Palavras-chave
YouTube
conteúdo de vídeo
engajamento digital
modelagem de tópicos
Como Citar
Resumo
Objetivo – O estudo investiga quais são os conteúdos mais populares que os influenciadores de mídia social discutem no YouTube, analisando a valência associada, delineando as categorias de conteúdo favorecidas pelos principais influenciadores brasileiros no YouTube e avaliando seu impacto no engajamento digital do consumidor.
Referencial Teórico – Baseia-se na literatura de marketing de influência, influenciadores de mídia social e engajamento digital do consumidor.
Metodologia – Por meio da abordagem de mineração de dados, o método abrange a coleta de características de postagens de vídeos, métricas de engajamento e transcrições de áudio de 34.563 vídeos em 103 canais do YouTube. Após o pré-processamento textual, conduz-se a modelagem de tópicos usando o algoritmo de alocação latente de Dirichlet (LDA) e análise de sentimento dos vídeos.
Resultados – Identificou-se 19 dimensões críticas de conteúdo de vídeo no YouTube. As 3 categorias de conteúdo com maior engajamento digital do usuário são: 'Família', 'Entretenimento/geral' e 'Cultura e entretenimento'. A análise de sentimento mostra que conteúdos relacionados a 'Beleza', 'Gastronomia' e 'Economia, Empreendedorismo e Negócios' têm maior valência positiva. 'Política, Economia e Notícias', 'Entretenimento/geral' e 'Jogos' contêm altas porcentagens de valência negativa.
Implicações Práticas e Sociais da Pesquisa – Os resultados fornecem uma compreensão profunda do conteúdo mais popular do YouTube e das taxas de engajamento digital. É essencial para empresas e influenciadores digitais que buscam maximizar seu alcance, ressoar com seu público-alvo e permanecer competitivos no cenário digital. Permite uma comunicação, criação de conteúdo e tomada de decisões estratégicas mais eficazes.
Contribuições – Entender as dinâmicas do conteúdo no YouTube pode fornecer insights valiosos para empresas, profissionais de marketing e criadores de conteúdo visando otimizar o posicionamento e suas estratégias de comunicação.
Referências
Agência Brasil (2024). “Pesquisa aponta pulverização no mercado de influenciadores”. Retrieved from: https://agenciabrasil.ebc.com.br/geral/noticia/2024-06/pesquisa-aponta-pulverizacao-no-mercado-de-influenciadores-digitais#. Accessed in June, 2024.
Aggrawal, N., Arora, A., Anand, A., & Irshad, M. S. (2018). View-count based modeling for YouTube videos and weighted criteria–based ranking. Advanced mathematical techniques in engineering sciences, 149-160.
Aleti, T., Pallant, J. I., Tuan, A., & van Laer, T. (2019). Tweeting with the stars: Automated text analysis of the effect of celebrity social media communications on consumer word of mouth. Journal of Interactive Marketing, 48, 17-32.
Almeida, Rafael J. A. (2018). Leia-léxico para inferência adaptada. Retrieved from: https://github.com/rafjaa/LeIA. Accessed June 2024.
Ata, S., Arslan, H. M., Baydaş, A., & Pazvant, E. (2022). The effect of social media influencers’ credibility on consumer’s purchase intentions through attitude toward advertisement. ESIC Market, 53(1), e280-e280.
Balakrishnan, V., & Lloyd-Yemoh, E. (2014). Stemming and lemmatization: A comparison of retrieval performances. Lecture Notes on Software Engineering, 2(3), 174-179.
Berger, J., Moe, W. W., & Schweidel, D. A. (2023). What Holds Attention? Linguistic Drivers of Engagement. Journal of Marketing, 87(5), 793-809.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning research, 3(Jan), 993-1022.
Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science, 35(6), 953-975.
Cano-Marin, E., Ribeiro-Soriano, D., Mardani, A., & Gonzalez-Tejero, C. B. (2023). Exploring the challenges of the COVID-19 vaccine supply chain using social media analytics: a global perspective. Sustainable Technology and Entrepreneurship, 2(3), 100047.
Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2020). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510-519.
Chen, M. J. (2020). Examining the influence of emotional expressions in online consumer reviews on perceived helpfulness. Information Processing & Management, 57(6), 1–15.
Chen, L., Yan, Y., & Smith, A. N. (2023). What drives digital engagement with sponsored videos? An investigation of video influencers’ authenticity management strategies. Journal of the Academy of Marketing Science, 51(1), 198-221.
Cheng, Y., Xie, Y., Zhang, K., Agrawal, A., & Choudhary, A. (2012). How online content is received by users in social media: A case study on Facebook.com posts. In 2nd Social Media Analytics Workshop, Beijing, China.
Chung, S., & Cho, H. (2017). Fostering Parasocial Relationships with Celebrities on social media: Implications for Celebrity Endorsement. Psychology & Marketing, 34(4), 481–495.
Daniel, C., & Dutta, K. (2018). Automated generation of latent topics on emerging technologies from YouTube Video content. In Proceedings of the 51st Hawaii International Conference on System Sciences, 1762-1770.
Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1), 7.
Digital Marketing Institute. (2024). 20 Surprising Influencer Marketing Statistics. Retrieved from: https://digitalmarketinginstitute.com/blog/20-influencer-marketing-statistics-that-will-surprise-you. Accessed in June 2024.
Feng, J., Mu, X., Wang, W., & Xu, Y. (2021). A topic analysis method based on a three-dimensional strategic diagram. Journal of Information Science, 47(6), 770-782.
Gavilanes, J. M., Flatten, T. C., & Brettel, M. (2018). Content strategies for digital consumer engagement in social networks: Why advertising is an antecedent of engagement. Journal of Advertising, 47(1), 4-23.
Google (2019). Insight Strategy Group, Global, “Premium Is Personal” studies, AU, BR, CA, DE, IN, JP, KR, U.K., U.S. In: What the world watched in a day. Retrieved from https://www.thinkwithgoogle.com/feature/youtube-video-data-watching-habits/. Accessed in June 2024.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tourism Management, 59, 467-483.
Hughes, C., Swaminathan, V., & Brooks, G. (2019). Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. Journal of Marketing, 83(5), 78-96.
Jacobson, J., Hodson, J., & Mittelman, R. (2022). Pup-ularity contest: The advertising practices of popular animal influencers on Instagram. Technological Forecasting and Social Change, 174, 121226.
Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: evidence from Facebook. Management Science, 64(11), 5105-5131.
Leung, F. F., Gu, F. F., Li, Y., Zhang, J. Z., & Palmatier, R. W. (2022). Influencer marketing effectiveness. Journal of marketing, 86(6), 93-115.
Li, X., Shi, M., & Wang, X. S. (2019). Video mining: Measuring visual information using automatic methods. International Journal of Research in Marketing, 36(2), 216-231.
Liu, X., Burns, A. C., & Hou, Y. (2017). An investigation of brand-related user-generated content on Twitter. Journal of Advertising, 46(2), 236-247.
Pezzuti, T., Leonhardt, J. M., & Warren, C. (2021). Certainty in Language Increases Consumer Engagement on Social Media. Journal of Interactive Marketing, 53, 32-46.
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.
Munaro, A. C., Barcelos, R. H., Francisco-Maffezzolli, E. C. F., Rodrigues, J. P. S., & Paraiso, E. C. (2021). To engage or not engage? The features of video content on YouTube affecting digital consumer engagement. Journal of Consumer Behaviour, 20(5), 1336-1352.
Munaro, A. C., Barcelos, R. H., Francisco-Maffezzolli, E. C., Rodrigues, J. P. S., & Paraiso, E. C. (2024). Does your style engage? Linguistic styles of influencers and digital consumer engagement on YouTube. Computers in Human Behavior, 156, 108217.
Nyagadza, B., Mazuruse, G., Simango, K., Chikazhe, L., Tsokota, T., & Macheka, L. (2023). Examining the influence of social media eWOM on consumers’ purchase intentions of commercialised indigenous fruits (IFs) products in FMCGs retailers. Sustainable Technology and Entrepreneurship, 2(3), 100040.
Nunes, R. H., Ferreira, J. B., Freitas, A. S. D., & Ramos, F. L. (2018). The effects of social media opinion leaders’ recommendations on followers’ intention to buy. Revista Brasileira de Gestão de Negócios, 20, 57-73.
Patel, P. C., Parida, V., & Tran, P. K. (2022). Perceived risk and the need for trust as drivers of improved surgical skills in 3D surgical video technology. Journal of Innovation & Knowledge, 7(4), 100269.
Patel, P. C., Stenmark, M., Parida, V., & Tran, P. K. (2023). A socio-institutional perspective on the reluctance among the elderly concerning the commercialization of 3D surgical video technology in Sweden. Journal of Innovation & Knowledge, 8(2), 100361.
Plummer, M. (2022). Why Video Plays A Key Role In Today’s Marketing Scene. Forbes. Innovation. Retrieved from: https://www.forbes.com/sites/forbestechcouncil/2022/06/03/why-video-plays-a-key-role-in-todays-marketing-scene/. Accessed in June 2024.
Řehůřek, R., & Sojka, P. (2010). Software framework for topic modeling with large corpora. In Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks. Valletta, Malta, 46-50.
Rodrigues, J. P., & Paraiso, E. (2020). From audio to information: Learning topics from audio transcripts. In Anais do VIII Symposium on Knowledge Discovery, Mining and Learning, 121-128.
Rouhani, S., & Mozaffari, F. (2022). Sentiment analysis researches story narrated by topic modeling approach. Social Sciences & Humanities Open, 6(1), 100309.
Santora, J. (2022). Influencer Marketing Hub. Key Influencer Marketing Statistics You Need to Know for 2022. Retrieved from https://influencermarketinghub.com/influencer-marketing-statistics/amp/. Accessed in May 2024.
Schouten, A. P., Janssen, L., & Verspaget, M. (2020). Celebrity vs. Influencer endorsements in advertising: the role of identification, credibility, and Product-Endorser fit. International Journal of Advertising, 39(2), 258-281.
Shahbaznezhad, H., Dolan, R., & Rashidirad, M. (2020). The Role of Social Media Content Format and Platform in Users' Engagement Behavior. Journal of Interactive Marketing, 53, 47-65.
Sette, G., & Brito, P. Q. (2020). To what extent are digital influencers creative? Creativity and Innovation Management, 29(S1), 90-102.
Tellis, G. J., MacInnis, D. J., Tirunillai, S., & Zhang, Y. (2019). What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence. Journal of Marketing, 83(4), 1-20.
Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. Journal of Marketing Research, 51(4), 463-479.
van Noort, G., Himelboim, I., Martin, J., & Collinger, T. (2020). Introducing a model of automated brand-generated content in an era of computational advertising. Journal of Advertising, 49(4), 411-427.
Voorveld, H. A. (2019). Brand communication in social media: a research agenda. Journal of Advertising, 48(1), 14-26.
Wallach, H. M. (2006). Topic modeling: beyond bag-of-words. In Proceedings of the 23rd international conference on Machine Learning, 977-984.
Yew, J., & Shamma, D. A. (2011). Know your data: Understanding implicit usage versus explicit action in video content classification. In Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems, 7881, 355-362.
Yoon, S. H., & Lee, S. H. (2022). What Likeability Attributes Attract People to Watch Online Video Advertisements?. Electronics, 11(13), 1960.
Zhang, Y., Moe, W. W., & Schweidel, D. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing, 34(1),100-119.
Em caso de aprovação do artigo para publicação, os direitos de copyright são cedidos pelo(s) autor(es) à Revista Brasileira de Gestão de Negócios – RBGN.
Nestes termos, é OBRIGATÓRIO que os autores enviem para RBGN o formulário de Cessão de Direitos Autorias devidamente preenchido e assinado. Conforme o modelo: [Direitosautorais]
As condições da Cessão de Direitos Autorais indicam que a Revista Brasileira de Gestão de Negócios – RBGN possui a título gratuito e em caráter definitivo os direitos autorais patrimoniais dos artigos por ela publicados. Não obstante a Cessão dos Direitos Autorais, a RBGN faculta aos autores o uso desses direitos sem restrições.
Os textos publicados na RBGN são de inteira responsabilidade de seus autores.
A revista adota o padrão de licença CC-BY Creative Commons Attribution 4.0 permitindo redistribuição e reutilização dos artigos sob a condição de que a autoria seja devidamente creditada.