Computing trust from revision history
|Conference paper (help)|
|Computing trust from revision history|
|Authors:||Honglei Zeng, Maher A. Alhossaini, Li Ding, Richard Fikes, Deborah L. McGuinness|
|Citation:||Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services 380 in ACM International Conference Proceeding Series : 8. 2006|
|Publisher:||Association for Computing Machinery, New York, NY, USA|
|Meeting:||2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services|
|Web:||DuckDuckGo Bing Google Yahoo! — Google PDF|
|Article:||Google Scholar PubMed|
|Restricted:||DTU Digital Library|
They consider 3 different trusts:
- Article trust
- Fragment trust
- Author trust
The main focus of the paper is article trust. Their trust value is a continuous value between 0 and 1.
Their dynamic Bayesian model is a Markov chain, where the posterior density distribution of the trust of an article (t_V_i+1) is dependent upon:
- The trust of the previous article version (t_v_i)
- The new insertion (i_i)
- The new deletion (d_i)
- The new author (t_A_i+1)
Beta distributions are used to model the probabilities and the BUGS software. The authors apply different priors based on user level: administrators, registered users, anonymous authors, and blocked authors. Trust from insertion and deletion are based on the size of the edit.
The longest common subsequence algorithm was used to compute the diff between consecutive articles.
The also trained a classifier to classify between featured and non-featured articles.
Data from the English Wikipedia from the geography category in January 2006 was used with:
- 50 featured articles
- 50 "clean-up" articles
- 768 normal articles
With a total of 40450 revisions.
For testing a classifier a further 200 new articles was also used.
Their classifier could predict featured article status with 82% and clean-up articles with 84%.
- The authors considers deletions and insertions (section 3.1), but what about moves?