vovrВысшее образование в России (Vysshee obrazovanie v Rossii = Higher Education in Russia)Vysshee Obrazovanie v Rossii = Higher Education in Russia0869-36172072-0459Moscow Polytechnic University10.31992/0869-3617-2024-33-2-89-108vovr-4807Research ArticleСтатьиЦифровой след: оценка удовлетворённости студентов качеством образованияDigital Footprint: Assessing Student Satisfaction with Education Qualityhttps://orcid.org/0000-0001-7189-0002КришталМ. М.KrishtalM. M.

Криштал Михаил Михайлович – д-р физ.-мат. наук, профессор, ректор

445020, Самарская обл., г. Тольятти, ул. Белорусская, 14

Mikhail M. Krishtal – Dr. Sci. (Physics-Mathematics), Professor, Rector

445020, Samara region, Togliatti, Belorusskaya str., 14

office@tltsu.ruhttps://orcid.org/0000-0002-3553-2272БогдановаА. В.BogdanovaA. V.

Богданова Анна Владимировна – канд. пед. наук, начальник отдела технологий онлайн-образования

445020, Самарская обл., г. Тольятти, ул. Белорусская, 14

WoS Reseacher ID: GRO-7042-2022

Anna V. Bogdanova – Cand. Sci. (Pedagogical Sciences), Head of Online Education Technologies Department

445020, Samara region, Togliatti, Belorusskaya str., 14

WoS Reseacher ID: GRO-7042-2022

 

a.bogdanova@tltsu.ruhttps://orcid.org/0000-0002-8419-6404МягковМ. Г.MyagkovM. G.

Мягков Михаил Георгиевич – PhD (пед. наук), профессор, ведущий научный сотрудник центра когнитивных исследований и нейронаук

634050, Томск, пр-т Ленина, 36

Mikhail G. Myagkov – PhD (Pedagogical Sciences), Professor, Leading Researcher, Centre for Cognitive Research and Neuroscience

36, Lenin Ave., Tomsk, 634050

myagkov@darkwing.uoregon.eduhttps://orcid.org/0000-0002-6069-779XАлександроваЮ. К.AlexandrovaYu. K.

Александрова Юлия Константиновна – мл. науч. сотрудник Центра прикладного анализа больших данных

634050, Томск, пр-т Ленина, 36

Yulia K. Alexandrova – Junior Researcher, Centre for Applied Big Data Analysis

36, Lenin Ave., Tomsk, 634050

jalexandrova@data.tsu.ruТольяттинский государственный университетРоссияTogliatti State UniversityRussian FederationТомский государственный университетРоссияTomsk State UniversityRussian Federation20240603202433289108Copyright © Криштал М.М., Богданова А.В., Мягков М.Г., Александрова Ю.К., 20242024Криштал М.М., Богданова А.В., Мягков М.Г., Александрова Ю.К.Krishtal M.M., Bogdanova A.V., Myagkov M.G., Alexandrova Y.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vovr.elpub.ru/jour/article/view/4807

Пандемия COVID-19 изменила подход к организации учебного процесса во всём мире. Российские вузы также были поставлены перед необходимостью быстро перевести всё обучение в онлайн-формат. Значение удовлетворённости студентов качеством образовательного процесса при онлайн-обучении повышается, поскольку является важным условием мотивации. Для понимания удовлетворённости студентов при переходе к новому формату реализации образовательного процесса были проанализированы цифровые следы студентов из социальной сети ВКонтакте с применением отдельных инструментов Big Data на программной платформе PolyAnalyst. Это позволило проследить изменения настроения студентов и на примере отдельно взятого вуза выявить и объяснить отклонения в отношении студентов к реализации учебного процесса, а также верифицировать методику. Разработанная авторами методика даёт возможность обнаружить проблемные вопросы в вузе, в том числе момент их возникновения, актуальность, степень озабоченности студентов. Такой контент-анализ можно применять не только для оценки удовлетворённости студентов качеством учебного процесса, но и для отслеживания появления любых проблем, которые вызывают беспокойство и сильные реакции со стороны студенческого сообщества, а также других сообществ и отдельных коллективов.

The COVID-19 pandemic has changed the way learning is organized around the world. Russian universities have also been faced with the need to quickly transfer all teaching to an online format. The importance of student satisfaction with the education quality in online learning is increasing, since it is an important condition for motivation. The paper shows that based on the analysis of students’ messages in the social network, it is possible to observe and analyze the overall dynamics and trends in student community satisfaction with the quality of the learning / the efficiency of universities and conduct a comparative analysis of the identified characteristic data groups with their totality. It is shown that the data on the reaction of students of a particular university may have significant deviations from the totality of data, which reflects the characteristics of the reaction of students of a particular university to events occurring at the same time. This may indicate the internal differences of the university, which form an appropriate response to external events.

To understand the satisfaction of students in the transition to a new implementation format of the learning. The digital traces of students from the social network VKontakte were analyzed using individual Big Data tools on the PolyAnalyst software platform. This made it possible to trace changes in the mood of students and, on the example of a single university, to identify and explain deviations in the attitude of students to the implementation of the learning, as well as to verify the methodology. The methodology developed by authors makes it possible to detect problematic issues in the university, including the moment of their occurrence, relevance, degree of concern of students. Such content analysis can be used not only to assess students’ satisfaction with the quality of the learning, but also to monitor the emergence of any problems that cause concern and strong reactions on the part of the student community, as well as other communities and individual groups.

пандемия COVID-19высшее образованиекачество образованияРосдистантонлайн-обучениедистанционное обучениецифровой следбольшие данные (Big Data)социальные сетианализ настроенийCOVID-19 pandemicRosdistantquality of educationonline learningdigital footprintbig datasocial networkssentiment analysisReferencesBakker A., Cai J. & Zenger L. Future themes of mathematics education research: an international survey before and during the pandemic // Educational Studies in Mathematics. 2021. No. 107. P. 1–24. DOI: 10.1007/s10649-021-10049-wBakker, A., Cai, J. & Zenger, L. (2021). Future themes of mathematics education research: an international survey before and during the pandemic. Educational Studies in Mathematics. No. 107, pp. 1-24, doi: 10.1007/s10649-021-10049-wTurnbull D., Chugh R. & Luck J. Transitioning to e-learning during the COVID-19 pandemic: how have higher education institutions responded to the challenge? // Education and Information Technologies. 2021. No. 26. P. 6401–6419. DOI: 10.1007/s10639-021-10633-wTurnbull, D., Chugh, R. & Luck, J. (2021). Transitioning to E-Learning during the COVID-19 pandemic: How have Higher Education Institutions responded to the challenge? Education and Information Technologies. No. 26, pp. 6401-6419, doi: 10.1007/s10639-021-10633-wRapanta C., Botturi L., Goodyear P. et al. Balancing technology, pedagogy and the new normal: post-pandemic challenges for higher education // Postdigital Science and Education. 2021. No. 3. P. 715–742. DOI: 10.1007/s42438-021-00249-1Rapanta, C., Botturi, L., Goodyear, P. et al. (2021). Balancing Technology, Pedagogy and the New Normal: Post-pandemic Challenges for Higher Education. Postdigital Science and Education. No. 3, pp. 715-742, doi: 10.1007/s42438-021-00249-1El-Sayad G., Md Saad N.H. & Thurasamy R. How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic // Journal of Computers in Education. 2021. No. 8. P. 527– 550. DOI: 10.1007/s40692-021-00191-yEl-Sayad, G., Md Saad, N.H. & Thurasamy, R. (2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. Journal of Computers in Education. No. 8, pp. 527-550, doi: 10.1007/s40692-021-00191-yOsina D.M., Tolstopyatenko G.P., Malinovsky A.A. Digitalization of higher legal education in Russia in the era of COVID-19 // Engineering Economics: Decisions and Solutions from Eurasian Perspective: ed. by S. Ashmarina, V. Mantoulenko, M. Vochozka. 2021. Vol. 139. P. 392–398. DOI: 10.1007/978-3-030-53277-2_47Osina, D.M., Tolstopyatenko, G.P., Malinovsky, A.A. (2021). Digitalization of higher legal education in Russia in the era of COVID-19. In: Ashmarina S., Mantoulenko V., Vochozka M. (eds.). Engineering Economics: Decisions and Solutions from Eurasian Perspective. Vol. 139, pp. 392- 398, doi: 10.1007/978-3-030-53277-2_47Shchekotin E., Goiko V., Myagkov M., Dunaeva D. Assessment of quality of life in regions of Russia based on social media data // Journal of Eurasian Studies. 2021. No. 12 (2). P. 182–198. DOI: 10.1177/18793665211034185Shchekotin, E., Goiko, V., Myagkov, M., Dunaeva, D. (2021). Assessment of quality of life in regions of Russia based on social media data. Journal of Eurasian Studies. Vol. 12, no. 2, pp. 182- 198. DOI: 10.1177/18793665211034185Zielinski A., Bьgel U. Multilingual analysis of Twitter news in support of mass emergency events // Proc. of the 9th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2012), Vancouver, Canada, 2012. DOI: 10.24406/publica-fhg-377702Zielinski, A., Bьgel, U. (2012). Multilingual analysis of Twitter news in support of mass emergency events. Proc. of the Ninth International Conference on Information Systems for Crisis Response and Management (ISCRAM 2012), Vancouver, Canada, doi: 10.24406/publicafhg-377702Shchekotin E., Myagkov M., Goiko V., Kashpur V. Digital methods of analysis of subjective quality of life: Case of Russian regions // Administratie si Management Public. 2021. No. 36. P. 25–48. DOI: 10.24818/amp/2021.36-02Shchekotin, E., Myagkov, M., Goiko, V., Kashpur, V. (2021). Digital methods of analysis of subjective quality of life: Case of Russian regions. Administratie si Management Public. No. 36, pp. 25-48, doi: 10.24818/amp/2021.36-02Yin J., Lampert A., Cameron M.A., Robinson B., Power R. Using social media to enhance emergency situation awareness // IEEE Intell Syst. 2012. No. 27(6). P. 52–59. DOI:10.1109/MIS.2012.6Yin, J., Lampert, A., Cameron, M.A., Robinson, B., Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intell Syst. Vol. 27, no. 6, pp. 52-59, doi:10.1109/MIS.2012.6Orlova V., Goiko V., Alexandrova Yu., Petrov E. Analysis of approaches to study identification in social media // E3S Web of Conferences. 2020. Vol. 210: 16034. DOI: 10.1051/e3sconf/202021016034Orlova, V., Goiko, V., Alexandrova, Yu., Petrov, E. (2020). Analysis of approaches to study identification in social media. E3S Web of Conferences. Vol. 210: 16034, doi: 10.1051/e3sconf/202021016034Verma S., Vieweg S., Corvey W., Palen L., Martin J., Palmer M., Schram A., Anderson K. Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets During Mass Emergency // Proceedings of the International AAAI Conference on Web and Social Media. 2021. Vol. 5. No. 1. P. 385–392. DOI: 10.1609/icwsm.v5i1.14119Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J., Palmer, M., Schram, A., & Anderson, K. (2021). Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets During Mass Emergency. Proc. of the International AAAI Conference on Web and Social Media. Vol. 5, no. 1, pp. 385-392, doi: 10.1609/icwsm.v5i1.14119Liu B. Sentiment analysis and subjectivity // Handbook of Natural Language Processing, Chapman & Hall/CRC Machine Learning & Pattern Recognition Series: ed. by Indurkhya N, Damerau FJ. Florida: Taylor & Francis Group, Boca Raton, 2010. P. 627–666. URL: https://www.routledge.com/Handbook-of-Natural-Language-Processing/Indurkhya-Damerau/p/book/9781420085921 (дата обращения: 22.12.2022).Liu, B. (2010). Sentiment analysis and subjectivity. In Indurkhya, N., Damerau, F.J. Handbook of Natural Language Processing, Chapman & Hall/CRC Machine Learning & Pattern Recognition Series. Florida: Taylor & Francis Group, Boca Raton, pp. 627-666. Available at: https://www.routledge.com/Handbook-of-Natural-Language-Processing/Indurkhya-Damerau/p/book/9781420085921 (accessed 22.12.2022).Pang B., Lee L. Opinion mining and sentiment analysis // Foundations and Trends in Information Retrieval. 2008. Vol. 2. No. 1–2. P. 1–135. DOI: 10.1561/1500000011Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval. Vol. 2, no. (1-2), pp. 1-135, doi: 10.1561/1500000011Treude C., Barzilay O., Storey M.A. How do programmers ask and answer questions on the web? (nier track) // Proc. of the 33rd International Conference on Software Engineering, ICSE. 2011. Vol. 11. P. 804–807. DOI: 10.1007/s11390-016-1672-0Treude, C., Barzilay, O., Storey, M.A. (2011). How do programmers ask and answer questions on the web? (nier track). Proc. of the 33rd International Conference on Software Engineering, ICSE. Vol. 11, pp. 804-807, doi: 10.1007/s11390-016-1672-0Mezouar M.E., Zhang F., Zou Y. Are tweets useful in the bug fixing process? An empirical study on firefox and chrome // Empirical Software Engineering. 2018. Vol. 23. No. 3. P. 1704– 1742. DOI: 0.1007/s10664-017-9559-4Mezouar, M.E., Zhang, F., Zou, Y. (2018). Are tweets useful in the bug fixing process? An empirical study on firefox and chrome. Empirical Software Engineering. Vol. 23, no. 3, pp. 1704-1742, doi: 0.1007/s10664-017-9559-4Aniche M., Treude C., Steinmacher I., Wiese I., Pinto G., Storey M.A., Gerosa M.A. How modern news aggregators help development communities shape and share knowledge // Proc. of the 40th International Conference on Software Engineering, ICSE ’18. 2018. P. 499–510. DOI: 10.1145/3180155.3180180Aniche, M., Treude, C., Steinmacher, I., Wiese, I., Pinto, G., Storey, M.A., Gerosa, M.A. (2018). How modern news aggregators help development communities shape and share knowledge. Proc. of the 40th International Conference on Software Engineering, ICSE ’18. Pp. 499-510, doi: 10.1145/3180155.3180180Sharma U., Datta R.K., Pabreja K. Sentiment Analysis and Prediction of Election Results 2018 // Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems: ed. by R. Shukla, J. Agrawal, S. Sharma, N. Chaudhari, K. Shukla. 2020. Vol. 100. P. 727– 739. DOI: 10.1007/978-981-15-2071-6_61Sharma, U., Datta, R.K., Pabreja, K. (2020). Sentiment Analysis and Prediction of Election Results 2018. In: Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds). Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems. Vol. 100, pp. 727-739, doi: 10.1007/978-981-15-2071-6_61Georgiadou E., Angelopoulos S., Drake H. Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes // International Journal of Information Management. 2020. Vol. 51(2): 102048. DOI: 10.1016/j.ijinfomgt.2019.102048Georgiadou, E., Angelopoulos, S., Drake, H. (2020). Big data analytics and international negotiations: Sentiment analysis of brexit negotiating outcomes. International Journal of Information Management. Vol. 51, no. 2: 102048, doi: 10.1016/j.ijinfomgt.2019.102048Si X.-M., Li C. Bounded confidence opinion dynamics in virtual networks and real networks // Journal of Computers. 2018. Vol. 29(3). P. 220– 228. DOI: 10.3966/199115992018062903021Si, X.-M., Li, C. (2018). Bounded confidence opinion dynamics in virtual networks and real networks. Journal of Computers. Vol. 29, no. 3, pp. 220-228, doi: 10.1016/j.ijinfomgt.2019.102048Kahani N., Bagherzadeh M., Dingel J., Cordy J.R. The problems with eclipse modeling tools: A topic analysis of eclipse forums // Proc. of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, MODELS ’16. 2016. P. 227–237. DOI: 10.1145/2976767.2976773Kahani, N., Bagherzadeh, M., Dingel, J., Cordy, J.R. (2016). The problems with eclipse modeling tools: A topic analysis of eclipse forums. Proc. of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, MODELS ’16, pp. 227-237, doi: 10.1145/2976767.2976773Squire M. Should we move to stack overflow? Measuring the utility of social media for developer support // Proc. of the 37th International Conference on Software Engineering, ICSE ’15. 2015. Vol. 2. P. 219–228. DOI: 10.1109/ICSE.2015.150Squire, M. (2015). Should we move to stack overflow? Measuring the utility of social media for developer support. Proc. of the 37th International Conference on Software Engineering, ICSE ’15. Vol. 2, pp. 219-228, doi: 10.1109/ICSE.2015.150Lazar J. Managing digital accessibility at universities during the COVID-19 pandemic // Universal Access in the Information Society. 2021. Vol. 21. P. 749-765. DOI: 10.1007/s10209-021-00792-5Lazar, J. (2021). Managing digital accessibility at universities during the COVID-19 pandemic. Universal Access in the Information Society. Vol. 21, pp. 749-765, doi: 10.1007/s10209-021-00792-5Ligthart A., Catal C., Tekinerdogan B. Systematic reviews in sentiment analysis: a tertiary study // Artificial Intelligence Review. 2021. Vol. 54. No. 2. P. 1–51. DOI: 10.1007/s10462-021-09973-3Ligthart, A., Catal, C. & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review. Vol. 54, no. 2, pp. 1-51, doi: 10.1007/s10462-021-09973-3Li K.C., Wong B.Tm. The Opportunities and Challenges of Social Media in Higher Education: A Literature Review // SN Computer Science. 2021. Vol. 2: 455. DOI: 10.1007/s42979-021-00857-5Li, K.C., Wong, B.Tm. (2021). The Opportunities and Challenges of Social Media in Higher Education: A Literature Review. SN Computer Science. No. 2: 455, doi: 10.1007/s42979-021-00857-5Purcell W.M., Lumbreras J. Higher education and the COVID-19 pandemic: navigating disruption using the sustainable development goals // Discover Sustainability. 2021. Vol. 2: 6, doi: 10.1007/s43621-021-00013-2Purcell, W.M., Lumbreras, J. (2021). Higher education and the COVID-19 pandemic: navigating disruption using the sustainable development goals. Discover Sustainability. Vol. 2: 6, doi: 10.1007/s43621-021-00013-2Абрамова М.О., Баранников К.А., Груздев И.А. и др. (2021). Качество образования в российских вузах: что мы поняли в пандемии: Аналитический доклад / науч. ред. Е.А. Суханова, И.Д. Фрумина. Томск: Издательство Томского государственного университета, 2021. 46 с. https://publications.hse.ru/pubs/share/direct/506461902.pdf (дата обращения: 18.06.2023).Abramova, M.O., Barannikov, K.A., Gruzdev, I.A. et al. (2021). Quality of Education in Russian Universities: What We Understood in the Pandemic: Analytical Report / sci. ed. by E.A. Sukhanova, I.D. Froumin. Tomsk: Tomsk State University Press, 46 p. Available at: https://publications.hse.ru/pubs/share/direct/506461902.pdf (accessed: 18.06.2023). (In Russ.).Самые популярные социальные сети в России // Газета «Коммерсантъ». № 174/П от 27.09.2021, с. 10. URL: https://www.kommersant.ru/doc/5006356 (дата обращения: 22.12.2022).The most popular social networks in Russia. Kommersant Newspaper. No. 174/П of 27.09.2021, p. 10. Available at: https://www.kommersant.ru/doc/5006356 (accessed: 22.12.2022). (In Russ.).Демченко Н., Макаров И. Instagram обошёл «ВКонтакте» по числу активных авторов в России. URL: https://www.rbc.ru/technology_and_media/30/11/2020/5fc4aa2c9a7947f4bfbce6db# (дата обращения: 22.12.2022).Demchenko, N., Makarov, I. Instagram Bypassed “VKontakte” in the Number of Active Authors in Russia. Available at: https://www.rbc.ru/technology_and_media/30/11/2020/5fc4aa2c9a7947f4bfbce6db# (accessed: 22.12.2022). (In Russ.).Magdin M., Balogh Z., Reichel J. et al. Automatic detection and classification of emotional states in virtual reality and standard environments (LCD): comparing valence and arousal of induced emotions // Virtual Reality. 2021. Vol. 25. P. 1029–1041. DOI: 10.1007/s10055-021-00506-5Magdin, M., Balogh, Z., Reichel, J. et al. (2021). Automatic detection and classification of emotional states in virtual reality and standard environments (LCD): comparing valence and arousal of induced emotions. Virtual Reality. No. 25, pp. 1029-1041, doi: 10.1007/s10055-021-00506-5Сигаева Е. Речевые парадоксы: Мы, Ты или Я? // Проект В17. URL: https://www.b17.ru/article/5993/ (дата обращения: 22.12.2022).Sigaeva, E. Speech Paradoxes: We, You or is it Me? Available at: https://www.b17.ru/article/5993/ (accessed: 22.12.2022). (In Russ.).Патент № 2769644 Российская Федерация, МПК G06Q 50/20 (2012.01), СПК G06Q 50/20 (2021.08). Система высшего образования онлайн: № 2021117849, заявлено 19.06.2021: опубликовано: 04.04.2022 / Криштал М.М., Боюр Р.В., Бабошина Е.С., Кутузов А.И., Соколова Т.А., Дроздова М.А., Репина Е.А., Денисова О.П., Богданова А.В., Хамидуллова Л.Р., Гасанова Р.М.; патентообладатель ТолГУ. – 44 с.Patent No. 2769644 the Russian Federation, МПК G06Q 50/20 (2012.01), СПК G06Q 50/20 (2021.08). Online Higher Education System: No. 2021117849, applied 19.06.2021: published: 04.04.2022 / Krishtal, M.M. , Boyur, R.V., Baboshina, E.S., Kutuzov, A.I., Sokolova, T.A., Drozdova, M.A., Repina, E.A., Denisova, O.P., Bogdanova, A.V., Khamidullova, L.R., Gasanova, R.M.; Patent Holder: Togliatti State University. 44 pКриштал М.М., Боюр Р.В., Бабошина Е.С., Кутузов А.И., Соколова Т.А., Денисова О.П. Опыт построения системы высшего образования онлайн (проект Росдистант). Предпосылки возникновения проекта. Ч. 1 // Стандарты и качество. 2021. № 11. С. 52–56. DOI: 10.35400/0038-9692-2021-11-52-56Krishtal, M.M., Boyur, R.V., Baboshina, E.S., Kutuzov, A.I., Sokolova, T.A., Denisova, O.P. (2021). Experience of building an online higher education system (Rosdistant project). Prerequisites for the emergence of the project. P. 1. Standarty i Kachestvo = Standards and Quality. No. 11, pp. 52-56, doi: 10.35400/0038-9692-2021-11-52-56 (In Russ., abstract in Eng.).Криштал М.М., Боюр Р.В., Бабошина Е.С., Кутузов А.И., Соколова Т.А., Денисова О.П. Опыт построения системы высшего образования онлайн (проект Росдистант). Основные институциональные изменения и эффекты. Ч. 2 // Стандарты и качество. 2021. № 12. С. 82–87. DOI: 10.35400/0038-9692-2021-12-82-87Krishtal, M.M., Boyur, R.V., Baboshina, E.S., Kutuzov, A.I., Sokolova, T.A., Denisova, O.P. (2021). Experience of building an online higher education system (Rosdistant project). The main institutional changes and effects. P. 2. Standarty i Kachestvo = Standards and Quality. No. 12, pp. 82-87, doi: 10.35400/0038-9692-2021-12-82-87 (In Russ., abstract in Eng.).Zefirova T., Loukachevitch N. Expression of irony and sarcasm on Twitter // Proc. of Third Workshop “Computational linguistics and language science”. 2019. Vol. 4. P. 45–49. DOI: 10.29007/tpzwZefirova, T., Lukachevich, N. (2019). Expression of irony and sarcasm on Twitter. Proc. of the Third Workshop “Computational linguistics and language science”. Vol. 4, pp. 45-49, doi: 10.29007/tpzw

The authors declare that there are no conflicts of interest present.