Authorship attribution (AA) is a stylometric analysis task of finding the author of an anonymous/disputed text document. In AA, the performance improvement of class-based feature selection schemas, such as Chi-square, and Gini index over frequency-based feature selection schemas, such as document frequency, common n-grams and inverted document frequency has been shown to be limited. In AA, the feature selection process is significantly affected by topic distributions. In this study, we assess the performance of a global feature selection approach into which the document’s topic category is incorporated to scale the existing feature weights. In this approach, the common features of an author among different topics indicate higher relevance for the author and thus have higher weights. On the other hand, features with biased topic distributions are assumed to have high topic relevance and lower weights. In this approach, the global topic measure and the author specific topic measure are combined in order to scale existing selection weights of the features. The 10-fold cross-validation experiment result on a multi-topic dataset with a random topic distribution indicates that our approach improves the performance of Chi-square, modified Gini index, and common n-grams schemas significantly in best performing configurations of the classifiers.

Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.

Domain, genre and topic influences on author style adversely affect the performance of authorship attribution (AA) in multi-genre and multi-domain data sets. Although recent approaches to AA tasks focus on suggesting new feature sets and sampling techniques to improve the robustness of a classification system, they do not incorporate domain-specific properties to reduce the negative impact of irrelevant features on AA. This study presents a novel scaling approach, namely, bucketed common vector scaling, to efficiently reduce negative domain influence without reducing the dimensionality of existing features; therefore, this approach is easily transferable and applicable in a classification system. Classification performances on English-language competition data sets consisting of emails and articles and Turkish-language web documents consisting of blogs, articles and tweets indicate that our approach is very competitive to top-performing approaches in English competition data sets and is significantly improving the top classification performance in mixed-domain experiments on blogs, articles and tweets.