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Article

Classification of Negative Information on Socially Significant Topics in Mass Media

1
Institute of Cybernetics and Information Technology, Satbayev University (KazNRTU), Satpayev str., 22A, Almaty 050013, Kazakhstan
2
Department of Natural Science and Computer Technologies, ISMA University, Lomonosov str., 1, LV-1011 Riga, Latvia
3
Institute of Information and Computational Technologies, Pushkin str., 125, Almaty 050010, Kazakhstan
4
Information-Analytical Center, Dostyk str., 18, Nur-Sultan 010000, Kazakhstan
*
Authors to whom correspondence should be addressed.
The work was funded by a grant BR05236839 of the Ministry of Education and Science of the Republic of Kazakhstan.
Symmetry 2020, 12(12), 1945; https://doi.org/10.3390/sym12121945
Received: 30 October 2020 / Revised: 19 November 2020 / Accepted: 23 November 2020 / Published: 25 November 2020
(This article belongs to the Special Issue 2020 Big Data and Artificial Intelligence Conference)
Mass media not only reflect the activities of state bodies but also shape the informational context, sentiment, depth, and significance level attributed to certain state initiatives and social events. Multilateral and quantitative (to the practicable extent) assessment of media activity is important for understanding their objectivity, role, focus, and, ultimately, the quality of the society’s “fourth power”. The paper proposes a method for evaluating the media in several modalities (topics, evaluation criteria/properties, classes), combining topic modeling of the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties, and classes is calculated after the formation of the topic model of the corpora. Several approaches are used to obtain weights that describe how each topic relates to each evaluation criterion/property and to each class described in the paper, including manual high-level labeling, a multi-corpora approach, and an automatic approach. The proposed multi-corpora approach suggests assessment of corpora topical asymmetry to obtain the weights describing each topic’s relationship to a certain criterion/property. These weights, combined with the topic model, can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804,829 news publications from 40 Kazakhstani sources published from 01 January 2018 to 31 December 2019, to classify negative information on socially significant topics. A BigARTM model was derived (200 topics) and the proposed model was applied, including to fill a table of the analytical hierarchical process (AHP) and all of the necessary high-level labeling procedures. Experiments confirm the general possibility of evaluating the media using the topic model of the text corpora, because an area under receiver operating characteristics curve (ROC AUC) score of 0.81 was achieved in the classification task, which is comparable with results obtained for the same task by applying the BERT (Bidirectional Encoder Representations from Transformers) model. View Full-Text
Keywords: Bayesian rules; journalism; mass media; multimodal mass media assessment; natural language processing; social influence; social significance; topic modeling Bayesian rules; journalism; mass media; multimodal mass media assessment; natural language processing; social influence; social significance; topic modeling
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MDPI and ACS Style

Mukhamediev, R.I.; Yakunin, K.; Mussabayev, R.; Buldybayev, T.; Kuchin, Y.; Murzakhmetov, S.; Yelis, M. Classification of Negative Information on Socially Significant Topics in Mass Media. Symmetry 2020, 12, 1945. https://doi.org/10.3390/sym12121945

AMA Style

Mukhamediev RI, Yakunin K, Mussabayev R, Buldybayev T, Kuchin Y, Murzakhmetov S, Yelis M. Classification of Negative Information on Socially Significant Topics in Mass Media. Symmetry. 2020; 12(12):1945. https://doi.org/10.3390/sym12121945

Chicago/Turabian Style

Mukhamediev, Ravil I., Kirill Yakunin, Rustam Mussabayev, Timur Buldybayev, Yan Kuchin, Sanzhar Murzakhmetov, and Marina Yelis. 2020. "Classification of Negative Information on Socially Significant Topics in Mass Media" Symmetry 12, no. 12: 1945. https://doi.org/10.3390/sym12121945

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