5 research outputs found
Detecting Sockpuppets in Deceptive Opinion Spam
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.Comment: 18 pages, Accepted at CICLing 2017, 18th International Conference on
Intelligent Text Processing and Computational Linguistic
A Deep Context Grammatical Model For Authorship Attribution
We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, delexicalized
parse of source texts generated by a standard lexicalized parser, which we apply to the authorship attribution task. First, we
motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and
limitations of the overall approach. Next we describe the procedure for building syntactic models for each author based on training
cases. We then outline the attribution process – assigning authorship to the model which yields the highest probability for the given
test case. We demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features
trained by a linear kernel SVM. We find that the model performs somewhat less successfully than the SVM over similar features. In the
conclusion, we outline how we plan to employ the model for syntactic evaluation of literary texts
Adapting Language Models for Non-Parallel Author-Stylized Rewriting
Given the recent progress in language modeling using Transformer-based neural
models and an active interest in generating stylized text, we present an
approach to leverage the generalization capabilities of a language model to
rewrite an input text in a target author's style. Our proposed approach adapts
a pre-trained language model to generate author-stylized text by fine-tuning on
the author-specific corpus using a denoising autoencoder (DAE) loss in a
cascaded encoder-decoder framework. Optimizing over DAE loss allows our model
to learn the nuances of an author's style without relying on parallel data,
which has been a severe limitation of the previous related works in this space.
To evaluate the efficacy of our approach, we propose a linguistically-motivated
framework to quantify stylistic alignment of the generated text to the target
author at lexical, syntactic and surface levels. The evaluation framework is
both interpretable as it leads to several insights about the model, and
self-contained as it does not rely on external classifiers, e.g. sentiment or
formality classifiers. Qualitative and quantitative assessment indicates that
the proposed approach rewrites the input text with better alignment to the
target style while preserving the original content better than state-of-the-art
baselines.Comment: Accepted for publication in Main Technical Track at AAAI 2