Identifying influential bloggers: WSDM 2008

Citation

Nitin Agarwal, Huan Liu, Lei Tang, Philip S. Yu, "Identifying the Influential Bloggers in a Community", Proceedings of the International Conference on Web Search and Web Data Mining (WSDM), 2008.

Online version

Available at Citesteer

Summary

This paper aims at identifying most influential bloggers in a blogging community. The paper first proposes some metric for assessing how influential a blog post is. Then the authors perform some experiments on blogs from few blog sites and qualitatively evaluate their results.

What makes a Blog influential

Recognition: An influential blog post is recognized by many, which can be judged by the number of in-links ($\iota$), i.e. the number of other posts referencing the particular post.
Activity Generation: A blog post that generates more activity is supposedly more influential. This is measured by the number of comments made on the blog post ($\gamma$).
Novelty: Novel ideas are supposed to be more influential [1]. A post that references more other posts (or has more out-links) is supposed to have lesser novel ideas. So novelty can be taken as negatively correlated with the number of out-links ($\theta$).
Eloquence: More eloquent posts are more influential [1]. Authors use the length of the blog post ($\lambda$) as a measure of eloquence.

Measuring Influence

The authors define a concept called InfluenceFlow. They conjecture that blog-influence flow can be thought of as a graph. For a post p having no. if in-links $\iota$ and no. of out-links $\theta$, the InfluenceFlow is defined as:
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle InfluenceFlow(p)= w_{in} \Sigma (m=1\ to\ \iota) I(p_m) – w_{out}\Sigma (n=1\ to\ \theta) I(p_n)}
Where win and wout are the weights that can be adjusted for incoming and outgoing influences; pm denotes the blog post that links to the post p, and pn denotes the post to which the post p links; I(px) is the influence score of the post px. Note that unfortunately the paper doesn’t mention how I score is computed from the four parameters discussed above. Authors further define the influence I for a post in terms of the InfluenceFlow, which looks weird, since they’ve already used I score in defining InfluenceFlow. I(p) ∝ wcomγp + InfluenceFlow(p) Where γp is the no. of comments made to the post p, and wcom is a regulating coefficient. For the constant of proportionality, authors use a measure of the quality of the blog. However, the measure used by authors is quite naive and is actually a function of the length of the blog post w(λ). So I(p) = w(λ)x (wcomγp + InfluenceFlow(p)) Authors further define iIndex(B) for a blogger B as max(I(pi)) where I(pi) is the influence score of a post made by blogger B. The higher the value of iIndex for any blogger, more influential they are considered.

Results

Two metrics were used: R-Precision and NDCG. NDCG is described in [2].

References

[1] D. Shen, Q. Yang, J.-T. Sun, and Z. Chen. Thread detection in dynamic text message streams. In Proc. of SIGIR ’06, pages 35–42, Seattle, Washington, 2006.
[2] K. Jrvelin and J. Keklinen. IR evaluation methods for retrieving highly relevant documents. In Proc. of SIGIR ’00, pages 41–48, Athens, Greece, 2000.