357 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":"116517495","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}
Online bibliographic databases have become widely available and are important resources to scientific researchers. These databases store rich information and many evolve into digital libraries. Using a bibliographic database of a specific discipline, we can extract a co-authorship and citation network of individual professionals. This allows for the study of patterns in scholarly contributions as well as for the exploration of scientific disputes associated with an individuals career. We have designed a visualization tool, which we call PathWay, to discover and understand patterns and trends in the bibliographic data over a selected period of time. With PathWay, we conducted case studies on a bibliography of approximately 400,000 scientists in physics over a 26 year time period. In this paper, we show how PathWay can be used to characterize one's academic career path in terms of the publication record, conduct comparative studies that would be difficult to do with conventional search methods, and also provide a way to gain insight into the emergence and the career implications of the scientific disputes associated with publications.
{"title":"Visual exploration of academic career paths","authors":"M. Wu, Robert W. Faris, K. Ma","doi":"10.1145/2492517.2492638","DOIUrl":"https://doi.org/10.1145/2492517.2492638","url":null,"abstract":"Online bibliographic databases have become widely available and are important resources to scientific researchers. These databases store rich information and many evolve into digital libraries. Using a bibliographic database of a specific discipline, we can extract a co-authorship and citation network of individual professionals. This allows for the study of patterns in scholarly contributions as well as for the exploration of scientific disputes associated with an individuals career. We have designed a visualization tool, which we call PathWay, to discover and understand patterns and trends in the bibliographic data over a selected period of time. With PathWay, we conducted case studies on a bibliography of approximately 400,000 scientists in physics over a 26 year time period. In this paper, we show how PathWay can be used to characterize one's academic career path in terms of the publication record, conduct comparative studies that would be difficult to do with conventional search methods, and also provide a way to gain insight into the emergence and the career implications of the scientific disputes associated with publications.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"94 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":"125712289","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}
Charles Perez, B. Birregah, R. Layton, Marc Lemercier, P. Watters
In the last few decades social networking sites have encountered their first large-scale security issues. The high number of users associated with the presence of sensitive data (personal or professional) is certainly an unprecedented opportunity for malicious activities. As a result, one observes that malicious users are progressively turning their attention from traditional e-mail to online social networks to carry out their attacks. Moreover, it is now observed that attacks are not only performed by individual profiles, but that on a larger scale, a set of profiles can act in coordination in making such attacks. The latter are referred to as malicious social campaigns. In this paper, we present a novel approach that combines authorship attribution techniques with a behavioural analysis for detecting and characterizing social campaigns. The proposed approach is performed in three steps: first, suspicious profiles are identified from a behavioural analysis; second, connections between suspicious profiles are retrieved using a combination of authorship attribution and temporal similarity; third, a clustering algorithm is performed to identify and characterise the suspicious campaigns obtained. We provide a real-life application of the methodology on a sample of 1,000 suspicious Twitter profiles tracked over a period of forty days. Our results show that a large set of suspicious profiles behaves in coordination (70%) and propagates mainly, but not only, trustworthy URLs on the online social network. Among the three largest detected campaigns, we have highlighted that one represents an important security issue for the platform by promoting a significant set of malicious URLs.
{"title":"REPLOT: Retrieving profile links on Twitter for suspicious networks detection","authors":"Charles Perez, B. Birregah, R. Layton, Marc Lemercier, P. Watters","doi":"10.1145/2492517.2500234","DOIUrl":"https://doi.org/10.1145/2492517.2500234","url":null,"abstract":"In the last few decades social networking sites have encountered their first large-scale security issues. The high number of users associated with the presence of sensitive data (personal or professional) is certainly an unprecedented opportunity for malicious activities. As a result, one observes that malicious users are progressively turning their attention from traditional e-mail to online social networks to carry out their attacks. Moreover, it is now observed that attacks are not only performed by individual profiles, but that on a larger scale, a set of profiles can act in coordination in making such attacks. The latter are referred to as malicious social campaigns. In this paper, we present a novel approach that combines authorship attribution techniques with a behavioural analysis for detecting and characterizing social campaigns. The proposed approach is performed in three steps: first, suspicious profiles are identified from a behavioural analysis; second, connections between suspicious profiles are retrieved using a combination of authorship attribution and temporal similarity; third, a clustering algorithm is performed to identify and characterise the suspicious campaigns obtained. We provide a real-life application of the methodology on a sample of 1,000 suspicious Twitter profiles tracked over a period of forty days. Our results show that a large set of suspicious profiles behaves in coordination (70%) and propagates mainly, but not only, trustworthy URLs on the online social network. Among the three largest detected campaigns, we have highlighted that one represents an important security issue for the platform by promoting a significant set of malicious URLs.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"202 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":"125742791","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}
Human spatial motions determine geographic social contacts that influence the way an information is spread on a population or a community. As mobility is a transverse dimension to social practices it is important to better understand its role. With the Eternal-Return model we propose, we simulate an artificial world populated by heterogeneous agents who differ in their mobility. We have chosen a multi-agent framework perspective for this simulation. We endow the agents with simple rules on how to move around the space and how to establish proximity-contacts. This allows to distinguish different kinds of mobile agents, from sedentary ones to travelers. To summarize the dynamics induced by mobility over time, we define the mobility-based Social Proximity Network as being the network of all distinct contacts between agents. Its properties give insight in the process of information spreading. We conduct simulations to understand how an information can be broadcast when agent-nodes are in motion.
{"title":"Simulating human mobility and information diffusion","authors":"M. Collard, P. Collard, Erick Stattner","doi":"10.1145/2492517.2492631","DOIUrl":"https://doi.org/10.1145/2492517.2492631","url":null,"abstract":"Human spatial motions determine geographic social contacts that influence the way an information is spread on a population or a community. As mobility is a transverse dimension to social practices it is important to better understand its role. With the Eternal-Return model we propose, we simulate an artificial world populated by heterogeneous agents who differ in their mobility. We have chosen a multi-agent framework perspective for this simulation. We endow the agents with simple rules on how to move around the space and how to establish proximity-contacts. This allows to distinguish different kinds of mobile agents, from sedentary ones to travelers. To summarize the dynamics induced by mobility over time, we define the mobility-based Social Proximity Network as being the network of all distinct contacts between agents. Its properties give insight in the process of information spreading. We conduct simulations to understand how an information can be broadcast when agent-nodes are in motion.","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":"125860702","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}
Kathleen M. Carley, J. Pfeffer, Huan Liu, Fred Morstatter, Rebecca Goolsby
When a crisis occurs, there is often little time to evaluate the situation and determine how best to respond. We use rapid ethnographic methods centered on the construction of geo-temporally contextualized social and knowledge networks. By utilizing a combination of Twitter and news media, the consulate attack in Libya were examined in near real time. In this work we outline a procedure to extract key insights from the event as an event unfolds using a suite of tools developed by a team of researchers from two universities.
{"title":"Near real time assessment of social media using geo-temporal network analytics","authors":"Kathleen M. Carley, J. Pfeffer, Huan Liu, Fred Morstatter, Rebecca Goolsby","doi":"10.1145/2492517.2492561","DOIUrl":"https://doi.org/10.1145/2492517.2492561","url":null,"abstract":"When a crisis occurs, there is often little time to evaluate the situation and determine how best to respond. We use rapid ethnographic methods centered on the construction of geo-temporally contextualized social and knowledge networks. By utilizing a combination of Twitter and news media, the consulate attack in Libya were examined in near real time. In this work we outline a procedure to extract key insights from the event as an event unfolds using a suite of tools developed by a team of researchers from two universities.","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":"129369075","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}
Jorge Gonçalves, V. Kostakos, Jayant Venkatanathan
Narrowcasting refers to the targeted segmentation of media dissemination, and has been proposed as a counterpart to broadcasting. We present an explorative study that evaluates narrowcasting as an approach to sharing in online social media. We test a narrowcasting prototype for Facebook with 54 participants over a four-week period. We outline the various strategies that participants used to appropriate narrowcasting, and report on participants' use and perceptions. We also report on the effects of default sharing options and gender on sharing behavior. Our work provides implications for online sharing, suggesting that narrowcasting is an effective strategy for online social platforms.
{"title":"Narrowcasting in social media: Effects and perceptions","authors":"Jorge Gonçalves, V. Kostakos, Jayant Venkatanathan","doi":"10.1145/2492517.2492570","DOIUrl":"https://doi.org/10.1145/2492517.2492570","url":null,"abstract":"Narrowcasting refers to the targeted segmentation of media dissemination, and has been proposed as a counterpart to broadcasting. We present an explorative study that evaluates narrowcasting as an approach to sharing in online social media. We test a narrowcasting prototype for Facebook with 54 participants over a four-week period. We outline the various strategies that participants used to appropriate narrowcasting, and report on participants' use and perceptions. We also report on the effects of default sharing options and gender on sharing behavior. Our work provides implications for online sharing, suggesting that narrowcasting is an effective strategy for online social platforms.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"3 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":"128977727","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}
Social networks are platforms where millions of users interact frequently and share variety of digital content with each other. Users express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights to such data. We attempt to extend the established OLAP(On-line Analytical Processing) technology to allow multidimensional analysis of social media data by integrating text and opinion mining methods into the data warehousing system and by exploiting various knowledge discovery techniques to deal with semi-structured and unstructured data from social media. The capabilities of OLAP are extended by semantic enrichment of the underlying dataset to discover new measures and dimensions for building data cubes and by supporting up-to-date analysis of the evolving as well as the historical social media data. The benefits of such an analysis platform are demonstrated by building a data warehouse for a social network of Twitter, dynamically enriching the underlying dataset and enabling multidimensional analysis.
{"title":"OLAPing social media: The case of Twitter","authors":"N. Rehman, Andreas Weiler, M. Scholl","doi":"10.1145/2492517.2500273","DOIUrl":"https://doi.org/10.1145/2492517.2500273","url":null,"abstract":"Social networks are platforms where millions of users interact frequently and share variety of digital content with each other. Users express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights to such data. We attempt to extend the established OLAP(On-line Analytical Processing) technology to allow multidimensional analysis of social media data by integrating text and opinion mining methods into the data warehousing system and by exploiting various knowledge discovery techniques to deal with semi-structured and unstructured data from social media. The capabilities of OLAP are extended by semantic enrichment of the underlying dataset to discover new measures and dimensions for building data cubes and by supporting up-to-date analysis of the evolving as well as the historical social media data. The benefits of such an analysis platform are demonstrated by building a data warehouse for a social network of Twitter, dynamically enriching the underlying dataset and enabling multidimensional analysis.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"7 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":"117178160","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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