Evaluation without ground truth : a comparative study on preference mining techniques in Twitter social network

• Autorzy: Pereira Fabiola S. F.

Identyfikatory

DOI

10.15439/2022F280

• Języki publikacji: EN

Abstrakty

EN

In social media research the lack of ground truth for evaluation is a recurrent problem. We study the preference mining task in Twitter network which suffers from this lack of ground truth problem. We implement three different methods from literature, considering a common preference domain of news and carry a comparative study among them. Our preliminary findings show that is possible to combine methods in order to avoid unfeasible user surveying baselines and enable the evaluation of techniques. In the future, our target is to completely eliminate ground truth sets and evaluate based on correlation and causality techniques.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 31
• Strony: 65--68
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Pereira Fabiola S. F.

• Faculty of Computing Federal University of Uberlândia Uberlândia, Minas Gerais, Brazil

Bibliografia

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• 5. F. S. F. Pereira, S. de Amo, and J. Gama, “On using temporal networks to analyze user preferences dynamics,” in Discovery Science: 19th International Conference, DS 2016, Bari, Italy, 2016., 2016.
• 6. X. Liu, “Modeling users’ dynamic preference for personalized recommendation,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15), 2015, pp. 1785-1791.
• 7. F. S. F. Pereira and S. de Amo, “Mineracao de preferencias do usuario em textos de redes sociais usando sentencas comparativas,” in Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2015, pp. 94-97.
• 8. M. A. Abbasi, J. Tang, and H. Liu, “Scalable learning of users’ preferences using networked data,” in Proceedings of the 25th ACM Conference on Hypertext and Social Media, ser. HT ’14. New York, NY, USA: ACM, 2014, pp. 4-12.
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• 10. T. Elsaleh, S. Enshaeifar, R. Rezvani, S. T. Acton, V. Janeiko, and M. Bermudez-Edo, “Iot-stream: A lightweight ontology for internet of things data streams and its use with data analytics and event detection services,” Sensors, vol. 20, no. 4, 2020. [Online]. Available: https://www.mdpi.com/1424-8220/20/4/953
• 11. T. R. Tangherlini, S. Shahsavari, B. Shahbazi, E. Ebrahimzadeh, and V. Roychowdhury, “An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, pizzagate and storytelling on the web,” PLOS ONE, vol. 15, no. 6, pp. 1-39, 06 2020. [Online]. Available: https://doi.org/10.1371/journal.pone.0233879

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).