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Toxic Communication on Twitch.tv. Effect of a Streamer
Ch. 34.
Poyane R.
This paper investigates on how spectators communication is organized in chats during broadcasts on Twitch.tv with the main focus on toxic communication. The main purpose of the paper is to understand how socio-demographic characteristics of a broadcaster and channel settings which broadcaster can control affect communication in a chat. Chat logs from Twitch.tv channels were used to create a topic model of viewers discussions. The result of regression analysis indicates that socio-demographic characteristics of a broadcaster have a statistically significant effect on the type of communication, which is manifested in chat.
Knyazev I. А., Медиаальманах 2025 № 2 С. 62–68
This study is devoted to the analysis of the peculiarities of perception of reality show programs by the Russian audience in the first half of the 2020s. The large number of new reality shows that have appeared on the domestic television market in recent years requires assessment and analysis.
years requires evaluation and analysis, including one ...
Added: March 19, 2026
Дейнеко А. Г., Труды по интеллектуальной собственности 2025 Т. 52 № 1 С. 8–18
The article discusses various approaches to the legal analysis of streaming, typical for private law and public law sciences. In the field of private law, streaming is considered mainly through the prism of the method of using the exclusive right to an audiovisual or other complex work (video game, TV broadcasting, etc.), which can be ...
Added: July 4, 2025
Анисимов В. А., Гуманитарные исследования в Восточной Сибири и на Дальнем Востоке 2024 № 3 С. 114–122
The article is devoted to the analysis of the phenomenon of monoculture, chronologically divided into pre-digital and digital. Pre-digital monoculture is presented through the critical analysis of cultural consumption. Using the case of such cultural texts as game of Thrones, Harry Potter and Star Wars, the author shows that pre-digital monoculture is a part of ...
Added: November 7, 2024
Trofimov A., Шавкунов М. В., Reznick S. et al., , in: Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems.: NY: Association for Computing Machinery (ACM), 2019. P. 264–265.
Large-scale classification of text streams is an essential problem that is hard to solve. Batch processing systems are scalable and proved their effectiveness for machine learning but do not provide low latency. On the other hand, state-of-the-art distributed stream processing systems are able to achieve low latency but do not support the same level of ...
Added: December 27, 2019
Poyane R., , in: Proceedings of the 22nd International Academic Mindtrek Conference.: NY: ACM, 2018. P. 262–265.
This paper is devoted to the study of how toxic communication is structured in chats during streams on Twitch.tv and how chat size affects it. The data from Twitch chat logs was used to create a topic model of themes which are discussed by viewers during stream. The result indicate that there are statistically significant ...
Added: December 14, 2019
Koltsov S., Nikolenko S. I., Koltsova O. et al., , in: WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference.: Elsevier, 2016. P. 342–343.
Topic modeling is a powerful tool for analyzing large collections of user-generated web content, but it still suffers from problems with topic stability, which are especially important for social sciences. We evaluate stability for differenttopic models and propose a new model, granulated LDA,that samples short sequences of neighboring words at once. We show that gLDA ...
Added: October 24, 2016
Korolev D., , in: DAAAM International scientific book 2014.: Wien: DAAAM International Publishing, 2014. Ch. 49 P. 605–614.
Using video on the Internet has become a common practice, but the television-like ‘passive viewer’ approach misses the benefits of the interactive nature of the Internet. The technological limitations of television can be overridden by the Internet. Having multiple sources of input does not mean they should be merged into one editor-controlled flat output. Treating ...
Added: February 21, 2015
Vorontsov K. V., Potapenko A., Machine Learning 2015 Vol. 101 No. 1 P. 303–323
Probabilistic topic modeling of text collections has been recently developed mainly within the framework of graphical models and Bayesian inference. In this paper we introduce an alternative semi-probabilistic approach, which we call additive regularization of topic models (ARTM). Instead of building a purely probabilistic generative model of text we regularize an ill-posed problem of stochastic matrix factorization ...
Added: February 19, 2015
Маслинский К. А., Детские чтения 2014 Т. 6 № 2 С. 112–126
The aim of this article is to analyze the discursive background for the characters of teachers in the Soviet school story of the afterwar period. The 1,8 million words corpus for the study was compiled of the novels about school and schooling by 37 authors, written in 1940-s — 1980-s. The contents of the episodes ...
Added: January 17, 2015
Konstantin Vorontsov, Anna Potapenko, , in: Communications in Computer and Information ScienceVol. 436: Analysis of Images, Social Networks and Texts. Third International Conference, AIST 2014 Yekaterinburg, Russia, April 10–12, 2014 Revised Selected Papers.: Cham: Springer, 2014. P. 29–46.
Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this tutorial we introduce a novel non-Bayesian approach, called Additive Regularization of Topic Models. ARTM is free of redundant probabilistic assumptions and provides a simple inference for many combined and multi-objective topic models. ...
Added: December 5, 2014
К.В. Воронцов, Потапенко А. А., Машинное обучение и анализ данных 2013 Т. 1 № 6 С. 657–686
Probabilistic topic models discover a low-dimensional interpretable representation of text corpora
by estimating a multinomial distribution over topics for each document and a multinomial
distribution over terms for each topic. A uni ed family of expectation-maximization (EM) like
algorithms with smoothing, sampling, sparsing, and robustness heuristics that can be used in
any combinations is considered. The known models PLSA (probabilistic ...
Added: May 6, 2014
Bodrunova S., Koltsov S., Koltsova O. et al., , in: Proceedings of the 12th Mexican International Conference on Artificial Intelligence (MICAI 2013)* I: Advances in Artificial Intelligence and Its Applications.: Berlin: Springer, 2013. P. 265–274.
An important text mining problem is to fi nd, in a large collection of texts, documents related to speci c topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to nd the most representative documents for subsequent qualitative interpretation. To solve this problem, we ...
Added: March 19, 2014
Berlin: Springer, 2013.
An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we ...
Added: March 19, 2014