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Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models.

The paper discusses the conditions for case marking on partitive constructions in direct object position in Turkish and some related languages. We focus on Turkish and then turn to some details of corresponding constructions in some other Turkic languages and in Standard Mongolian. Turkish exhibits Differential Object Marking, which primarily depends on the semantic-pragmatic factor of specificity. Partitive constructions with the ablative for the superset in Turkish come in different forms, depending on how the subset expression is realized: (a) by a lexical noun as head, (b) by the classifier tane ‘item’, functioning as a “dummy noun”, and (c) by a numeral, quantifier or adjective. Case marking of the direct object is optional for (a), and obligatory for (most instances of) (c). This type of obligatory case marking is dependent on the obligatory marking of the adjective, quantifier or numeral with a default 3 rd person singular agreement suffix, which then requires case marking. Construction (b) does not allow for case marking, when the classifier is bare; when the classifier is followed by the default 3 rd person singular agreement marking, that marking requires obligatory case morphology, just like in construction (c). We hypothesize that structural case marking can either express the semantic-pragmatic condition of specificity in terms of referential anchoring or it must obey a formal condition, namely the requirement of the agreement suffix to be followed by overt case. The languages we have studied show an interesting micro-variation. They differ (among other properties) with respect to classifiers – in particular, with respect to whether they have [+human] classifiers or not. In addition, one language among the languages under investigation, namely Kirghiz, substitutes the agreement marker in its function as a filler of the partitive’s nominal head by a different marker: a morpheme expressing a set. Here, the agreement marker is used to express specificity, given that its presence is not required for formal reasons. In direct object partitive constructions with subset expressions that are expressed as full noun phrase/lexical noun heads (option (a) above), overt accusative case indicates specificity in most of the investigated languages. In options (b) and (c), the investigated languages provide different patterns when marking the referential status of the partitive heads, thus indicating the variation among these languages with respect to the nominal category feature of the partitive heads involved. This article is part of the Special Collection:  Partitives