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Automatic Recognition of Native Advertisements for the Slovak Language

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Systems, Signals and Image Processing (IWSSIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1527))

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In recent years the native advertisement is becoming more and more prevalent in online spaces. Differentiating between genuine content and native advertisement using Natural Language Processing is therefore also becoming a very interesting research topic. In this paper, we examine the possibilities of using deep textual representation for the Slovak language to recognize the “PR (Public relations) articles” (that serve as a native advertisement in this context) from authentic news articles on popular Slovak news websites. We show that the BERT (Bidirectional Encoder Representations from Transformers) embeddings as a text representation are sufficient for this task (achieving accuracy over 80% even with a statistical model - Logistic Regression) and that the models generally perform better without prior lemmatization.

We have scraped three Slovak news websites (for a total of 5455 news articles containing both paid-for content and a wide variety of genuine categories), and we have evaluated multiple binary classification methods (Logistic Regression, Random forest classifier and Support Vector Machines) trained on top of generated RoBERTa sentence embeddings. On our testing set, we were able to achieve an accuracy of 85.13%.

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The research described in the paper was done within the International Center of Excellence for Research on Intelligent and Secure Information and Communication Technologies and Systems - II. stage, ITMS code: 313021W404, co-financed by the European Regional Development Fund.

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Correspondence to Zuzana Bukovcikova .

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Andicsova, V., Bukovcikova, Z., Sopiak, D., Oravec, M. (2022). Automatic Recognition of Native Advertisements for the Slovak Language. In: Rozinaj, G., Vargic, R. (eds) Systems, Signals and Image Processing. IWSSIP 2021. Communications in Computer and Information Science, vol 1527. Springer, Cham.

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