1,065 research outputs found
Identifying Influential Bloggers: Time Does Matter
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" \cite{keller1}.
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
The Blogosphere at a Glance — Content-Based Structures Made Simple
A network representation based on a basic wordoverlap
similarity measure between blogs is introduced.
The simplicity of the representation renders
it computationally tractable, transparent and insensitive
to representation-dependent artifacts. Using
Swedish blog data, we demonstrate that the representation,
in spite of its simplicity, manages to capture
important structural properties of the content
in the blogosphere. First, blogs that treat similar
subjects are organized in distinct network clusters.
Second, the network is hierarchically organized as
clusters in turn form higher-order clusters: a compound
structure reminiscent of a blog taxonomy
Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
Identification of Influential Social Networkers
Online social networking is deeply interleaved in today\u27s lifestyle. People come together and build communities to share thoughts, offer suggestions, exchange information, ideas, and opinions. Moreover, social networks often serve as platforms for information dissemination and product placement or promotion through viral marketing. The success rate in this type of marketing could be increased by targeting specific individuals, called \u27influential users\u27, having the largest possible reach within an online community. In this paper, we present a method aiming at identifying the influential users within an online social networking application. We introduce ProfileRank, a metric that uses popularity and activity characteristics of each user to rank them in terms of their influence. We then assess this algorithm\u27s added value in identifying influential users compared to other commonly used social network analysis metrics, such as the betweenness centrality and the well-known PageRank, by performing an experimental evaluation on a synthetic and a real-life dataset. We also integrate all three metrics in a unified metric and measure its performance
An Information Diffusion-Based Recommendation Framework for Micro-Blogging
Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches
Continuance Intention of Food Blog Users in Pakistan
Purpose:
The purpose of this paper is to analyze the relationship between different factors affecting the interest of Pakistani blog users reading food blogs using components of the ECT model.
Methodology:
With the sample size of 392 food blog readers, the study analyzes the impact of expectation confirmation theory, blog user’s involvement, and habit on continuance intention of using the blog, and satisfaction level.
Findings:
User habit and user involvement both are positively related to factors which are users’ perceived enjoyment, satisfaction, and intention to revisit the blog. Users’ perceived enjoyment is positively related to user satisfaction and intention to revisit the food blog. Findings suggest that when bog users are satisfied, they intend to revisit the blog. Blogging time does not moderate the effect of habit on either perceived enjoyment, satisfaction, or continuance intention.
Conclusion:
It is concluded from the research that ECT can be applied to examine the satisfaction of blog users and their intention to continue blog use. However, further research is required to analyze the impact of ECT in another context apart from food blog readers and the blogging domain. This research extends the efforts of earlier research as previous research emphasized enjoyment and user involvement and rarely have, they covered the moderating effect caused by blogging time and the effect of blog users’ habits specifically in the food and beverage industry
Social influence analysis in microblogging platforms - a topic-sensitive based approach
The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need
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