The detection and improvement of low-quality information is a key concern in Web applications that are based on user-generated content; a popular example is the online encyclopedia Wikipedia. Existing research on quality assessment of user-generated content deals with the classification as to whether the content is high-quality or low-quality. This paper goes one step further: it targets the prediction of quality flaws, this way providing specific indications in which respects low-quality content needs improvement. The prediction is based on user-defined cleanup tags, which are commonly used in many Web applications to tag content that has some shortcomings. We apply this approach to the English Wikipedia, which is the largest and most popular user-generated knowledge source on the Web. We present an automatic mining approach to identify the existing cleanup tags, which provides us with a training corpus of labeled Wikipedia articles. We argue that common binary or multiclass classification approaches are ineffective for the prediction of quality flaws and hence cast quality flaw prediction as a one-class classification problem. We develop a quality flaw model and employ a dedicated machine learning approach to predict Wikipedia's most important quality flaws. Since in the Wikipedia setting the acquisition of significant test data is intricate, we analyze the effects of a biased sample selection. In this regard we illustrate the classifier effectiveness as a function of the flaw distribution in order to cope with the unknown (real-world) flawspecific class imbalances. The flaw prediction performance is evaluated with 10 000 Wikipedia articles that have been tagged with the ten most frequent quality flaws: provided test data with little noise, four flaws can be detected with a precision close to 1.

In visual communication, text emphasis is used to increase the comprehension of written text and to convey the author's intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author's intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.

Graph Neural Networks (GNNs) have proven to be useful for many different practical applications. However, many existing GNN models have implicitly assumed homophily among the nodes connected in the graph, and therefore have largely overlooked the important setting of heterophily, where most connected nodes are from different classes. In this work, we propose a novel framework called CPGNN that generalizes GNNs for graphs with either homophily or heterophily. The proposed framework incorporates an interpretable compatibility matrix for modeling the heterophily or homophily level in the graph, which can be learned in an end-to-end fashion, enabling it to go beyond the assumption of strong homophily. Theoretically, we show that replacing the compatibility matrix in our framework with the identity (which represents pure homophily) reduces to GCN. Our extensive experiments demonstrate the effectiveness of our approach in more realistic and challenging experimental settings with significantly less training data compared to previous works: CPGNN variants achieve state-of-the-art results in heterophily settings with or without contextual node features, while maintaining comparable performance in homophily settings.

Research in automatic text plagiarism detection focuses on algorithms that compare suspicious documents against a collection of reference documents. Recent approaches perform well in identifying copied or modified foreign sections, but they assume a closed world where a reference collection is given. This article investigates the question whether plagiarism can be detected by a computer program if no reference can be provided, e.g., if the foreign sections stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism analysis; it is closely related to the problem of authorship verification. Our contributions are threefold. (1) We organize the algorithmic building blocks for intrinsic plagiarism analysis and authorship verification and survey the state of the art.(2) We show how the meta learning approach of Koppel and Schler, termed ''unmasking'', can be employed to post-process unreliable stylometric analysis results. (3) We operationalize and evaluate an analysis chain that combines document chunking, style model computation, one-class classification, and meta learning. Problem statementIn the following, the term plagiarism refers to text plagiarism, i.e., the use of another author's information, language, or writing, when done without proper acknowledgment of the original source. Plagiarism detection refers to the unveiling of text plagiarism. Existing approaches to computer-based plagiarism detection break down this task into manageable parts:''Given a text d and a reference collection D, does d contain a section s for which one can find a document d i [ D that contains a section s i such that under some retrieval model R the similarity u R between s and s i is above a threshold h?''Observe that research on automated plagiarism detection presumes a closed world where a reference collection D is given. Since D can be extremely largepossibly the entire indexed part of the World Wide Web-the main research focus is on efficient search technology: near-similarity search and near-duplicate detection (Brin et al

Wikipedia provides an information quality assessment model with criteria for human peer reviewers to identify featured articles. For this classification task "Is an article featured or not?" we present a machine learning approach that exploits an article's character trigram distribution. Our approach differs from existing research in that it aims to writing style rather than evaluating meta features like the edit history. The approach is robust, straightforward to implement, and outperforms existing solutions. We underpin these claims by an experiment design where, among others, the domain transferability is analyzed. The achieved performances in terms of the F -measure for featured articles are 0.964 within a single Wikipedia domain and 0.880 in a domain transfer situation.