3 G. Wu M. Harrigan P. Cunningham 2011 A Characterization of Regions of Wikipedia Based on Motifs in the Edit Graph The 22nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS'11) 166–173 Wikipedia works because of the many eyes idea. Good Wikipedia pages are authoritative sources due to the collaboration of a number of knowledge contributors. In this paper we explore the hypothesis that the extent of this collaboration for a specific article can be assessed by looking at the edit graph associated with that article, i.e. the network of contributors and articles. As a first step in this direction we show that different regions of Wikipedia have very different edit graph motif profiles. We show, for example, that articles on sociologists (a well-curated region of Wikipedia) are very different to articles on footballers in the English Premiership. 3 G. Wu M. Harrigan P. Cunningham 2011 Characterizing Wikipedia Pages Using Edit Network Motif Profiles The 3rd International Workshop on Search and Mining User-Generated Contents (SMUC'11) 3 G. Wu D. Greene B. Smyth* P. Cunningham 2010 Distortion as a Validation Criterion in the Identification of Suspicious Reviews 1st Workshop on Social Media Analytics (SOMA'10) shilling, reviews, anomalous <p>Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor. In this paper we propose a distortion criterion for assessing the im- pact of methods for uncovering suspicious hotel reviews in TripAdvisor. The principle is that dishonest reviews will distort the overall popularity ranking for a collection of hotels. Thus a mechanism that deletes dishonest reviews will distort the popularity ranking significantly, when compared with the removal of a similar set of reviews at random. This distortion can be quantified by comparing popularity rankings before and after deletion, using rank correlation. We present an evaluation of this strategy in the assessment of shill detection mechanisms on a dataset of hotel reviews col- lected from TripAdvisor.</p>

* Non-Clique Members

 http://snap.stanford.edu/soma2010/papers/soma2010_2.pdf 3 G. Wu D. Greene P. Cunningham 2010 Merging Multiple Criteria to Identify Suspicious Reviews Proc. 4th ACM Conference on Recommender Systems (RecSys'10) shilling, reviews, anomalous <p>Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor, given the rewards that can be derived from posting false or biased reviews. In this paper we present a number of criteria that might be indicative of suspicious reviews and evaluate alternative methods for integrating these criteria to produce a unified &quot;suspiciousness&quot; ranking. The criteria derive from characteristics of the network of reviewers and also from analysis of the content and impact of reviews and ratings. The integration methods that are evaluated are singular value decomposition and the unsupervised hedge algorithm. These alternatives are evaluated in a user study on TripAdvisor reviews, where volunteers were asked to rate the suspiciousness of reviews that have been highlighted by the criteria.</p> http://irserver.ucd.ie/dspace/handle/10197/2403 31 P. Cunningham B. Smyth G. Wu D. Greene 2010 Does TripAdvisor Make Hotels Better? UCD CSI Technical Report UCD-CSI-2010-04 <p>Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor. In this paper we propose a distortion criterion for assessing the impact of methods for uncovering suspicious hotel reviews in TripAdvisor. The principle is that dishonest reviews will distort the overall popularity ranking for a collection of hotels. Thus a mechanism that deletes dishonest reviews will distort the popularity ranking significantly, when compared with the removal of a similar set of reviews at random. This distortion can be quantiï¬ed by comparing popularity rankings before and after deletion, using rank correlation. We present an evaluation of this strategy in the assessment of shill detection mechanisms on a dataset of hotel reviews collected from TripAdvisor.</p> http://www.csi.ucd.ie/files/ucd-csi-2010-04.pdf