In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. Our system relies on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic in the sense that they promote a particular political cause or viewpoint. We trained a logistic regression model with features ranging from simple bag-of-words to vocabulary richness and text readability features. Our system achieved 72.9 accuracy on the test data that is annotated manually and 60.8 on the test data that is annotated with distant supervision. Additional experiments showed that significant performance improvements can be achieved with better feature pre-processing.
Similarly to , we believe that there is an inherent style in propaganda, regardless of the source publishing it. Many stylistic features were proposed for authorship identification, i.e., the task of predicting whether a piece of text has been written by a particular author. One of the most successful representations for such a task are character-level @math -grams @cite_21 , and they turn out to represent some of our most important stylistic features.
@cite_21
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Authorship analysis by means of textual features is an important task in linguistic studies. We employ complex networks theory to tackle this disputed problem. In this work, we focus on some measurable quantities of word co-occurrence network of each book for authorship characterization. Based on the network features, attribution probability is defined for authorship identification. Furthermore, two scaling exponents, q-parameter and α-exponent, are combined to classify personal writing style with acceptable high resolution power. The q-parameter, generally known as the nonextensivity measure, is calculated for degree distribution and the α-exponent comes from a power law relationship between number of links and number of nodes in the co-occurrence network constructed for different books written by each author. The applicability of the presented method is evaluated in an experiment with thirty six books of five Persian litterateurs. Our results show high accuracy rate 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. While these results are good for privacy, they raise concerns about fraud. We argue that some linguistic features change when people hide their writing style and by identifying those features, stylistic deception can be recognized. The major contribution of this work is a method for detecting stylistic deception in written documents. We show that using a large feature set, it is possible to distinguish regular documents from deceptive documents with 96.6 accuracy (F-measure). We also present an analysis of linguistic features that can be modified to hide writing style. A critical aspect of malware forensics is authorship analysis. The successful outcome of such analysis is usually determined by the reverse engineer’s skills and by the volume and complexity of the code under analysis. To assist reverse engineers in such a tedious and error-prone task, it is desirable to develop reliable and automated tools for supporting the practice of malware authorship attribution. In a recent work, machine learning was used to rank and select syntax-based features such as n-grams and flow graphs. The experimental results showed that the top ranked features were unique for each author, which was regarded as an evidence that those features capture the author’s programming styles. In this paper, however, we show that the uniqueness of features does not necessarily correspond to authorship. Specifically, our analysis demonstrates that many “unique” features selected using this method are clearly unrelated to the authors’ programming styles, for example, unique IDs or random but unique function names generated by the compiler; furthermore, the overall accuracy is generally unsatisfactory. Motivated by this discovery, we propose a layered Onion Approach for Binary Authorship Attribution called OBA2. The novelty of our approach lies in the three complementary layers: preprocessing, syntax-based attribution, and semantic-based attribution. Experiments show that our method produces results that not only are more accurate but have a meaningful connection to the authors’ styles. a 2014 The Author. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http: creativecommons.org licenses by-nc-nd 3.0 ). Character n-grams have been identified as the most successful feature in both singledomain and cross-domain Authorship Attribution (AA), but the reasons for their discriminative value were not fully understood. We identify subgroups of charactern-grams that correspond to linguistic aspects commonly claimed to be covered by these features: morphosyntax, thematic content and style. We evaluate the predictiveness of each of these groups in two AA settings: a single domain setting and a cross-domain setting where multiple topics are present. We demonstrate that characterngrams that capture information about affixes and punctuation account for almost all of the power of character n-grams as features. Our study contributes new insights into the use of n-grams for future AA work and other classification tasks.