Blog Network Research Articles

National Cancer Information Service in Italy: an information points network as a new model for providing information for cancer patients.

The international literature data report that good information and communication are fundamental components of a therapeutic process. They contribute to improve the patient-health care professional relationship, to facilitate doctor-patient relationships, therapeutic compliance and adherence, and to the informed consent in innovative clinical trials. We report the results of a multicentric national initiative that developed a 17-information-structure network: 16 Information Points located in the major state-funded certified cancer centers and general hospitals across Italy and a national Help-line at the nonprofit organization AIMaC (the Italian oncologic patients, families and friends association), and updated the already existing services with the aim to create the National Cancer Information Service (SION). The project is the result of a series of pilot and research projects funded by the Italian Ministry of Health. The Information Service model proposed is based on some fundamental elements: 1) human interaction with experienced operators, adequately trained in communication and information, complemented with 2) virtual interaction (Help line, Internet, blog, forum and social network); 3) informative material adequate for both scientific accuracy and communicative style; 4) adequate locations for appropriate positioning and privacy (adequate visibility); 5) appropriate advertising. First results coming from these initiatives contributed to introduce issues related to "Communication and Information to patients" as a "Public Health Instrument" to the National Cancer Plan approved by the Ministry of Health for the years 2010-2012.

Adapting Stochastic Block Models to Power-Law Degree Distributions.

Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.

Entrevista a un cavernísofo

Entrevista a Juan. A. Negrete, premio anual 2014 a la promoción de la filosofía y la cultura en Málaga. El premio se hace extensivo a la red de blogs: Cavernisofía, Cavernética y Cavernisofíasegundaplanta.

Trabajo entre pares en la curación digital de contenidos curriculares / Peer-working in the digital curation of curricular contents

In this paper it is presented a collaborative work by peers for the digital curation of curricular contents in high education. To do so it was created a net of blogs in which the digital resources related to these curricular contents were published. This was the information on which the content healing work was done through a peer assessment. Furthermore, the results of the assessment and opinion of the students about it are shown, based on information of a qualitative and quantitative nature. A total of 69 students (13 females and 56 males) from the second grade in Physical Activity and Sport Sciences participated in the experience. However, only 50 of the participants (10 females and 40 males) remained active until end of the work. As results, a good feedback was obtained from both the creation of the blog network and the peer evaluation performed, highlighting the amount of information obtained through collaborative work. However, some aspects of improvement were detected, such as the reliability of the sources of information, the intra-group organization and the management of autonomy, elements in which it is necessary to continue working to consolidate the network of collaborative work in higher education.

Social media and political communication

Abstract As the Internet has come to play a greater role in politics, there has been a growing scholarly interest in how digital and social media are changing politics. The competition between the innovation and normalization hypotheses has been at the center of the debate. This article sets out to identify evidence of innovation and normalization in terms of how politicians communicate in- and the level of influence they are attaining within the political blogosphere. The analyses conducted show paradoxical results as we find that those groups of politicians who are utilizing political communication in the blogosphere in more innovative and progressive ways – mirroring the hopes and expectations about how social media might influence politicians and political communication – have weaker positions within the blog network compared to other politicians.

Finding multiple core-periphery pairs in networks.

With a core-periphery structure of networks, core nodes are densely interconnected, peripheral nodes are connected to core nodes to different extents, and peripheral nodes are sparsely interconnected. Core-periphery structure composed of a single core and periphery has been identified for various networks. However, analogous to the observation that many empirical networks are composed of densely interconnected groups of nodes, i.e., communities, a network may be better regarded as a collection of multiple cores and peripheries. We propose a scalable algorithm to detect multiple nonoverlapping groups of core-periphery structure in a network. We illustrate our algorithm using synthesized and empirical networks. For example, we find distinct core-periphery pairs with different political leanings in a network of political blogs and separation between international and domestic subnetworks of airports in some single countries in a worldwide airport network.

A Block Model for Node Popularity in Networks with Community Structure

SummaryThe community structure that is observed in empirical networks has been of particular interest in the statistics literature, with a strong emphasis on the study of block models. We study an important network feature called node popularity, which is closely associated with community structure. Neither the classical stochastic block model nor its degree-corrected extension can satisfactorily capture the dynamics of node popularity as observed in empirical networks. We propose a popularity-adjusted block model for flexible and realistic modelling of node popularity. We establish consistency of likelihood modularity for community detection as well as estimation of node popularities and model parameters, and demonstrate the advantages of the new modularity over the degree-corrected block model modularity in simulations. By analysing the political blogs network, the British Members of Parliament network and the ‘Digital bibliography and library project’ bibliographical network, we illustrate that improved empirical insights can be gained through this methodology.

Completeness of Community Structure in Networks

By defining a new measure to community structure, exclusive modularity, and based on cavity method of statistical physics, we develop a mathematically principled method to determine the completeness of community structure, which represents whether a partition that is either annotated by experts or given by a community-detection algorithm, carries complete information about community structure in the network. Our results demonstrate that the expert partition is surprisingly incomplete in some networks such as the famous political blogs network, indicating that the relation between meta-data and community structure in real-world networks needs to be re-examined. As a byproduct we find that the exclusive modularity, which introduces a null model based on the degree-corrected stochastic block model, is of independent interest. We discuss its applications as principled ways of detecting hidden structures, finding hierarchical structures without removing edges, and obtaining low-dimensional embedding of networks.

Networking de conocimiento en sociología: análisis de redes de blogs, vídeos de youtube y comentarios en twitter sobre sociología

Aunque los estudios sobre la producción de conocimiento científico han crecido ampliamente en los últimos años con la cienciometría y bibliometría, poco se ha investigado sobre otras fuentes alternativas de producción y divulgación de conocimiento como blogs, vídeos de YouTube y comentarios en Twitter. Estas fuentes online de conocimiento cobran especial relevancia en disciplinas como la Sociología, donde algunos académicos buscan acercar el conocimiento sociológico a la población general. Para explorar qué conocimiento sobre Sociología se produce y divulga en estas fuentes online y cómo se organiza, estudiamos la generación de redes de conocimiento (networking de conocimiento) en blogs, vídeos de YouTube y comentarios en Twitter usando la metodología de análisis de redes. Específicamente, la presente investigación analiza las redes de hipervínculos de los principales blogs de Sociología, las redes de las etiquetas de vídeos sobre Sociología alojados en YouTube, y las redes de hashtags en Twitter vinculados con #sociología. Los principales resultados señalan que existe una comunidad de blogs de Sociología cohesionada y fuertemente conectada, hay baja presencia de vídeos sobre Sociología en español, y la Sociología aparece vinculada en Twitter con otras ciencias sociales, autores clásicos y social media.

A problem-centered analysis of enterprise social software projects

The use of social software in organizations has matured over the last years. Enterprise social software such as a wiki, blog and social network creates value for employees, optimizes workflows and contributes to improved collaboration within and across enterprises. However, many organizations face problems and barriers during the introduction of social software tools and platforms, which can have a decisive impact on the overall success. Through a comprehensive analysis of 13 case studies and 6 problem-centered interviews with experts of social software introduction, a framework for problems occurring in enterprise social software projects was created. In total, 21 different problems that influence a social software introduction were identified and clustered into the areas of project management, technology, culture, top management, and employees.

An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges

Since the last two decades, XML has gained momentum as the standard for web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.

La intimidación entre los escolares y nuevas formas de violencia

This article is intended to collect some results of the several studies that have been made concerning Bullying and Harassment among boys and girls who attend basic education institutions in the central part of Santa Cruz province, in Argentina. It encloses theoretical framework about the problem of bullying and violence among children at school. It presents information about the region, some common aggressive behaviors as well as different ways and places in which those violent habits might be produced, according to children. Describes school populations suffering from such practices and analyzes some differences from school years, gender and zonal characteristics. It also shows samples of Victimization Rate and General Aggression Rate got in the area. Open to further discussion, this work provides certain description about some bullying phenomenon in the area, such as the case of the “Round-up Effect” , produced by the lack of anonymity in barely populated places. This article presents new emerging issues in the research field, like the appearance of n ew ways of violence , related to digital communication technologies such as instant messaging, e-mail, chat, blog, photoblog and social networks. To conclude, it offers some ideas about the construction of Early Warning Devices and Early Intervention Devices.

Corporate Blogging and Job Performance: Effects of Work-related and Nonwork-related Participation

Corporate blogs are expected to facilitate communication, knowledge sharing, and collaborative innovation within organizations. However, empirical evidence has yet to be found illustrating whether and how such applications have affected job performance. Drawing upon social network theory, we postulate a conceptual model suggesting that employees’ online social relationships accumulated through work- and nonwork-related blog participation will engender different effects on job performance. The model is empirically tested using digital trace and archival data collected from two in-practice systems of a large telecommunications company. The results reveal that, in the work-related blog network, the structural and cognitive dimensions of social relationships positively affect job performance, whereas the relational dimension shows a negative influence. Meanwhile, participation in nonwork-related blog network benefits job performance for employees with a high level of performance in the previous time period, but is detrimental for other employees. The findings uncover the influencing mechanism of corporate blogging on job performance and offer practical advice for managers to better exploit the value of intraorganizational social media.

Vertex nomination schemes for membership prediction

Suppose that a graph is realized from a stochastic block model where one of the blocks is of interest, but many or all of the vertices' block labels are unobserved. The task is to order the vertices with unobserved block labels into a ``nomination list'' such that, with high probability, vertices from the interesting block are concentrated near the list's beginning. We propose several vertex nomination schemes. Our basic - but principled - setting and development yields a best nomination scheme (which is a Bayes-Optimal analogue), and also a likelihood maximization nomination scheme that is practical to implement when there are a thousand vertices, and which is empirically near-optimal when the number of vertices is small enough to allow comparison to the best nomination scheme. We then illustrate the robustness of the likelihood maximization nomination scheme to the modeling challenges inherent in real data, using examples which include a social network involving human trafficking, the Enron Graph, a worm brain connectome and a political blog network.

Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood

The stochastic block model (SBM) is a mixture model for the clustering of nodes in networks. The SBM has now been employed for more than a decade to analyze very different types of networks in many scientific fields, including biology and the social sciences. Recently, an analytical expression based on the collapsing of the SBM parameters has been proposed, in combination with a sampling procedure that allows the clustering of the vertices and the estimation of the number of clusters to be performed simultaneously. Although the corresponding algorithm can technically accommodate up to 10 000 nodes and millions of edges, the Markov chain, however, tends to exhibit poor mixing properties, that is, low acceptance rates, for large networks. Therefore, the number of clusters tends to be highly overestimated, even for a very large number of samples. In this article, we rely on a similar expression, which we call the integrated complete data log likelihood, and propose a greedy inference algorithm that focuses on maximizing this exact quantity. This algorithm incurs a smaller computational cost than existing inference techniques for the SBM and can be employed to analyze large networks (several tens of thousands of nodes and millions of edges) with no convergence problems. Using toy datasets, the algorithm exhibits improvements over existing strategies, both in terms of clustering and model selection. An application to a network of blogs related to illustrations and comics is also provided.

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Quantifying Partisan Selective Exposure Through Network Text Analysis of Elite Political Blog Networks During the U.S. 2012 Presidential Election

ABSTRACTUtilizing network-based content analysis methodologies, this study examined 316,594 hyperlinks and 60,378 headlines culled from 20 elite, partisan political blogs through 10 months of the U.S. 2012 presidential year for evidence of partisan selective exposure in blog production practices. Hyperlinks reveal dense intra-interconnectivity among progressive blogs as opposed to conservative blogs. Elite, traditional media sources operated as bridges between spheres that otherwise tended to partisan selective exposure in their partisan source linking practices toward congenial media. Headline network text analysis revealed divergent issue and people agendas promoted between both partisan blog networks, providing very strong evidence for the operation of partisan selective exposure in the emergent content practices of these elite produser publics across the 2012 U.S. presidential year.

Research on the Design and Application of Micro Blog Network Marketing into the Global Trade

The emergence and rapid development of Micro Blog has provided a new, swift and effective tool for the international trade. Nowadays this new tool is called Micro Blog internet marketing. Compared with blog, the micro log is much more convenient and the issued content is much more brief which makes the users issue messages timely and usually it is a much more effective channel for the readers and the bloggers to communicate with each other. Micro blog has quickly become a popular tool to communicate and every website has begun to open their own micro blog to propaganda their companies. The micro blog internet marketing can reduce the cost of international trade, improve trade efficiency and customer satisfaction and improve the enterprises’ awareness of competition. Therefore to lay much more emphasis on the role that the micro blog internet marketing has played in international trade is a must in the new century. This is also a way to increase the construction of internet information, thus regulating the micro internet marketing and promoting the healthy development of this field.

“Newbies” and “Celebrities”: Detecting social roles in an online network of teachers via participation patterns

The advent of social networking tools allows teachers to create online networks and share information. While some virtual networks have a formal structure and defined boundaries, many do not. These unstructured virtual networks are difficult to study because they lack defined boundaries and a formal structure governing leadership roles and the transfer of information. The purpose of the study was to explore the relationship between how a member participates in a virtual blog network and the role of that member in the network. Unlike previous studies that use behavioral or structural characteristics of an individual’s network to infer social roles, this study utilized cluster analysis to combine behavior and structural information in role detection. Quantitative methods from social network analysis were used to compare the network structure of individual bloggers both across and within groups. The results indicate that how an individual participates in the network has an influence not only on their current role in the network, but also in how and how quickly their role in the community changes.

Anti-Muslim prejudice in the virtual space: A case study of blog network structure and message features of the ‘Ground Zero mosque controversy’

The purpose of this study is to understand how an extremist blog advanced its perspective in the public sphere and further generated public support for prejudicial opinions against Muslims in the US. The authors utilize the case study method, hyperlink network analysis and centering resonance analysis to uncover details of the linkage and message features of the blog. The findings show that this blog developed a highly polarized network that engaged like-minded others, prominent public figures and mainstream media. Additionally, content of the blog employed cultural stereotypes that resonated deeply with public sentiments. Further, the network position and content features of the blog were not static attributions, but constantly evolved as the issue developed. Theoretical and political implications are discussed.