Authorship Attribution Through Words Surrounding Named Entities

In text analysis, authorship attribution occurs in a variety of ways. The field of computational linguistics becomes more important as the need of authorship attribution and text analysis becomes more widespread. For this research, pre-existing authorship attribution software, Java Graphical Authorship Attribution Program (JGAAP), implements a named entity recognizer, specifically the Stanford Named Entity Recognizer, to probe into similar genre text and to aid in extricating the correct author. This research specifically examines the words authors use around named entities in order to test the ability of these words at attributing authorshi

English Bards and Unknown Reviewers: a Stylometric Analysis of Thomas Moore and the Christabel Review

Fraught relations between authors and critics are a commonplace of literary history. The particular case that we discuss in this article, a negative review of Samuel Taylor Coleridge's Christabel (1816), has an additional point of interest beyond the usual mixture of amusement and resentment that surrounds a critical rebuke: the authorship of the review remains, to this day, uncertain. The purpose of this article is to investigate the possible candidacy of Thomas Moore as the author of the provocative review. It seeks to solve a puzzle of almost two hundred years, and in the process clear a valuable scholarly path in Irish Studies, Romanticism, and in our understanding of Moore's role in a prominent literary controversy of the age

Identifying users on social networking using pattern recognition in messages

Online social networks, such as Facebook and Twitter, have become a huge part of many people\u27s lives, often as their main means of communication with other people. Because of frequency of use and the apparent security measures of these sites, users often falsely believe the proffered identity of the person they are talking to. This blind belief sometimes results in security threats due to the passing of private or confidential information to the wrong user. This may lead to malicious readers getting a user\u27s private information and using it illegally. This work proposes a mathematical model for identifying security threats using pattern recognition with the aid of an extension of the Naive Bayes method called the Friendship Naive Bayes. Since specific patterns could be observed by examining the communication history between users, the proposed scheme uses these patterns to authenticate that the new message was written by the same person from the history. The scheme then calculates the probability of identifying the person as either the correct or incorrect user

Deception in Authorship Attribution

In digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. While stylometry techniques can identify authors with high accuracy in non-adversarial scenarios, their accuracy is reduced to random guessing when faced with authors who intentionally obfuscate their writing style or attempt to imitate that of another author. Most authorship attribution methods were not evaluated in challenging real-world datasets with foreign language and unconventional spelling (e.g. l33tsp3ak). In this thesis we explore the performance of authorship attribution methods in adversarial settings where authors take measures to hide their identity by changing their writing style and by creating multiple identities. We show that using a large feature set, it is possible to distinguish regular documents from deceptive documents with high accuracy and present an analysis of linguistic features that can be modified to hide writing style. We show how to adapt regular authorship attribution to difficult datasets such as leaked underground forum and present a method for detecting multiple identities of authors. We demonstrate the utility of our approach with a case study that includes applying our technique to an underground forum and manual analysis to validate the results, enabling the discovery of previously undetected multiple accounts.Ph.D., Computer Science -- Drexel University, 201

Authorship Verification

In recent years, stylometry, the study of linguistic style, has become more prominent in security and privacy applications involving written language, mostly in digital and online domains. Although literature is abundant with computational stylometry research, the field of authorship verification is relatively unexplored. Authorship verification is the binary semi-open-world problem of determining whether a document is written by a given author or not. A key component in authorship verification techniques is confidence measurement, on which verification decisions are based, expressed by acceptance thresholds selected and tuned per need. This thesis demonstrates how utilization of confidence-based approaches in stylometric applications, and their combination with traditional approaches, can benefit classification accuracy, and allow new domains and problems to be analyzed. We start by motivating the usage of authorship verification approaches with two stylometric applications: native-language identification from non-native text and active linguistic user authentication. Next, we introduce the Classify-Verify algorithm, which integrates classification with binary verification, applied to several stylometric problems. Classify-Verify is proposed as an open-world alternative to restricted closed-world attribution methods, and is shown effective in dealing with possibly missing candidate authors by thwarting misclassifications, coping with various domains and scales, and even adversarial authors who try to fool the classifier.Ph.D., Computer Science -- Drexel University, 201

Authorship Identification and Writeprint Visualization

The Internet provides an ideal anonymous channel for concealing computer-mediated malicious activities, as the network-based origins of critical electronic textual evidence (e.g., emails, blogs, forum posts, chat log etc.) can be easily repudiated. Authorship attribution is the study of identifying the actual author of the given anonymous documents based on the text itself, and, for decades, many linguistic stylometry and computational techniques have been extensively studied for this purpose. However, most of the previous research emphasizes promoting the authorship attribution accuracy and few works have been done for the purpose of constructing and visualizing the evidential traits; also, these sophisticated techniques are difficult for cyber investigators or linguistic experts to interpret. In this thesis, based on the EEDI (End-to-End Digital Investigation) Framework we propose a visualizable evidence-driven approach, namely VEA, which aims at facilitating the work of cyber investigation. Our comprehensive controlled experiment and stratified experiment on the real-life Enron email data set both demonstrate that our approach can achieve even higher accuracy than traditional methods; meanwhile, its output can be easily visualized and interpreted as evidential traits. In addition to identifying the most plausible author of a given text, our approach also estimates the confidence for the predicted result based on a given identification context and presents visualizable linguistic evidence for each candidate

Detecting deceptive behaviour in the wild:text mining for online child protection in the presence of noisy and adversarial social media communications

A real-life application of text mining research “in the wild”, i.e. in online social media, differs from more general applications in that its defining characteristics are both domain and process dependent. This gives rise to a number of challenges of which contemporary research has only scratched the surface. More specifically, a text mining approach applied in the wild typically has no control over the dataset size. Hence, the system has to be robust towards limited data availability, a variable number of samples across users and a highly skewed dataset. Additionally, the quality of the data cannot be guaranteed. As a result, the approach needs to be tolerant to a certain degree of linguistic noise. Finally, it has to be robust towards deceptive behaviour or adversaries. This thesis examines the viability of a text mining approach for supporting cybercrime investigations pertaining to online child protection. The main contributions of this dissertation are as follows. A systematic study of different aspects of methodological design of a state-ofthe- art text mining approach is presented to assess its scalability towards a large, imbalanced and linguistically noisy social media dataset. In this framework, three key automatic text categorisation tasks are examined, namely the feasibility to (i) identify a social network user’s age group and gender based on textual information found in only one single message; (ii) aggregate predictions on the message level to the user level without neglecting potential clues of deception and detect false user profiles on social networks and (iii) identify child sexual abuse media among thousands of legal other media, including adult pornography, based on their filename. Finally, a novel approach is presented that combines age group predictions with advanced text clustering techniques and unsupervised learning to identify online child sex offenders’ grooming behaviour. The methodology presented in this thesis was extensively discussed with law enforcement to assess its forensic readiness. Additionally, each component was evaluated on actual child sex offender data. Despite the challenging characteristics of these text types, the results show high degrees of accuracy for false profile detection, identifying grooming behaviour and child sexual abuse media identification