PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach (original) (raw)
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MIIB: A Metric to Identify Top Influential Bloggers in a Community
PLOS ONE, 2015
Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.
Modelling to identify influential bloggers in the blogosphere: A survey
The user participatory nature of the social web has revolutionized the use of the conventional web. The social web is an integral part of our daily life. Due to the resulting exponential growth of the social web, a number of research domains have emerged, involving research activities that aim to study human nature, to analyse human sentiments and emotions, and to find the impact of various users in the social networks. Recently, the research focus has shifted to identifying a user's influence on other users in a social network. In the recent literature, we find a number of models proposed to find the most influential users in the blogging community. In this paper, we review the models to find these influential bloggers. The existing models are classified into feature-based and network-based categories. The feature-based models consider the salient factors to measure bloggers' influence. The network models, on the other hand, consider the graph-based social network structure of the bloggers to identify those who have the most impact on fellow members. This survey introduces each model with its features, novel aspects, and the datasets used. In addition to the discussion about the model, a comparative analysis of the datasets is presented. We conclude by discussing applications of the relevant literature, exploring open research issues and challenges, and sharing possible future directions in this active area of research.
RESEARCH ARTICLE MIIB: A Metric to Identify Top Influential Bloggers in a Community
2016
Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of com-merce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers ’ productivity and popularity. P...
Finding the top influential bloggers based on productivity and popularity features
New Review of Hypermedia and Multimedia, 2016
A blog acts as a platform of virtual communication to share comments or views about products, events and social issues. Like other social web activities, blogging actions spread to a large number of people. Users influence others in many ways, such as buying a product, having a particular political or social opinion or initiating new activity. Finding the top influential bloggers is an active research domain as it helps us in various fields, such as online marketing, e-commerce, product search and eadvertisements. There exist various models to find the influential bloggers, but they consider limited features using non-modular approach. This paper proposes a new model, Popularity and Productivity Model (PPM), based on a modular approach to find the top influential bloggers. It consists of popularity and productivity modules which exploit various features. We discuss the role of each proposed and existing features and evaluate the proposed model against the standard baseline models using datasets from the real-world blogs. The analysis using standard performance evaluation measures verifies that both productivity and popularity modules play a vital role to find influential bloggers in blogging community in an effective manner.
How Influential Are You: Detecting Influential Bloggers in a Blogging Community
Lecture Notes in Computer Science, 2012
Blogging is a popular activity with high impact on marketing, shaping public opinions, and informing the world about major events from a grassroots point of view. Influential bloggers are recognized by businesses as significant forces for product promotion or demotion, and by oppressive political regimes as serious threats to their power. This paper studies the problem of identifying influential bloggers in a blogging community, BlogCatalog, by using network centrality metrics. Our analysis shows that bloggers are connected in a core-periphery network structure, with the highly influential bloggers well connected with each others forming the core, and the non-influential bloggers at the periphery. The six node centrality metrics we analyzed are highly correlated, showing that an aggregate centrality score as a measure of influence will be stable to variations in centrality metrics.
A blog acts as a platform of virtual communication to share comments or views about products, events and social issues. Like other social web activities, blogging actions spread to a large number of people. Users influence others in many ways, such as buying a product, having a particular political or social opinion or initiating new activity. Finding the top influential bloggers is an active research domain as it helps us in various fields, such as online marketing, e-commerce, product search and eadvertisements. There exist various models to find the influential bloggers, but they consider limited features using non-modular approach. This paper proposes a new model, Popularity and Productivity Model (PPM), based on a modular approach to find the top influential bloggers. It consists of popularity and productivity modules which exploit various features. We discuss the role of each proposed and existing features and evaluate the proposed model against the standard baseline models using datasets from the real-world blogs. The analysis using standard performance evaluation measures verifies that both productivity and popularity modules play a vital role to find influential bloggers in blogging community in an effective manner.
Identifying the Productive and Influential Bloggers in a Community
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2000
Social networking has become one of the most important trends on the Web, leading to the development of several social applications such as blogs. Blogs are locations on the Web where individuals are provided with the ability to express their opinion, experience, and knowledge about a product, an event, or any other subject. The tremendous popularity of these services has rendered the problem of identifying the most influential bloggers significant, since its solution can lead to numerous major benefits for commerce, advertising, and searching. The current works on this topic either ignore temporal aspects or they fail to gracefully incorporate recency, productivity, and influence at the same time. This paper investigates the issue of identifying bloggers who are both productive and influential by introducing the blogger's productivity index and blogger's influence index. The proposed metrics are evaluated against the state-of-theart influential blogger identification methods by employing data collected from a real-world community blog site. The obtained results confirm that the new methods are able to identify significant patterns in the bloggers' behavior.
Identifying Influential Bloggers: Time Does Matter
2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2009
Blogs have recently become one of the most favored services on the Web. Many users maintain a blog and write posts to express their opinion, experience and knowledge about a product, an event and every subject of general or specific interest. More users visit blogs to read these posts and comment them. This "participatory journalism" of blogs has such an impact upon the masses that Keller and Berry argued that through blogging "one American in tens tells the other nine how to vote, where to eat and what to buy" . Therefore, a significant issue is how to identify such influential bloggers. This problem is very new and the relevant literature lacks sophisticated solutions, but most importantly these solutions have not taken into account temporal aspects for identifying influential bloggers, even though the time is the most critical aspect of the Blogosphere. This article investigates the issue of identifying influential bloggers by proposing two easily computed blogger ranking methods, which incorporate temporal aspects of the blogging activity. Each method is based on a specific metric to score the blogger's posts. The first metric, termed MEIBI, takes into consideration the number of the blog post's inlinks and its comments, along with the publication date of the post. The second metric, MEIBIX, is used to score a blog post according to the number and age of the blog post's inlinks and its comments. These methods are evaluated against the state-of-the-art influential blogger identification method utilizing data collected from a real-world community blog site. The obtained results attest that the new methods are able to better identify significant temporal patterns in the blogging behaviour.
Identifying Influential Bloggers in a Community
Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their virtual communities of similar interests. Activities happened in Blogosphere affect the external world. One way to understand the development on Blogosphere is to find influential blog sites. There are many non-influential blog sites which form the "the long tail". Regardless of a blog site being influential or not, there are influential bloggers. Inspired by the high impact of the influentials in a physical community, we study a novel problem of identifying influential bloggers at a blog site. Active bloggers are not necessarily influential. Influential bloggers can impact fellow bloggers in various ways. In this paper, we discuss the challenges of identifying influential bloggers, investigate what constitutes influential bloggers, present a preliminary model attempting to quantify an influential blogger, and pave the way for building a robust model that allows for finding various types of the influentials. To illustrate these issues, we conduct experiments with data from a real-world blog site, evaluate multi-facets of the problem of identifying influential bloggers, and discuss unique challenges. We conclude with interesting findings and future work.
Identifying the Influentials in Blogosphere
ABSTRACT Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. As its size doubled for every 6 months, Blogosphere is expanding with about 175,500 daily blogs. Bloggers form their virtual communities of similar interests. Activities happened in Blogosphere affect the external world in many ways.