A Comparative Analysis of the Sentiments Expressed in Comments Posted under Identical Content across Different Social Media Platforms
Main Article Content
Keywords
ruBERT, Comments Sentiment, Russian-Language Comments, Social Media Platforms, Painting and Handcraft Bloggers
Abstract
Currently, there is a wide range of platforms available for video content and communication. Bloggers attract different audiences on various platforms, who view the content and provide feedback in the form of comments. The accessibility of similar tools, particularly the ability to post short videos, has encouraged bloggers to share the same content across multiple platforms. This has allowed for a comparative analysis of the sentiments expressed in comments on identical content posted on different platforms.
For the purpose of this study, short videos by Russian-language bloggers producing content related to art were chosen for analysis. This field was selected as it is relatively neutral in terms of social and political perspectives. The ruBERT model was employed to classify comments based on their emotional tone. The findings support the hypothesis that the comments’ sentiment under identical videos related to art, posted on various platforms, differs.
References
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Wu L, Morstatter F, Liu H. 2018. ‘SlangSD: building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification’. Language Resources and Evaluation. 52, 839-52. https://doi.org/10.1007/s10579-018-9416-0
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Zaheri, S; Leath, J; Stroud, D. 2020. ‘Toxic comment classification’. SMU Data Science Review, 3(1), 13.
Alshamrani, S; Abuhamad, M; Abusnaina, A; Mohaisen, D. 2020. ‘Investigating Online Toxicity in Users Interactions with the Mainstream Media Channels on YouTube’. The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN’20), pp 1–6.
Alrumaih, A; Al-Sabbagh, A; Alsabah, R; Kharrufa, H; & Baldwin, J. 2020. ‘Sentiment analysis of comments in social media’. International Journal of Electrical & Computer Engineering (2088-8708), 10(6). DOI: 10.11591/ijece.v10i6.pp5917-5922
Aly, ES; van der Haar, DT. 2020. ‘Slang-based text sentiment analysis in Instagram’. Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2, pp 321-329. https://doi.org/10.1007/978-981-32-9343-4_25
Aral, S; Walker, D. 2012. ‘Identifying influential and susceptible members of social networks’. Science, 337(6092): 337-341. DOI: 10.1126/science.1215842
Ayvaz S; Shiha MO. 2017. ‘The Effects of Emoji in Sentiment Analysis’. International Journal of Computer and Electrical Engineering. International Academy Publishing (IAP), 9(1): 360-369. doi: 10.17706/ijcee.2017.9.1.360-369
Balcıoğlu, YS. 2024. ‘Examining Turkish Youth's Reaction to Instagram Influencers: A Sentiment Analysis’. İletişim ve Diplomasi, (12): 59-73. https://doi.org/10.54722/iletisimvediplomasi.1489078
Chen, CP. 2013. ‘Exploring personal branding on YouTube’. Journal of internet commerce, 12(4): 332-347. https://doi.org/10.1080/15332861.2013.859041
Cheng, Z; Li, Y. 2024. ‘Like, comment, and share on TikTok: Exploring the effect of sentiment and second-person view on the user engagement with TikTok news videos’. Social Science Computer Review, 42(1): 201-223. https://doi.org/10.1177/08944393231178603
Devlin, J; Chang, M-W; Lee, K; Toutanova, K. 2019. ‘BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding’. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 4171–4186. https://doi.org/10.48550/arXiv.1810.04805
Di Marco, N; Loru, E; Bonetti, A; Serra, AOG; Cinelli, M; Quattrociocchi, W. 2024. ‘The Evolution of Language in Social Media Comments’. arXiv preprint arXiv:2406.11450. https://arxiv.org/abs/2406.11450
Eyipınar, CD; Büyükkalkan, F; Semiz, K. 2021. ‘Sentiment Analysis of Youtube Videos Comments on Sports Nutrition’. International Journal of Physical Education Sport and Technologies 2 (2): 27-39.
Fang, Z; Zhang, Q; Tang, X; Wang, A; Baron, C. 2020. ‘An implicit opinion analysis model based on feature-based implicit opinion patterns’. Artificial Intelligence Review, 53, pp 4547-4574. https://doi.org/10.1007/s10462-019-09801-9
Felix, R; Rauschnabel, PA; Hinsch, C. 2017. ‘Elements of strategic social media marketing: A holistic framework’. Journal of business research, 70, 118-126. https://doi.org/10.1016/j.jbusres.2016.05.001
Jothi, SA; Pandeeswari, N. 2023. ‘Enriched Glove of Word Embedding and BERT of Contextual Embedding in Sarcasm Detection with AdaGrad Optimization Technique’. In 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp 1535-1539. IEEE. doi: 10.1109/ICIMIA60377.2023.10426427
Li, D; Rzepka, R; Ptaszynski, M; Araki, K. 2019. ‘A Novel Machine Learning-based Sentiment Analysis Method for Chinese Social Media Considering Chinese Slang Lexicon and Emoticons’. The AAAI-19 Workshop nn Affective Content Analysis AFFCON2019: Modeling Affect-In-Action, 2328.
Kim, JW; Guess, A; Nyhan, B; Reifler, J. 2021. ‘The distorting prism of social media: How self-selection and exposure to incivility fuel online comment toxicity’. Journal of Communication, 71(6), 922-946. https://doi.org/10.1093/joc/jqab034
King, CM; McCashin, D. 2022. ‘Commenting and connecting: A thematic analysis of responses to YouTube vlogs about borderline personality disorder’. Internet interventions, 28, 100540. https://doi.org/10.1016/j.invent.2022.100540
Kotler, P; Levy, SJ. 1969. ‘Broadening the concept of marketing’. Journal of marketing, 33(1), 10-15. https://doi.org/10.2307/1248482
Kuratov, Y; Arkhipov, M. 2019. ‘Adaptation of deep bidirectional multilingual transformers for Russian language’. Computational Linguistics and Intellectual Technologies, 18 (25): 333-339.
McAlexander, JH; Schouten, JW; Koenig, HF. 2002. ‘Building brand community’. Journal of marketing, 66(1): 38-54. https://doi.org/10.1509/jmkg.66.1.38.18451
Miller, C; Cohen, S; Klug, D; Vasilescu, B; KaUstner, C. 2022. ‘"Did you miss my comment or what?" understanding toxicity in open source discussions’. Proceedings of the 44th International Conference on Software Engineering, pp 710-722.
Muniz AM; Schau, HJ. 2005. ‘Religiosity in the abandoned Apple Newton brand community’. Journal of consumer research, 31(4): 737-747. https://doi.org/10.1086/426607
Nie, RX; Tian, ZP; Wang, JQ; Chin, KS. 2020. ‘Hotel selection driven by online textual reviews: Applying a semantic partitioned sentiment dictionary and evidence theory’. International Journal of Hospitality Management, 88, 102495. https://doi.org/10.1016/j.ijhm.2020.102495
Obadimu, A; Mead, E; Hussain, MN; Agarwal, N. 2019. ‘Identifying toxicity within Youtube video comment’. Social, Cultural, and Behavioral Modeling: 12th International Conference, SBP-BRiMS 2019, Washington, DC, USA, July 9–12, 2019, Proceedings 12, 214-223). Springer International Publishing. https://doi.org/10.1007/978-3-030-21741-9_22
Păvăloaia, VD; Teodor, EM; Fotache, D; Danileţ, M. 2019. ‘Opinion mining on social media data: sentiment analysis of user preferences’. Sustainability, 11(16): 4459. https://doi.org/10.3390/su11164459
Poecze, F; Ebster, C; & Strauss, C. 2022. ‘Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types’. Personal and Ubiquitous Computing, 26(3): 901-910. https://doi.org/10.1007/s00779-019-01361-7
Safko, L.; Brake, D. 2009. The Social Media Bible: Tactics, Tools, and Strategies for Business Success. John Wiley & Sons: Hoboken, New Jersey, USA.
Smetanin, S; Komarov, M. 2021. ‘Share of toxic comments among different topics: the case of Russian social networks’. In 2021 IEEE 23rd Conference on Business Informatics (CBI), 2, pp 65-70. IEEE. doi: 10.1109/CBI52690.2021.10056
Sonare, B; Dewan, JH; Thepade, SD; Dadape, V; Gadge, T; Gavali, A. 2023. ‘Detecting Sarcasm in Reddit Comments: A Comparative Analysis’. 2023 4th International Conference for Emerging Technology (INCET), pp 1-6. IEEE. doi: 10.1109/INCET57972.2023.10170613
Svetlov, K; Platonov, K. 2019. ‘Sentiment analysis of posts and comments in the accounts of Russian politicians on the social network’. 2019 25th Conference of Open Innovations Association (FRUCT), 299-305. IEEE. doi: 10.1109/INCET57972.2023.10170613
Sulaiman, A; Feizollah, A; Mostafa, M. M; & Zakaria, Z. 2023. ‘Profiling the halal food consumer on Instagram: integrating image, textual, and social tagging data’. Multimedia Tools and Applications, 82(7): 10867-10886. https://doi.org/10.1007/s11042-022-13685-3
Wu L, Morstatter F, Liu H. 2018. ‘SlangSD: building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification’. Language Resources and Evaluation. 52, 839-52. https://doi.org/10.1007/s10579-018-9416-0
Xiang, Z; Du, Q; Ma, Y; Fan, W. 2017. ‘A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism’. Tourism Management, 58, 51-65. https://doi.org/10.1016/j.tourman.2016.10.001
Yarushkina, NG; Moshkin, VS; Konstantinov, AA. 2020. ‘Word2Vec and BERT language models used for a sentiment analysis of text posts in social network’. Automation of Control Processes. 61(3), 60–69. {In Russian} doi: 10.35752/1991-2927-2020-3-61-60-69
Zhan, M; Tu, R; Yu, Q. 2018. ‘Understanding readers: Conducting sentiment analysis of Instagram captions’. Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, pp 33-40. https://doi.org/10.1145/3297156.3297270
Zaheri, S; Leath, J; Stroud, D. 2020. ‘Toxic comment classification’. SMU Data Science Review, 3(1), 13.
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2025-04-14
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Gadasina, L., & Sotnichenko, A. (2025). A Comparative Analysis of the Sentiments Expressed in Comments Posted under Identical Content across Different Social Media Platforms. Journal of Telecommunications and the Digital Economy, 13(1), 138-159. https://doi.org/10.18080/jtde.v13n1.1140
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Special Issue: Driving the Digital Economy
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How to Cite
Gadasina, L., & Sotnichenko, A. (2025). A Comparative Analysis of the Sentiments Expressed in Comments Posted under Identical Content across Different Social Media Platforms. Journal of Telecommunications and the Digital Economy, 13(1), 138-159. https://doi.org/10.18080/jtde.v13n1.1140
