Identifying Influential Bloggers: Time Does Matter
- 1. Identifying Influential Bloggers Time Does Matter WI/IAT 2009, September 15-18, Milan, Italy Leonidas Akritidis Dimitrios Katsaros Panayiotis Bozanis
- 2. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 2 The Evolution of Web 2.0 • Massive transition in the applications and services hosted on the Web. • The obsolete static Web sites have been replaced by numerous, novel, interactive services • New Feature: Dynamic Content
- 3. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 3 Virtual Communities • Web 2.0 includes virtual communities where the users share 1. Ideas 2. Knowledge 3. Experiences 4. Opinions 5. Files (Media Content, Images, Audio, Video) • Examples Include 1. Blogs 2. Forums 3. Wikis 4. Media Sharing Services 5. Bookmarks Sharing Services
- 4. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 4 Blogs • Blogs are locations on the Web where some individuals (the bloggers) express their opinions or experiences about various subjects. • Such entries are called blog posts • The readers submit their own comments to the original blog post.
- 5. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 5 Posts • A post is characterized by the blogger’s name and the publication date. • We know who wrote this post and when. • It may contain text, images, videos or sounds and links to other blog posts and Web pages.
- 6. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 6 Blogosphere • The virtual universe that contains all blogs • Accommodates two types of blogs: 1. Individual blogs, maintained and updated by one blogger 2. Community blogs, or multi-authored blogs, where several bloggers may start discussions • We focus only on community blogs.
- 7. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 7 The Influentials • In a physical world, people use to consult others about a variety of issues: • Which restaurant to choose, which place to visit, which movie to watch. • These others are the influentials. • This is also valid for Blogosphere
- 8. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 8 Identifying the Influential Bloggers: Why is it important? • The influentials help others in their decision making and their opinion is important. • Companies can “use” them as “unofficial spokesmen”, instead of advertising their products. • They are considered as market movers. (Don’t buy this, but this). • They can forge political agendas (Don’t vote him).
- 9. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 9 Identification of Influential Bloggers • It seems similar to the problem of identifying influential blog sites and authoritative Web pages. • However, the techniques proposed for these problems cannot be applied to our case.
- 10. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 10 Existing Models • Very few • Not relative • Blogosphere modeling, mining, trust/reputation, spam blog recognition, discovering and analyzing blog communities • Relative: • Influence Flow Model
- 11. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 11 Influence Flow Model (IFM, 1) • It is based on four parameters • Recognition (number of incoming links), • Activity Generation (number of comments) • Novelty (i.p. to number of outgoing links) • Eloquence (i.p. to the post’s length).
- 12. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 12 Influence Flow Model (IFM, 2) • An influence score is calculated for each post. The post with the maximum influence score is used as the blogger’s representative post. • w(λ) is a weight function of the post’s length • wcom regulates the contribution of the number of comments γ(p). • win and wout adjust the contribution of the incoming and outgoing influence.
- 13. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 13 IFM Drawbacks • Isolating a single post is simplistic. • A blogger may have written only a handful of influential posts and numerous others of low quality. Productivity is overlooked. • It depends on user defined weights. Changing the values of the weights leads to alternative rankings. • It ignores a very important factor: Time. • It uses demanding and unstable recursive definitions
- 14. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 14 Measuring a Blogger’s Influence • Number of Posts (Productivity) • Age of the Posts • Number of Incoming Links • Age of the Incoming Links • Number of Comments • We argue that the outgoing Links weaken a post’s influence.
- 15. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 15 MEIBI Scores • Metric for Identifying a Blogger’s Influence • Assigns a score to the ith post of the jth blogger • ΔTPj(i): time interval (in days) between current time and the date that the post i was submitted. • Rj(i): posts referring the ith post of blogger j. • C(i): the set of comments to post i of blogger j • γ=4, δ=1
- 16. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 16 MEIBI Definition • A blogger j has MEIBI index equal to m, if m of his/her BP(j) posts get a score each and the rest BP(j) − m posts get a score • This definition awards both influence and productivity • A blogger will be influential if s/he has posted several influential posts recently. miSm j miSm j
- 17. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 17 Motivations • An old post may still be influential. • How could we deduce this? • We examine the age of the incoming links • If a post is not cited anymore, it is an indication that it negotiates outdated topics • On the other hand, if an old post continues to be linked presently, then it probably contains influential material.
- 18. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 18 • We assign to each incoming link of a post a weight depending on the link’s age. • ΔTP(x): time interval between current time and the date that the post x was submitted. MEIBIX Scores
- 19. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 19 MEIBIX Definitions • A blogger j has MEIBIX index equal to x, if x of his/her BP(j) posts get a score each, and the rest BP(j)−x posts get a score xiSx j xiSx j
- 20. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 20 Experiments: Dataset • Millions of blog sites exist • It is essential to detect an active blog community that provides 1. Blogger Identification 2. Date and time of posts 3. Number of comments 4. Number of outgoing links.
- 21. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 21 Data Characteristics • The Unofficial Apple Weblog (TUAW) meets all these requirements. • Crawled in the first week of Dec. 2008. • 160,000 pages. • 17,831 blog posts. • 51 unique bloggers. • 269,449 comments. • 5 years of blogging activity.
- 22. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 22 Inlinks: Age • Posts get old very quickly • The majority of links come within a few hours after the post’s submission
- 23. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 23 Plain Methods for Bloggers Ranking • Ranking by blogging activity • S. Mc Nulty is the most active blogger • He has been inactive in the last 5 months
- 24. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 24 Plain Methods for Bloggers Ranking • Ranking by H-Index • E. Sadun is the most influential blogger • She has been inactive in the last 3 months
- 25. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 25 MEIBI Rankings • MEIBI considers Bohon as the most influential (793 posts, 676 cited posts, 9,439 inlinks and 14,745 comments) • Rose is the 5th, better than Warren
- 26. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 26 MEIBIX vs Plain Methods • The top four bloggers are the same. • On the other hand, MEIBIX considers Warren to be more influential than Rose.
- 27. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 27 Comparison • Rose: 793 posts, 364 cited posts, 4222 (5.3 per post) incoming links and 13499 comments (17 per post) • Warren: 133 posts, 112 cited posts, 1605 incoming links (12 per post) and 4857 comments (36.5 per post).
- 28. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 28 Conclusion • Rose has published more posts and received more incoming links and comments. • But Warren’s posts are more attractive. • Therefore, MEIBI is more sensitive to the overall performance of a blogger (productive bloggers) • And MEIBIX awards bloggers that publish more influential posts.
- 29. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 29 Rankings in limited time windows • We have tested our methods by only considering the posts published only the previous month (November 2008).
- 30. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 30 Comparison • The IFM method positions M. Lu in the 3rd place, higher than C.Warren and D.Caolo. • But for that specific month, Warren and Caolo have published more posts. • Moreover, their posts received more inlinks and comments. Hence, their posts were more influential than Lu’s. • MEIBI and MEIBIX produce fairer rankings
- 31. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 31 Blogging behavior over 2008: MEIBI • We have also studied the behavior of the TUAW bloggers over 2008. • Our models allow the observation of the ranking fluctuation.
- 32. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 32 Blogging behavior over 2008: MEIBIX • We see that E. Sadun was among the most influential bloggers two months ago, but she is currently inactive. • From the moment R. Palmer became active, he is among the most influential. • We can not say the same for S. Sande.
- 33. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 33 Conclusions • We have detected and studied the problem of identifying influential bloggers in a community. • We proposed two novel measures for that. • For the first time, we introduce temporal aspects to the identification of the influentials. • The two measures also award productivity.
- 34. U. of Thessaly, Greece WI/IAT 2009, Milan, Italy 34 Thank you Any Questions?