B. Thomas Adler
From WikiPapers
| B. Thomas Adler (Alternative names for this author) | |
 | |
| Affiliation | Unknown [+] |
| Country | Unknown [+] |
| Co-authors | Andrew G. West, Ian Pye, Krishnendu Chatterjee, Luca de Alfaro, Marco Faella, Paolo Rosso, Santiago M. Mola Velasco, Vishwanath Raman |
| Website | http://users.soe.ucsc.edu/~thumper/ |
| Statistics | |
| Authorship | Publications (6), datasets (0), tools (1) |
| Citations | Total (20), average (3.33333333333), median (2.5), max (9), min (0) |
| Keywords | |
| Search | |
| DBLP · Google Scholar | |
| Export and share | |
| BibTeX, CSV, RDF, JSON | |
      
| |
| Browse properties · List of authors | |
B. Thomas Adler is an author.
Tools
| Tool | Description |
|---|---|
| WikiTrust | WikiTrust is an open-source, on-line reputation system for Wikipedia authors and content. |
Publications
Only those publications related to wikis are shown here.| Title | Keyword(s) | Published in | Language | DateThis property is a special property in this wiki. | Abstract | R | C |
|---|---|---|---|---|---|---|---|
| WikiTrust: Content-Driven Reputation for the Wikipedia | English | June 2012 | 0 | 0 | |||
| Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features | Wikipedia Wiki Collaboration Vandalism Machine learning Metadata Natural Language Processing Reputation |
Lecture notes in computer science | English | February 2011 | Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions. | 0 | 1 |
| Detecting Wikipedia Vandalism using WikiTrust - Lab Report for PAN at CLEF 2010 | English | 2010 | 0 | 1 | |||
| Assigning Trust to Wikipedia Content | WikiSym | English | 2008 | The Wikipedia is a collaborative encyclopedia: anyone can contribute to its articles simply by clicking on an "edit" button. The open nature of the Wikipedia has been key to its success, but has also created a challenge: how can readers develop an informed opinion on its reliability? We propose a system that computes quantitative values of trust for the text in Wikipedia articles; these trust values provide an indication of text reliability. The system uses as input the revision history of each article, as well as information about the reputation of the contributing authors, as provided by a reputation system. The trust of a word in an article is computed on the basis of the reputation of the original author of the word, as well as the reputation of all authors who edited text near the word. The algorithm computes word trust values that vary smoothly across the text; the trust values can be visualized using varying text-background colors. The algorithm ensures that all changes to an article's text are reflected in the trust values, preventing surreptitious content changes. We have implemented the proposed system, and we have used it to compute and display the trust of the text of thousands of articles of the English Wikipedia. To validate our trust-computation algorithms, we show that text labeled as low-trust has a significantly higher probability of being edited in the future than text labeled as high-trust. | 0 | 5 | |
| Measuring Author Contributions to the Wikipedia | WikiSym | English | 2008 | 0 | 4 | ||
| A content-driven reputation system for the Wikipedia | English | 2007 | We present a content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits they perform to Wikipedia articles are preserved by subsequent authors, and they lose reputation when their edits are rolled back or undone in short order. Thus, author reputation is computed solely on the basis of content evolution; user-to-user comments or ratings are not used. The author reputation we compute could be used to flag new contributions from low-reputation authors, or it could be used to allow only authors with high reputation to contribute to controversialor critical pages. A reputation system for the Wikipedia could also provide an incentive for high-quality contributions. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691,551 pages and 5,587,523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, as judged by human observers, and of being later undone, as measured by our algorithms. | 0 | 9 |
