Damage detection and mitigation in open collaboration applications.

Format:

Book

Thesis/Dissertation

Author/Creator:

West, Andrew G.

Contributor:

Kannan, Sampath, committee member.

Haeberlen, Andreas, committee member.

De Alfaro, Luca, 1966- committee member.

Smith, Jonathan M., committee member.

Sokolsky, Oleg, advisor.

Lee, Insup, advisor.

University of Pennsylvania. Computer and Information Science.

Language:

English

Subjects (All):

Computer science.

Information science.

Information technology.

0489.

0723.

0984.

Penn dissertations--Computer and Information Science.

Computer and Information Science--Penn dissertations.

Local Subjects:

Penn dissertations--Computer and Information Science.

Computer and Information Science--Penn dissertations.

0489.

0723.

0984.

Physical Description:

137 pages

Contained In:

Dissertation Abstracts International 75-01B(E).

System Details:

Mode of access: World Wide Web.

text file

Summary:

Collaborative functionality is changing the way information is amassed, refined, and disseminated in online environments. A subclass of these systems characterized by "open collaboration" uniquely allow participants to modify content with low barriers-to-entry. A prominent example and our case study, English Wikipedia, exemplifies the vulnerabilities: 7%+ of its edits are blatantly unconstructive. Our measurement studies show this damage manifests in novel socio-technical forms, limiting the effectiveness of computational detection strategies from related domains. In turn this has made much mitigation the responsibility of a poorly organized and ill-routed human workforce. We aim to improve all facets of this incident response workflow.

Complementing language based solutions we first develop content agnostic predictors of damage. We implicitly glean reputations for system entities and overcome sparse behavioral histories with a spatial reputation model combining evidence from multiple granularity. We also identify simple yet indicative metadata features that capture participatory dynamics and content maturation. When brought to bear over damage corpora our contributions: (1) advance benchmarks over a broad set of security issues ("vandalism"), (2) perform well in the first anti-spam specific approach, and (3) demonstrate their portability over diverse open collaboration use cases.

Probabilities generated by our classifiers can also intelligently route human assets using prioritization schemes optimized for capture rate or impact minimization. Organizational primitives are introduced that improve workforce efficiency. The whole of these strategies are then implemented into a tool ("STiki") that has been used to revert 350,000+ damaging instances from Wikipedia. These uses are analyzed to learn about human aspects of the edit review process, properties including scalability, motivation, and latency. Finally, we conclude by measuring practical impacts of work, discussing how to better integrate our solutions, and revealing outstanding vulnerabilities that speak to research challenges for open collaboration security.

Notes:

Thesis (Ph.D. in Computer and Information Science) -- University of Pennsylvania, 2013.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Advisers: Insup Lee; Oleg Sokolsky.

Local Notes:

School code: 0175.

ISBN:

9781303396953

Access Restriction:

Restricted for use by site license.