Biblio

Plagiarism is the act of using someone else’s words or ideas without giving them due credit and representing it as one’s own work. In today's world, it is very easy to plagiarize others' work due to advancement in technology, especially by the use of the Internet or other offline sources such as books or magazines. Plagiarism can be classified into two broad categories on the basis of detection namely extrinsic and intrinsic plagiarism. Extrinsic plagiarism detection refers to detecting plagiarism in a document by comparing it against a given reference dataset, whereas, Intrinsic plagiarism detection refers to detecting plagiarism with the help of variation in writing styles without using any reference corpus. Although there are many approaches which can be adopted to detect extrinsic plagiarism, few are available for intrinsic plagiarism detection. In this paper, a simplified approach is proposed for developing an intrinsic plagiarism detector which is helpful in detecting plagiarism even when no reference corpus is available. The approach deals with development of an intrinsic plagiarism detection system by identifying the writing style of authors in the document using stylometric features and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The proposed system has an easy to use interactive interface where user has to upload a text document to be checked for plagiarism and the result is displayed on the web page itself. In addition, the user can also see the analysis of the document in the form of graphs.

Writing style is a combination of consistent decisions associated with a specific author at different levels of language production, including lexical, syntactic, and structural. In this paper, we introduce a style-aware neural model to encode document information from three stylistic levels and evaluate it in the domain of authorship attribution. First, we propose a simple way to jointly encode syntactic and lexical representations of sentences. Subsequently, we employ an attention-based hierarchical neural network to encode the syntactic and semantic structure of sentences in documents while rewarding the sentences which contribute more to capturing the writing style. Our experimental results, based on four benchmark datasets, reveal the benefits of encoding document information from all three stylistic levels when compared to the baseline methods in the literature.

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 users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author's identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.