Node labels as random variables prior belief observed neighbor potentials compatibility potentials Opinion Fraud Detection in Online Reviews using Network.

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node labels as random variables prior belief observed neighbor potentials compatibility potentials Opinion Fraud Detection in Online Reviews using Network Effects Leman Akoglu Stony Brook University Christos Faloutsos Carnegie Mellon University Rishi Chandy Carnegie Mellon University Which reviews do/should you trust? Problem Statement A network classification problem: Given Classify network objects into type-specific classes: the user-product review network (bipartite)review sentiments (+: thumbs-up, -: thumbs-down) users: `honest’ / `fraudster’ products: `good’ / `bad’ reviews: `genuine’ / `fake’ Property 1: Network effects Fraudulence of reviews/reviewers is revealed in relation to others. So review network should be used. A Fake-Review(er) Detection System Desired properties that such a system to have: Property 2: Side information Information on behavioral (e.g. login times) and linguistic (e.g. use of capital letters) clues should be exploited. Property 3: Un/Semi-supervision Methods should not expect fully labeled training set. (humans are at best close to random) 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 Formulation: A Collective Classification Approach Objective function utilizes pairwise Markov Random Fields (Kindermann&Snell, 1980): edge signs 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). Iterative process in which neighbor variables “talk” to each other, passing messages When consensus reached, calculate belief signed Inference Algorithm (sIA): Inference “I (variable x1) believe you (variable x2) belong in these states with various likelihoods…” I) Repeat for each node: II) At convergence: i Scoring:Scoring: BeforeAfter Compatibility: Datasets I)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) 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) Competitors Real-data Results Performance on simulated data: (from left to right) sIA, wv-RC, HITS Top 100 users and their product votes: + (4-5) rating o (1-2) rating “bot” members? Top-scorers matter: 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.