node labels as random variables

1. Opinion Fraud Detection in Online Reviews using Network Effects • users: `honest’ / `fraudster’ • reviews: `genuine’ / `fake’ • products: `good’ / `bad’ • review sentiments (+: thumbs-up, -: thumbs-down) • the user-product review network (bipartite) i Leman Akoglu Stony Brook University leman@cs.stonybrook.edu Rishi Chandy Carnegie Mellon University rishic@cs.cmu.edu Christos Faloutsos Carnegie Mellon University christos@cs.cmu.edu Approach Problem Results and Conclusions Problem Formulation: A Collective Classification Approach Datasets Objective function utilizes pairwise Markov Random Fields (Kindermann&Snell, 1980): • SWM: All app reviews of entertainment category (games, news, sports, etc.) from an anonymous online app store database • As of June 2012: • * 1, 132, 373 reviews • * 966, 842 users • * 15,094 software products (apps) • Ratings: 1 (worst) to 5 (best) • II) Also simulated fake review data (with ground truth) edge signs node labels as random variables compatibility potentials observed neighbor potentials prior belief Competitors Compared to 2 iterative classifiers (modified to handle signed edges): I) Weighted-vote Relational Classifier (wv-RC) (Macskassy&Provost, 2003) II) HITS (honesty-goodness in mutual recursion) (Kleinberg, 1999) Which reviews do/should you trust? Inference A Fake-Review(er) Detection System • Finding best assignments is the inference problem, NP-hard for general graphs. • We use a computationally tractable (linearly scalable with network size) approximate inference algorithm called Loopy Belief Propagation (LBP) (Pearl, 1982). • Iterativeprocess in which neighbor variables • “talk” to each other, passing messages • When consensus reached, calculate belief • signed Inference Algorithm (sIA): Desired properties that such a system to have: Performance on simulated data: (from left to right) sIA, wv-RC, HITS Property 2: Side information Information on behavioral (e.g. login times) and linguistic (e.g. use of capital letters) clues should be exploited. Property 1: Network effects Fraudulence of reviews/reviewers is revealed in relation to others. So review network should be used. Real-data Results “I (variable x1) believe you (variable x2) belong in these states with various likelihoods…” Top 100 users and their product votes: Property 3: Un/Semi-supervision Methods should not expect fully labeled training set. (humans are at best close to random) “bot” members? I) Repeat for each node: Property 4: Scalability Methods should be (sub)linear in data/network size. Property 5: Incremental Methods should compute fraudulence scores incrementally with the arrival of data (hourly/daily). Problem Statement + (4-5) rating o (1-2) rating A network classification problem: Given Classify network objects into type-specific classes: Top-scorers matter: II) At convergence: Compatibility: Conclusions • Novel framework that exploits network effects to automatically spot fake review(er)s. • Problem formulation as collective classification in bipartite networks • Efficient scoring/inference algorithm to handle signed edges • Desirable properties: i) general, ii) un/semi-supervised, iii) scalable • Experiments on real&synthetic data: better than competitors, finds real fraudsters. Scoring: Before After