Pashakhin S. Topic Modeling for Frame Analysis of News Media, in: Proceedings of the Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, Saint-Petersburg, Russia, 10-12 November 2016 / Eds.: S. I. Balandin, A. Filchenkov, L. Pivovarova, J. Zizka. FRUCT Oy, 2016. P. 103-106.
Koltsov, S., Pashakhin, S., & Dokuka, S. (2018). A Full-Cycle Methodology for News Topic Modeling and User Feedback Research. In S. Staab, O. Koltsova, & D. I. Ignatov (Eds.), Social Informatics (Vol. 11185, pp. 308–321). Springer International Publishing. https://doi.org/10.1007/978-3-030-01129-1_19
Koltcov S., Ignatenko V., Pashakhin S. (2020) How Many Clusters? An Entropic Approach to Hierarchical Cluster Analysis. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_40
Koltcov S, Ignatenko V, Pashakhin S. Fast Tuning of Topic Models: An Application of Rényi Entropy and Renormalization Theory. Proceedings. 2020; 46(1):5. https://doi.org/10.3390/ecea-5-06674
Kazun A., Pashakhin S. «Chuzhie vybory»: novosti sosednego gosudarstva o vyborakh prezidenta RF v 2018 g. [‘Alien Elections’: Neighboring State News on the 2018 Russian Presidential Elections]. Journal of Economic Sociology = Ekonomicheskaya sotsiologiya, vol. 22, no 1, pp. 71–91. doi: 10.17323/1726-3247-2021-1-71-91 (in Russian).
Koltsova, O., & Pashakhin, S. (2020). Agenda divergence in a developing conflict: Quantitative evidence from Ukrainian and Russian TV newsfeeds. Media, War & Conflict, 13(3), 237–257. https://doi.org/10.1177/1750635219829876
Koltsova O., Alexeeva S., Pashakhin S., Koltsov S. (2020) PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media. In: Filchenkov A., Kauttonen J., Pivovarova L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. https://doi.org/10.1007/978-3-030-59082-6_1
Moroz A., Pashakhin S., Koltsov S. (2021) Modeling Cascade Growth: Predicting Content Diffusion on VKontakte. In: Antonyuk A., Basov N. (eds) Networks in the Global World V. NetGloW 2020. Lecture Notes in Networks and Systems, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-030-64877-0_12
Talk at UC San Francisco, Department of Testing, San Francisco, California
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk at London School of Testing, London, UK
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA