[Data Science Meetup] Rinat Gareev: Scammer Detection on Crowdsourcing Platforms

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Microtask crowdsourcing is a trade-off between budget, speed of getting results and their quality. It is a popular approach to quickly build a training dataset for machine learning. There are standard quality assurance mechanics on crowdsourcing platforms but they still might leave an opportunity for scamming. In this presentation I will talk about our experience in improving scammer detection, how to estimate fairness and proficiency of contributors using unsupervised machine learning models

Data & Analytics

Scammer Detection
on Crowdsourcing Platforms
Rinat Gareev,

Agenda
● Online Microtask Crowdsourcing
● Scamming
● Algorithms
● Evaluation and results
● Ongoing sub-Projects

Online Microtask Crowdsourcing - 1
Customer
Budget
Data

Online Microtask Crowdsourcing - 2
Platforms (SaaS)

Online Microtask Crowdsourcing - 3 Workers
Judgments

Notes
Focus on small tasks (each unit takes 1-10 minutes)

Job Examples
● Categorization
● Matching (images, products, etc.)
● Ratings
● Find & Correct
● Image / Audio / Video annotation
● Transcription

Types of Workers
Good – read & understand instructions, provide correct answers
Bad – poor understanding, bad answers
Adversarial – misunderstand instructions, provide reversed answers
Scammers – provide random or constant answers

Quality control
● Agreement-based methods
● Embedded golden standard (quiz and hidden test questions)
● T&S team

Scammers
Intention – earn money providing ‘random’ answers.
First: Learn answers for test questions using fake accounts.

Challenges
● scale (thousands of jobs, millions of workers)
● different job structures (multi-question tasks, several answer types)
● worker quality can dramatically change from a job to another one
● evaluation (no ground truth, only partial labelling is feasible)

Problem Definition
“Reactive Scammer Detection”
Given:
worker answers for a single job
Output:
worker qualities

Modelling
● Worker
● Unit of work
● True answers

Worker models
● Constant (all workers are same)
● Single scalar (quality or proficiency)
● Bias and variance
● Vector (latent skills/topics)
● Confusion matrix → error rate

Unit models
● Constant (all units are ‘equal’)
● Single scalar (difficulty)
● Vector (topics)

True answer models
● one answer per question
● probability distribution per question

Majority Voting
WORKER
● Constant
● Scalar
● Bias and variance
● Vector
● Confusion matrix
TRUE ANSWER
● Single
● Distribution
UNIT
● Constant
● Scalar
● Vector

Weighted Majority Voting
WORKER
● Constant
● Scalar
● Bias and variance
● Vector
● Confusion matrix
UNIT
● Constant
● Scalar
● Vector
TRUE ANSWER
● Single
● Distribution

Dawid-Skene Model
WORKER
● Constant
● Scalar
● Bias and variance
● Vector
● Confusion matrix
UNIT
● Constant
● Scalar
● Vector
TRUE ANSWER
● Single
● Distribution

GLAD* Model
WORKER
● Constant
● Scalar
● Bias and variance
● Vector
● Confusion matrix
UNIT
● Constant
● Scalar
● Vector
TRUE ANSWER
● Single
● Distribution
*Generative model of Labels, Abilities, and Difficulties

GLAD model
Expectation-Maximization
E-Step
M-Step

GLAD - Implementation
Challenges:
● rewrite all derivations in matrix form
● very sparse data
○ 4 dimensions involved:
■ (1) units,
■ (2) workers,
■ (3) questions,
■ (4) answers;
○ In-box sparse tensors were not used, instead:
tf.tile, tf.gather, tf.where, tf.segment_sum, etc...
Benefits:
● Comp. Graph
● CG and Session
decoupling
● In-box
Parallelization

Evaluation - Challenges
● before evaluation - we only know about positives detected before (using older
tools)
● after evaluation - we need to check false positives with most suspicious
scores
● there is a mess among banned accounts
● there are quite a few boundary cases

Evaluation - Results (AUC precision - recall)

12500 suspicious accounts were detected (on 1300 jobs with at least 1 susp.)
● 4200 were banned
Precision for 2 months

Full Webinar: https://info.tigergraph.com/graph-gurus-32 By watching this webinar you will: -See how similarity algorithms are used to calculate "distances" between entities. -Learn what data scientists mean when they say Labels and Training. -Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to learning the best value of k.

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