Detecting Sockpuppets in Deceptive Opinion Spam

03/09/2017
by   Marjan Hosseinia, et al.
0

This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches. Two methods are explored. The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features. The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from the training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors. Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/09/2016

Minimax Lower Bounds for Realizable Transductive Classification

Transductive learning considers a training set of m labeled samples and ...
research
08/25/2018

Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set

Recently, string kernels have obtained state-of-the-art results in vario...
research
02/24/2016

Domain Specific Author Attribution Based on Feedforward Neural Network Language Models

Authorship attribution refers to the task of automatically determining t...
research
04/13/2022

Fast Few-shot Debugging for NLU Test Suites

We study few-shot debugging of transformer based natural language unders...
research
07/06/2020

Are Labels Necessary for Classifier Accuracy Evaluation?

To calculate the model accuracy on a computer vision task, e.g., object ...
research
03/28/2022

Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)

The unlabeled data are generally assumed to be normal data in detecting ...
research
12/03/2013

Test Set Selection using Active Information Acquisition for Predictive Models

In this paper, we consider active information acquisition when the predi...

Please sign up or login with your details

Forgot password? Click here to reset