The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi
hotspots, and distributed networks like Tor has drastically complicated the task of identifying …

We present a model to perform authorship attribution of tweets using Convolutional Neural
Networks (CNNs) over character n-grams. We also present a strategy that improves model …

Convolutional neural networks (CNNs) have demonstrated superior capability for extracting
information from raw signals in computer vision. Recently, character-level and multi-channel …

Authorship attribution is associated with important applications in forensics and humanities
research. A crucial point in this field is to quantify the personal style of writing, ideally in a …

Approaches to authorship attribution, the task of identifying the author of a document, are
based on analysis of individuals' writing style and/or preferred topics. Although the problem …

Authorship verification is the task of analyzing the linguistic patterns of two or more texts to
determine whether they were written by the same author or not. The analysis is traditionally …

A writer's style depends not just on personal traits but also on her intent and mental state. In
this paper, we show how variants of the same writing task can lead to measurable …

We present a novel word level vector representation based on symmetric patterns (SPs). For
this aim we automatically acquire SPs (eg,“X and Y”) from a large corpus of plain text, and …

The world is facing a new era in which social media communication plays a fundamental
role in people's lives. Along with proven benefits, several collateral drawbacks have risen …

This paper presents work on using continuous representations for authorship attribution. In
contrast to previous work, which uses discrete feature representations, our model learns …