Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify such fraudulent users. We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product. Intuitively, a user is fair if it provides reliable ratings that are close to the goodness of the product. We formulate a mutually recursive definition of these metrics, and further address cold start problems and incorporate behavioral properties of users and products in the formulation. We propose an iterative algorithm, FairJudge, to predict the values of the three metrics. We prove that FairJudge is guaranteed to converge in a bounded number of iterations, with linear time complexity. By conducting five different experiments on five rating platforms, we show that FairJudge significantly outperforms nine existing algorithms in predicting fair and unfair users. We reported the 100 most unfair users in the Flipkart network to their review fraud investigators, and 80 users were correctly identified (80 accuracy). The FairJudge algorithm is already being deployed at Flipkart.
algorithms are often feature-based. Consensus based features have been proposed in @cite_29 @cite_15 -- our proposed goodness metric is also inspired by consensus or wisdom of crowds'. Commonly used features are derived from timestamps @cite_1 @cite_6 @cite_25 and review text @cite_36 @cite_8 @cite_13 . SpEagle @cite_34 extends FraudEagle @cite_10 to incorporate behavior features. BIRDNEST @cite_23 creates a Bayesian model to estimate the belief of each user's deviation in rating behavior from global expected behavior. @cite_12 @cite_26 @cite_27 study coordinated spam behavior of multiple users. A on behavior based algorithms can be found in @cite_9 .
@cite_26 @cite_8 @cite_36 @cite_10 @cite_29 @cite_9 @cite_1 @cite_6 @cite_27 @cite_23 @cite_15 @cite_34 @cite_13 @cite_25 @cite_12
2952852844 1996359281 2964316623 2119830539
The probability that a user will click a search result depends both on its relevance and its position on the results page. The position based model explains this behavior by ascribing to every item an attraction probability, and to every position an examination probability. To be clicked, a result must be both attractive and examined. The probabilities of an item-position pair being clicked thus form the entries of a rank- @math matrix. We propose the learning problem of a Bernoulli rank- @math bandit where at each step, the learning agent chooses a pair of row and column arms, and receives the product of their Bernoulli-distributed values as a reward. This is a special case of the stochastic rank- @math bandit problem considered in recent work that proposed an elimination based algorithm Rank1Elim, and showed that Rank1Elim's regret scales linearly with the number of rows and columns on "benign" instances. These are the instances where the minimum of the average row and column rewards @math is bounded away from zero. The issue with Rank1Elim is that it fails to be competitive with straightforward bandit strategies as @math . In this paper we propose Rank1ElimKL which simply replaces the (crude) confidence intervals of Rank1Elim with confidence intervals based on Kullback-Leibler (KL) divergences, and with the help of a novel result concerning the scaling of KL divergences we prove that with this change, our algorithm will be competitive no matter the value of @math . Experiments with synthetic data confirm that on benign instances the performance of Rank1ElimKL is significantly better than that of even Rank1Elim, while experiments with models derived from real data confirm that the improvements are significant across the board, regardless of whether the data is benign or not. Human behaviour is highly individual by nature, yet statistical structures are emerging which seem to govern the actions of human beings collectively. Here we search for universal statistical laws dictating the timing of human actions in communication decisions. We focus on the distribution of the time interval between messages in human broadcast communication, as documented in Twitter, and study a collection of over 160,000 tweets for three user categories: personal (controlled by one person), managed (typically PR agency controlled) and bot-controlled (automated system). To test our hypothesis, we investigate whether it is possible to differentiate between user types based on tweet timing behaviour, independently of the content in messages. For this purpose, we developed a system to process a large amount of tweets for reality mining and implemented two simple probabilistic inference algorithms: 1. a naive Bayes classifier, which distinguishes between two and three account categories with classification performance of 84.6 and 75.8 , respectively and 2. a prediction algorithm to estimate the time of a user's next tweet with an . Our results show that we can reliably distinguish between the three user categories as well as predict the distribution of a user's inter-message time with reasonable accuracy. More importantly, we identify a characteristic power-law decrease in the tail of inter-message time distribution by human users which is different from that obtained for managed and automated accounts. This result is evidence of a universal law that permeates the timing of human decisions in broadcast communication and extends the findings of several previous studies of peer-to-peer communication. Algorithms are increasingly used to make important decisions about human lives, ranging from social benefit assignment to predicting risk of criminal recidivism. The sensitivity of these decisions has raised important questions about ensuring fairness in algorithmic decision making. Recent work has focused on addressing two aspects of fairness: distributional fairness, concerning the fairness of the outcomes of decision making, and procedural fairness, concerning the fairness of the decision-making process. Assessment of procedural fairness relies on human judgments, for example, about the fairness of using certain features in the algorithm making the decision. Yet to our knowledge, almost no prior work examines people’s perceptions of fairness in algorithmic decision making. We propose a framework for understanding why people perceive certain features as fair to be used in algorithms. We define a framework of eight underlying properties of features, such as relevance, volitionality and reliability, and hypothesize that people use these properties to form a heuristic for making moral judgments about the fairness of feature use in decision-making algorithms. We evaluate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person’s assessment of the eight underlying properties in our exemplar scenario, we can predict if they will perceive a feature as fair to be used with at least 87 accuracy. With the advent of crowdsourcing services it has become quite cheap and reasonably effective to get a data set labeled by multiple annotators in a short amount of time. Various methods have been proposed to estimate the consensus labels by correcting for the bias of annotators with different kinds of expertise. Since we do not have control over the quality of the annotators, very often the annotations can be dominated by spammers, defined as annotators who assign labels randomly without actually looking at the instance. Spammers can make the cost of acquiring labels very expensive and can potentially degrade the quality of the final consensus labels. In this paper we propose an empirical Bayesian algorithm called SpEMthat iteratively eliminates the spammers and estimates the consensus labels based only on the good annotators. The algorithm is motivated by defining a spammer score that can be used to rank the annotators. Experiments on simulated and real data show that the proposed approach is better than (or as good as) the earlier approaches in terms of the accuracy and uses a significantly smaller number of annotators.