Abstract
This paper considers an important scientific and applied task of identifying irrelevant and unreliable information on web resources, which is an important area of development and implementation of methods of data mining. The analysis of modern methods and means of estimation of irrelevant and unreliable information from the point of view of estimation of information sources is carried out and the basic problem directions which arise in the course of their functioning are allocated.
A system of indicators for filtering unreliable and irrelevant information, which is obtained on the basis of several sources, is proposed. Based on this system, a method of checking information from web resources for relevance and reliability has been implemented. This approach is based on the possibility of using a predefined resource, the data from which are only reliable.
A method of detecting inaccurate and irrelevant information has been developed, taking into account the peculiarities of its distribution through relevant pages in social networks and the use of multitasking classification of information obtained from various data sources.
The proposed intelligent data processing methods together with other methods of intellectual analysis used to evaluate information obtained from the Internet, will significantly increase the efficiency of the process of establishing irrelevance and inaccuracy of information, and will build an assessment of a particular web resource for publishing and disseminating such information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vo, N., Lee, K.: The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18), pp. 275–284. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3209978.3210037
Ye, J., Skiena, S.: MediaRank: Computational Ranking of Online News Sources, pp. 2469–2477 (2019). https://doi.org/10.1145/3292500.3330709.
Yu, B., Zhang, Z., Liu, T., Wang, B., Li, S., Li, Q.: Beyond word attention: using segment attention in neural relation extraction. IJCAI (2019)
Wang, C.: Relation extraction. In: Proceedings of the Twenty-Eighth International Joint Conference on Artifcial Intelligence. International Joint Conferences on Artifcial Intelligence Organization, pp. 5401–5407. https://doi.org/10.24963/ijcai.2019/750
Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. 53(5), 1–40, Article 109 (2020). https://doi.org/10.1145/3395046
Ray, O., Qian, J., Wang, W.Y.: A survey on natural language processing for fake news detection (2020). ArXiv abs/1811.00770
Dyvak, M., Papa, O., Melnyk, A., Pukas, A., Porplytsya, N., Rot, A.: Interval model of the efficiency of the functioning of information web resources for services on ecological expertise. Mathematics 8(12), 2116 (2020). https://doi.org/10.3390/math8122116
Nørregaard, J., Horne, B.D., Adalı, S.: NELA-GT-2018: a large multi-labelled news dataset for the study of misinformation in news articles. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 630–638 (2018)
Trivedi, R., Sisman, B., Dong, X., Faloutsos, C., Ma, J., Zha, H.: LinkNBed: multi-graph representation learning with entity linkage, pp. 252–262 (2018). https://doi.org/10.18653/v1/P18-1024
Vilone, G., Longo, L.: Explainable artificial intelligence: a systematic review (2020)
Kovbasistyi, A., Melnyk, A., Dyvak, M., Brych, V., Spivak, I.: Method for detection of non-relevant and wrong information based on content analysis of web resources. In: 2017 XIIIth International Conference on Per-spective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, 2017, pp. 154–156. https://doi.org/10.1109/MEMSTECH.2017.7937555
Dyvak, M.P., Kovbasistyi, A.V., Melnyk, A.M., Turchyn, L.Y., Martsenyuk Y.O.: System for web resources content structuring and recognizing with the machine learning elements. Radio Electron. Comput. Sci. Control (3) (2018). https://doi.org/10.15588/1607-3274-2018-3-14
Dyvak, A., Melnyk, A., Shevchuk, R., Kovbasistyi, A., Huhul, O., Tymchyshyn, V.: Mathematical modeling of the estimation process of functioning efficiency level of information web-resources. In: Proceedings of the 2020 10th International Conference “Advanced Computer Information Technologies” – Deggendorf, Germany, 16–18 September 2020, pp. 492–496 (2020)
Bian, T., et al.: Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020). arXiv preprint arXiv:2001.06362
Sample, C., McAlaney, J., Bakdash, J.Z., Thackray, H.: A cultural exploration of social media manipulators. In: Proceedings of the 17th European Conference on Cyber Warfare and Security, Oslo, Norway, pp. 342–341 (2018)
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., Liu, Y.: Combating fake news: a survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol. 10, 1–42 (2019)
Hashimoto, T., Shepard, D.L., Kuboyama, T., Shin, K., Kobayashi, R., Uno, T.: Analyzing temporal patterns of topic diversity using graph clustering. J. Supercomput. 77(5), 4375–4388 (2020). https://doi.org/10.1007/s11227-020-03433-5
Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 797–806 (2017)
Dutta, H.S., Dutta, V.R., Adhikary, A., Chakraborty, T.: HawkesEye: detecting fake retweeters using Hawkes process and topic modeling. IEEE Trans. Inf. Forensics Secur. 15, 2667–2678 (2020)
Gontier, C., Pfister, J.P.: Identifiability of a binomial synapse. Front. Comput. Neurosci. 14, 86 (2020). https://doi.org/10.3389/fncom.2020.558477. PMID: 33117139
Gao, S., Ma, J., Chen, Z.: Modeling and predicting retweeting dynamics on microblogging platforms. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM 2015, pp. 107–116 (2015)
Lukasik, M., Srijith, P.K., Vu, D., Bontcheva, K., Zubiaga, A., Cohn, T.: Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, pp. 393–398 (2016)
Shevchuk, R., Melnyk, A., Opalko, O., Shevchuk, H.: Software for automatic estimating security settings of social media accounts. In: 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), pp. 769–773 (2020). https://doi.org/10.1109/ACIT49673.2020.9208879
Ganji, M.D.; Rahmanzadeh, A.: Chapter 6—Mathematical modeling and simulation. In: Nguyen-Tri, P., Do, T.-O., Nguyen, T.A. (eds.) Smart Nanocontainers. Micro and Nano Technologies, pp. 89–102. Elsevier, Amsterdam (2020). ISBN 978-0-12-816770-0
Brainard, J., Hunter, P., Hall, I.: An agent-based model about the effects of fake news on a norovirus outbreak. Rev. D’épidémiologie Santé Publique 68, 99–107 (2020)
Wahid-Ul-Ashraf, A., Budka, M., Musial, K.: Simulation and Augmentation of Social Networks for Building Deep Learning Models (2019). arXiv arXiv:1905.09087
Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. 53, 1–40 (2020)
Shrivastava, G., Kumar, P., Ojha, R.P., Srivastava, P.K., Mohan, S., Srivastava, G.: Defensive modeling of fake news through online social networks. IEEE Trans. Comput. Social Syst. 7, 1159–1167 (2020)
Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: The online human-bot interactions: detection, estimation, and characterization. In: Proceedings of the International AAAI Conference on Web and Social Media, Palo Alto, CA, USA, 25–28 June 2018 (2018)
Yang, K., Varol, O., Davis, C.A., Ferrara, E., Flammini, A., Menczer, F.: Arming the public with artificial intelligence to counter social bots. Hum. Behav. Emerg. Technol. 1, 4861 (2019)
Saura, J.R., Ribeiro-Soriano, D., Palacios-Marqués, D.: From user-generated data to data-driven innovation: a research agenda to understand user privacy in digital markets. Int. J. Inf. Manag. 102331 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dyvak, M., Melnyk, A., Mazepa, S., Stetsko, M. (2022). An Ontological Approach to Detecting Irrelevant and Unreliable Information on Web-Resources and Social Networks. In: Klymash, M., Beshley, M., Luntovskyy, A. (eds) Future Intent-Based Networking. Lecture Notes in Electrical Engineering, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-030-92435-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-92435-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92433-1
Online ISBN: 978-3-030-92435-5
eBook Packages: EngineeringEngineering (R0)