Leveraging Social Context for Searching Social Media

The ability to utilize and benefit from today’s explosion of social media sites depends on providing tools that allow users to productively participate. In order to participate, users must be able to find resources (both people and information) that they find valuable. Here, we argue that in order to do this effectively, we should make use of a user’s “social context”. A user’s social context includes both their personal social context (their friends and the communities to which they belong) and their community social context (their role and identity in different communities)

Study about the different use of explicit and implicit tags in social bookmarking

This is the accepted version of the following article: Arolas, E. E., & Ladrón-de-Guevar, F. G. (2012). Uses of explicit and implicit tags in social bookmarking. Journal of the American Society for Information Science and Technology, 63(2), 313-322. doi:10.1002/asi.21663, which has been published in final form at http://dx.doi.org/10.1002/asi.21663Although Web 2.0 contains many tools with different functionalities, they all share a common social nature. One tool in particular, social bookmarking systems (SBSs), allows users to store and share links to different types of resources, i.e., websites, videos, images. To identify and classify these resources so that they can be retrieved and shared, fragments of text are used. These fragments of text, usually words, are called tags. A tag that is found on the inside of a resource text is referred to as an obvious or explicit tag. There are also nonobvious or implicit tags, which don't appear in the resource text. The purpose of this article is to describe the present situation of the SBSs tool and then to also determine the principal features of and how to use explicit tags. It will be taken into special consideration which HTML tags with explicit tags are used more frequently.Estelles Arolas, E.; González Ladrón De Guevara, FR. (2012). Study about the different use of explicit and implicit tags in social bookmarking. Journal of the American Society for Information Science and Technology. 63(2):313-322. doi:10.1002/asi.21663S313322632Bar-Ilan, J., Zhitomirsky-Geffet, M., Miller, Y., & Shoham, S. (2010). The effects of background information and social interaction on image tagging. Journal of the American Society for Information Science and Technology, 61(5), 940-951. doi:10.1002/asi.21306Bateman, S., Muller, M. J., & Freyne, J. (2009). Personalized retrieval in social bookmarking. Proceedinfs of the ACM 2009 international conference on Supporting group work - GROUP ’09. doi:10.1145/1531674.1531688Delicious' Blog 2010 What's next for Delicious http://blog.delicious.com/blog/2010/12/whats-next-for-delicious.htmlDing, Y., Jacob, E. K., Zhang, Z., Foo, S., Yan, E., George, N. L., & Guo, L. (2009). Perspectives on social tagging. Journal of the American Society for Information Science and Technology, 60(12), 2388-2401. doi:10.1002/asi.21190Eisterlehner , F. Hotho , A. Jäschke , R. ECML PKDD Discovery Challenge 2009 (DC09)Farooq, U., Kannampallil, T. G., Song, Y., Ganoe, C. H., Carroll, J. M., & Giles, L. (2007). Evaluating tagging behavior in social bookmarking systems. Proceedings of the 2007 international ACM conference on Conference on supporting group work - GROUP ’07. doi:10.1145/1316624.1316677Farooq , U. Zhang , S.M. Carroll , J. 2009 Sensemaking of scholarly literature through taggingFu, W.-T., Kannampallil, T., Kang, R., & He, J. (2010). Semantic imitation in social tagging. ACM Transactions on Computer-Human Interaction, 17(3), 1-37. doi:10.1145/1806923.1806926Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 964-971. doi:10.1145/32206.32212Golder , S.A. Huberman , B.A. 2005 The structure of collaborative tagging systems http://www.hpl.hp.com/research/idl/papers/tagsKörner, C., Benz, D., Hotho, A., Strohmaier, M., & Stumme, G. (2010). Stop thinking, start tagging. Proceedings of the 19th international conference on World wide web - WWW ’10. doi:10.1145/1772690.1772744Koutrika, G., Effendi, F. A., Gyöngyi, Z., Heymann, P., & Garcia-Molina, H. (2008). Combating spam in tagging systems. ACM Transactions on the Web, 2(4), 1-34. doi:10.1145/1409220.1409225Lipczak, M., & Milios, E. (2010). The impact of resource title on tags in collaborative tagging systems. Proceedings of the 21st ACM conference on Hypertext and hypermedia - HT ’10. doi:10.1145/1810617.1810648Marinho, L. B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., & Symeonidis, P. (2010). Social Tagging Recommender Systems. Recommender Systems Handbook, 615-644. doi:10.1007/978-0-387-85820-3_19Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). HT06, tagging paper, taxonomy, Flickr, academic article, to read. Proceedings of the seventeenth conference on Hypertext and hypermedia - HYPERTEXT ’06. doi:10.1145/1149941.1149949Mathes , A. 2004 Folksonomies-Cooperative classification and communication through shared metadata http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.htmlMelenhorst, M., & van Setten, M. (2007). Usefulness of Tags in Providing Access to Large Information Systems. 2007 IEEE International Professional Communication Conference. doi:10.1109/ipcc.2007.4464070Millen, D., Feinberg, J., & Kerr, B. (2005). Social bookmarking in the enterprise. Queue, 3(9), 28. doi:10.1145/1105664.1105676Robu, V., Halpin, H., & Shepherd, H. (2009). Emergence of consensus and shared vocabularies in collaborative tagging systems. ACM Transactions on the Web, 3(4), 1-34. doi:10.1145/1594173.1594176Schmitz, C., Hotho, A., Jäschke, R., & Stumme, G. (s. f.). Mining Association Rules in Folksonomies. Data Science and Classification, 261-270. doi:10.1007/3-540-34416-0_28Smith , G. 2004 Atomiq: Folksonomy: social classification http://atomiq.org/archives/2004/08/folksonomy_social_classification.htmlSubramanya, S. B., & Liu, H. (2008). Socialtagger - collaborative tagging for blogs in the long tail. Proceeding of the 2008 ACM workshop on Search in social media - SSM ’08. doi:10.1145/1458583.1458588Au Yeung, C., Gibbins, N., & Shadbolt, N. (2009). Contextualising tags in collaborative tagging systems. Proceedings of the 20th ACM conference on Hypertext and hypermedia - HT ’09. doi:10.1145/1557914.1557958Zhang, N., Zhang, Y., & Tang, J. (2009). A tag recommendation system for folksonomy. Proceeding of the 2nd ACM workshop on Social web search and mining - SWSM ’09. doi:10.1145/1651437.165144

Exploiting links and text structure on the Web : a quantitative approach to improving search quality

[no abstract

Facebook and Youth@SG: Online Privacy and Personal Information Disclosure

Master'sMASTER OF ART

Relación entre el crowdsourcing y la inteligencia colectiva: el caso de los sistemas de etiquetado social

El crowdsourcing es un término acuñado recientemente que hace referencia a un tipo de iniciativas que se dan en Internet. En estas iniciativas, alguien, ya sea una empresa, una persona o una institucion, propone a la multitud de Internet la realización de una tarea a cambio de una recompensa. Para que estas iniciativas se puedan llevar a cabo, Internet, y más concretamente, el desarrollo de la Web 2.0, ha sido fundamental. Internet, además de suponer la base tecnológica sobre la que se asienta el crowdsourcing, permite a este tipo de iniciativas tener acceso a cientos de miles de individuos de cualquier parte del mundo. Al haber sido un término acuñado recientemente, la literatura existente es escasa, realidad que va subsanándose paulatinamente. Además, las fronteras conceptuales del término son difusas. Por esta razón, muchas veces se confunde el crowdsourcing con procesos relacionados aunque no exactamente iguales, como la innovación abierta, la co-creación o la inteligencia colectiva. La presente tesis tiene como objetivo clarificar cual es exactamente la relación existente entre el crowdsourcing y uno de estos fenómenos: la inteligencia colectiva. Con este fin, se analizarán los sistemas de etiquetado social, una aplicación Web 2.0 claramente perteneciente al ámbito de la Inteligencia Colectiva, para observar las diferencias y semejanzas entre ésta y el crowdsourcing. En el camino que se recorre para identificar y analizar esta relación, se alcanzan otros hitos relevantes que ayudan a conseguir el objetivo de la tesis. En lo que al crowdsourcing respecta, se ha definido este término en base a ocho elementos, lo que facilita la identificación de qué es o no crowdsourcing. También se ha desarrollado una tipología de iniciativas de crowdsourcing en base a otras tipologías propuestas por diferentes autores. En cuanto a los sistemas de etiquetado social, se ha analizado y descrito el uso que hacen los usuarios de las etiquetas que describen los recursos de Internet, además de explicar como estos sistemas pueden favorecer los procesos de investigación colaborativos.Estellés Arolas, E. (2013). Relación entre el crowdsourcing y la inteligencia colectiva: el caso de los sistemas de etiquetado social [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31661TESI

Blog content mining: topic identification and evolution extraction.

Ng, Kuan Kit.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 92-100).Abstract also in Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Blog Overview --- p.2Chapter 1.2 --- Motivation --- p.4Chapter 1.2.1 --- Blog Mining --- p.5Chapter 1.2.2 --- Topic Detection and Tracking --- p.8Chapter 1.3 --- Objectives and Contributions --- p.9Chapter 1.4 --- Proposed Methodology --- p.11Chapter 2 --- Related Work --- p.13Chapter 2.1 --- Web Document Clustering --- p.13Chapter 2.2 --- Document Clustering with Temporal Information --- p.15Chapter 2.3 --- Blog Mining --- p.17Chapter 3 --- Feature Extraction and Selection --- p.20Chapter 3.1 --- Blog Extraction and Content Cleaning --- p.21Chapter 3.1.1 --- Blog Parsing and Structure Identification --- p.22Chapter 3.1.2 --- Stop-word Removal --- p.24Chapter 3.1.3 --- Word Stemming --- p.25Chapter 3.1.4 --- Heuristic Content Cleaning and Multiword Grouping --- p.25Chapter 3.2 --- Feature Selection --- p.26Chapter 3.2.1 --- Term Frequency Inverse Document Frequency --- p.27Chapter 3.2.2 --- Term Contribution --- p.29Chapter 4 --- Blog Topic Extraction --- p.31Chapter 4.1 --- Requirements of Document Clustering --- p.32Chapter 4.1.1 --- Vector Space Modeling --- p.32Chapter 4.1.2 --- Similarity Measurement --- p.33Chapter 4.2 --- Document Clustering --- p.34Chapter 4.2.1 --- Partitional Clustering --- p.36Chapter 4.2.2 --- Hierarchial Clustering --- p.37Chapter 4.2.3 --- Density-Based Clustering --- p.38Chapter 4.3 --- Proposed Concept Clustering --- p.40Chapter 4.3.1 --- Semantic Distance between Concepts --- p.43Chapter 4.3.2 --- Bounded Density-Based Clustering --- p.47Chapter 4.3.3 --- Document Assignment with Topic Clusters --- p.57Chapter 4.4 --- Discussion --- p.58Chapter 5 --- Blog Topic Evolution --- p.61Chapter 5.1 --- Topic Evolution Graph --- p.61Chapter 5.2 --- Topic Evolution --- p.64Chapter 6 --- Experimental Result --- p.69Chapter 6.1 --- Evaluation of Topic Cluster --- p.70Chapter 6.1.1 --- Evaluation Criteria --- p.70Chapter 6.1.2 --- Evaluation Result --- p.73Chapter 6.2 --- Evaluation of Topic Evolution --- p.79Chapter 6.2.1 --- Results of Topic Evolution Graph --- p.80Chapter 6.2.2 --- Evaluation Criteria --- p.82Chapter 6.2.3 --- Evaluation of Topic Evolution --- p.83Chapter 6.2.4 --- Case Study --- p.84Chapter 7 --- Conclusions and Future Work --- p.88Chapter 7.1 --- Conclusions --- p.88Chapter 7.2 --- Future Work --- p.90Bibliography --- p.92Chapter A --- Stop Word List --- p.101Chapter B --- Feature Selection Comparison --- p.104Chapter C --- Topic Evolution --- p.106Chapter D --- Topic Cluster --- p.10

Security Analysis of System Behaviour - From "Security by Design" to "Security at Runtime" -

The Internet today provides the environment for novel applications and processes which may evolve way beyond pre-planned scope and purpose. Security analysis is growing in complexity with the increase in functionality, connectivity, and dynamics of current electronic business processes. Technical processes within critical infrastructures also have to cope with these developments. To tackle the complexity of the security analysis, the application of models is becoming standard practice. However, model-based support for security analysis is not only needed in pre-operational phases but also during process execution, in order to provide situational security awareness at runtime. This cumulative thesis provides three major contributions to modelling methodology. Firstly, this thesis provides an approach for model-based analysis and verification of security and safety properties in order to support fault prevention and fault removal in system design or redesign. Furthermore, some construction principles for the design of well-behaved scalable systems are given. The second topic is the analysis of the exposition of vulnerabilities in the software components of networked systems to exploitation by internal or external threats. This kind of fault forecasting allows the security assessment of alternative system configurations and security policies. Validation and deployment of security policies that minimise the attack surface can now improve fault tolerance and mitigate the impact of successful attacks. Thirdly, the approach is extended to runtime applicability. An observing system monitors an event stream from the observed system with the aim to detect faults - deviations from the specified behaviour or security compliance violations - at runtime. Furthermore, knowledge about the expected behaviour given by an operational model is used to predict faults in the near future. Building on this, a holistic security management strategy is proposed. The architecture of the observing system is described and the applicability of model-based security analysis at runtime is demonstrated utilising processes from several industrial scenarios. The results of this cumulative thesis are provided by 19 selected peer-reviewed papers

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Enterprise modelling framework for dynamic and complex business environment: socio-technical systems perspective

The modern business environment is characterised by dynamism and ambiguity. The causes include global economic change, rapid change requirements, shortened development life cycles and the increasing complexity of information technology and information systems (IT/IS). However, enterprises have been seen as socio-technical systems. The dynamic complex business environment cannot be understood without intensive modelling and simulation. Nevertheless, there is no single description of reality, which has been seen as relative to its context and point of view. Human perception is considered an important determinant for the subjectivist view of reality. Many scholars working in the socio-technical systems and enterprise modelling domains have conceived the holistic sociotechnical systems analysis and design possible using a limited number of procedural and modelling approaches. For instance, the ETHICS and Human-centred design approaches of socio-technical analysis and design, goal-oriented and process-oriented modelling of enterprise modelling perspectives, and the Zachman and DoDAF enterprise architecture frameworks all have limitations that can be improved upon, which have been significantly explained in this thesis. [Continues.

Evaluating the usability of a tag-based, multi-faceted knowledge organization system

Evaluació i desenvolupament d'una interfície per al sistema de tags, amb conceptes de jerarquia i facetes, TACKO (TAg-based Context-dependant Knowledge Organization System) desenvolupat a la Technische Universität München