Twitter interaction between audiences and influencers. Sentiment, polarity, and communicative behaviour analysis methodology

Autores/as

DOI:

https://doi.org/10.3145/epi.2022.nov.18

Palabras clave:

Twitter, Audiences, Influencers, Sentiment analysis, Parasocial interaction, Polarity, Emotions, Communication, Social networks, Social media, Hashtags, Theoretical advance, Methodology

Resumen

Twitter is one of several social networks with the highest numbers of users in Spain. In spite of this, how are communicative relationships developed in the digital environment among influencers who have emerged on the Internet? These personalities have a stronger influence on children and young people than traditional celebrities. The aim of this work is to study the communicative interaction generated on the profiles of Spanish influencers with the most followers on Twitter, based on the number of content items generated and the responses they receive from users. The polarity and sentiment conveyed by these communications have also been analysed. By processing publications in real time using machine learning based on sentiment analysis, 48,878 tweets and retweets from five influencers were studied over a period of 40 days. The results show that the publications reached nearly 200 million followers, and despite being fourth in terms of the number of followers, @IbaiLlanos is the influencer who leads the conversations on Twitter with the highest number of tweets, retweets, and audience share. Among the most popular topics, sporting events stand out. This study has also confirmed that the most frequently stated emotion is surprise, and that positive messages prevail over those that are negative and neutral with regard to polarity. Nevertheless, the linear regression data has verified that the main trend is toward publishing negative messages, with a lower statistical correlation, which is a behaviour that might possibly be duplicated on other social networks.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Ahmad, Munir; Aftab, Shahib (2017). "Analyzing the performance of SVM for polarity detection with different datasets". International journal of modern education & computer science, v. 9, n. 10, pp. 29-36. https://doi.org/10.5815/ijmecs.2017.10.04

Ali, Mubashir; Baqir, Amees; Psaila, Giuseppe; Malik, Sayyam (2020). "Towards the discovery of influencers to follow in micro-blogs (Twitter) by detecting topics in posted messages (tweets)". Applied sciences, v. 10, n.16, 5715. https://doi.org/10.3390/app10165715

Alp, Zeynep-Zengin; í–ÄŸí¼dí¼cí¼, Åžule-Gí¼ndí¼z (2018). "Identifying topical influencers on Twitter based on user behavior and network topology". Knowledge based systems, v. 141, pp. 211-221. https://doi.org/10.1016/j.knosys.2017.11.021

Anderson, Monica; Jiang, Jinging (2018). "Teens, social media & technology 2018". Pew Research Center, v. 31, pp. 1673-1689. https://pewrsr.ch/3clGcWZ

Arce-Garcí­a, Sergio; Orviz-Martí­nez, Natalia; Cuervo-Carabel, Tatiana (2020). "Impact of emotions expressed by digital newspapers on Twitter". Profesional de la información, v. 29, n. 5, e290520. https://doi.org/10.3145/epi.2020.sep.20

Ariza-Martí­n, Pablo (2021). "Ibai colapsa Twitch con un millón y medio de espectadores en directo para ver su velada de boxeo". El correo, 27 mayo. https://bit.ly/3ffMCWa

Autocontrol (2020). Código de conducta sobre el uso de influencers en la publicidad. https://bit.ly/3owWSP6

Bae, Youngge; Lee, Hongchul (2012). "Sentiment analysis of Twitter audiences: Measuring the positive or negative influence of popular twitterers". Journal of the American Society for Information Science and Technology, v. 63, n. 12, pp. 2521-2535. https://doi.org/10.1002/asi.22768

Berger, Jonah; Milkman, Katherine (2012). "What makes online content viral?". American Marketing Association, v. 49, n. 2, pp. 192-205. https://doi.org/10.1509/jmr.10.0353

Berne-Manero, Carmen; Marzo-Navarro, Mercedes (2020). "Exploring how influencer and relationship marketing serve corporate sustainability". Sustainability, v. 12, n. 11, 4392. https://doi.org/10.3390/su12114392

Bharti, Santosh-Kumar; Naidu, Reddy; Babu, Korra-Santhya (2017). "Hyperbolic feature-based sarcasm detection in tweets: a machine learning approach". In: 2017 14th IEEE India Council international conference (Indicon). https://doi.org/10.1109/INDICON.2017.8487712

Bond, Bradley (2016). "Following your "˜friend´: Social media and the strength of adolescents´ parasocial relationships with media personae". Cyberpsychology, behavior, and social networking, v. 19, n. 11, pp. 656-660. https://doi.org/10.1089/cyber.2016.0355

Bossen, Christina-Bucknell; Kottasz, Rita (2020). "Uses and gratifications sought by pre-adolescent and adolescent TikTok consumers". Young consumers, v. 21, n. 4, pp. 1747-3616. https://doi.org/10.1108/YC-07-2020-1186

Campbell, Colin; Farrell, Justine-Rapp (2020). "More than meets the eye: the functional components underlying influencer marketing". Business horizons, v. 63, n. 4, pp. 469-479. https://doi.org/10.1016/j.bushor.2020.03.003

Cardoso, Alejandra; Talame, Lorena; Amor, Matí­as; Neil, Carlos (2019). "Minerí­a de opiniones: análisis de sentimientos en una red social". En: XXI Workshop de investigadores en ciencias de la computación. http://sedici.unlp.edu.ar/handle/10915/77379

Casaló, Luis; Flavián, Carlos; Ibáñez-Sánchez, Sergio (2020). "Influencers on Instagram: Antecedents and consequences of opinion leadership". Journal of business research, v. 117, pp. 510-519. https://doi.org/10.1016/j.jbusres.2018.07.005

Chang, Wei-Lun (2019). "The impact of emotion: A blended model to estimate influence on social media". Information systems frontiers, v. 21, n. 5, pp. 1137-1151. https://doi.org/10.1007/s10796-018-9824-0

Cho, Jin-Hee (2018). "Dynamics of uncertain and conflicting opinions in social networks". IEEE transactions on computational social systems, v. 5, n. 2, pp. 518-531. https://doi.org/10.1109/TCSS.2018.2826532

Court, Eduardo; Rengifo, Erick-Williams (2011). Estadí­sticas y econometrí­a financiera. Buenos Aires: Cengage Learning. ISBN: 978 98 714864 8 9

Dibble, Jayson; Hartmann, Tilo; Rosaen, Sarah (2016). "Parasocial interaction and parasocial relationship: conceptual clarification and a critical assessment of measures". Human communication research, v. 42, n. 1, pp. 21-44. https://doi.org/10.1111/hcre.12063

Drescher, Christian; Wallner, Guenter; Kriglstein, Simone; Sifa, Rafet; Drachen, Anders; Pohl, Margit (2018). "What moves players? Visual data exploration of Twitter and gameplay data". In: Proceedings of the 2018 CHI conference on human factors in computing systems. https://doi.org/10.1145/3173574.3174134

Dridi, Amna; Recupero, Diego-Reforgiato (2019). "Leveraging semantics for sentiment polarity detection in social media". International journal of machine learning and cybernetics, v. 10, n. 8, pp. 2045-2055. https://doi.org/10.1007/s13042-017-0727-z

Edelmann, Noella (2017). "Lurking in online participation and e-participation". In: 2017 Fourth international conference on eDemocracy & eGovernment (Icedeg), pp. 282-284. http://doi.org/10.1109/ICEDEG.2017.7962552

Fernández-Muñoz, Cristóbal; Garcí­a-Guardia, Marí­a-Luisa (2016). "Las principales celebrities en Twitter: análisis de su comunicación e influencia en la red social". Comunicaí§í£o, mí­dia e consumo, v. 13, n. 38, pp. 116-129. https://doi.org/10.18568/cmc.v13i38.1285

Fernández-Prados, Juan; Lozano-Dí­az, Antonia; Cuenca-Piqueras, Cristina; González-Moreno, Marí­a-José (2021). "Analysis of teenage cyberactivists on Twitter and Instagram around the world". In: 2021 9th International conference on information and education technology (Iciet), pp. 476-479. https://ieeexplore.ieee.org/abstract/document/9419619

Garcia, Klaifer; Berton, Lilian (2021). "Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA". Applied soft computing, v. 101, 107057. https://doi.org/10.1016/j.asoc.2020.107057

Gopi, Arepalli-Peda; Jyothi, Naga-Sravana; Narayana, Laksman; Sandeep, Satya (2020). "Classification of tweets data based on polarity using improved RBF kernel of SVM". International journal of information technology. https://doi.org/10.1007/s41870-019-00409-4

Grí¤ve, Jan-Frederik (2017). "Exploring the perception of influencers vs. traditional celebrities: are social media stars a new type of endorser?". In: Proceedings of the 8th international conference on social media & society. https://doi.org/10.1145/3097286.3097322

Hasan, Ali; Moin, Sana; Karim, Ahmad; Shamshirband, Shahaboddin (2018). "Machine learning-based sentiment analysis for Twitter accounts". Mathematical and computational applications, v. 23, n. 1, 11. https://doi.org/10.3390/mca23010011

Hernández-Ruiz, Alejandra; Gutiérrez, Yoan (2021). "Analysing the Twitter accounts of licensed sports gambling operators in Spain: a space for responsible gambling?". Communication & society, v. 34, n. 4, pp. 65-79. https://doi.org/10.15581/003.34.4.65-79

Horton, Donald; Wohl, Richard (1956). "Mass communication and para-social interaction". Psychiatry. Journal for the study of interpersonal processes, v. 19, n. 3, pp. 215-229.

Hwang, Kumju; Zhang, Qi (2018). "Influence of parasocial relationship between digital celebrities and their followers on followers´ purchase and electronic word-of-mouth intentions, and persuasion knowledge". Computers in human behavior, v. 87, pp. 155-173. https://doi.org/10.1016/j.chb.2018.05.029

IAB Spain (2022). Estudio de redes sociales 2022. https://iabspain.es/estudio/estudio-de-redes-sociales-2022

Interactivadigital.com (2021). "La velada del año, de Ibai Llanos, hace historia". Interactiva, 28 mayo. https://bit.ly/3wuhmKa

Ishtiaq, Munazza (2015). "Sentiment analysis of Twitter data using sentiment influencers". Journal of intelligent computing, v. 6, n. 1, pp. 17-24. https://bit.ly/3AfNSQu

Jain, Somya; Sinha, Adwitiya (2020). "Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19". Chaos, solitons & fractals, v. 139, 110037. https://doi.org/10.1016/j.chaos.2020.110037

Jiménez-Castillo, David; Sánchez-Fernández, Raquel (2019). "The role of digital influencers in brand recommendation: examining their impact on engagement, expected value and purchase intention". International journal of information management, v. 49, pp. 366-376. https://doi.org/10.1016/j.ijinfomgt.2019.07.009

Judd, Charles; McClelland, Gary; Ryan, Carey (2017). Data analysis: a model comparison approach to regression, anova, and beyond. London: Routledge. ISBN: 978 1 3 157441 3 1 https://doi.org/10.4324/9781315744131

Khajeheian, Datis; Kolli, Shaghayegh (2020). "Digital games get viral on social media: a social network analysis of Pokémon Go on Twitter". International journal of web based communities, v. 16, n. 3, pp. 262-278. https://doi.org/10.1504/IJWBC.2020.108632

Ki, Chung-Wha; Kim, Youn-Kyung (2019). "The mechanism by which social media influencers persuade consumers: the role of consumers´ desire to mimic". Psychology & marketing, v. 36, n. 10, pp. 905-922. https://doi.org/10.1002/mar.21244

Kim, Jihyun; Kim, Jinyoung; Collins, Chad (2021). "First impressions in 280 characters or less: sharing life on Twitter and the mediating role of social presence". Telematics and informatics, v. 61, 101596. https://doi.org/10.1016/j.tele.2021.101596

Kim, Jihyun; Song, Hayeon (2016). "Celebrity´s self-disclosure on Twitter and parasocial relationships: a mediating role of social presence". Computers in human behavior, v. 62, pp. 570-577. https://doi.org/10.1016/j.chb.2016.03.083

Kowalczyk, Christine; Pounders, Kathrynn (2016). "Transforming celebrities through social media: the role of authenticity and emotional attachment". Journal of product & brand management, v. 24, n. 4, pp. 345-356. https://doi.org/10.1108/JPBM-09-2015-0969

Krause, Amanda; North, Adrian; Heritage, Brody (2018). "Musician interaction via social networking sites: celebrity attitudes, attachment, and their correlates". Music & science, v. 1. https://doi.org/10.1177/2059204318762923

Kreissl, Julian; Possler, Daniel; Klimmt, Christoph (2021). "Engagement with the gurus of gaming culture: parasocial relationships to let´s players". Games and culture, v. 16, n. 8, pp. 1021-1043. https://doi.org/10.1177/15554120211005241

Lahuerta-Otero, Eva; Cordero-Gutiérrez, Rebeca (2016). "Looking for the perfect tweet. The use of data mining techniques to find influencers on Twitter". Computers in human behavior, v. 64, pp. 575-583. https://doi.org/10.1016/j.chb.2016.07.035

Liu, Bing (2017). "Many facets of sentiment analysis". In: Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio. A practical guide to sentiment analysis. Cham, Switzerland: Springer, pp. 11-40. ISBN: 978 3 319 55394 8

Loria, Enrica; Pirker, Johanna; Drachen, Aanders; Marconi, Annapaola (2020). "Do influencers influence? - Analyzing players´ activity in an online multiplayer game". In: 2020 IEEE conference on games (CoG), pp. 120-127. https://bit.ly/3BhFKAy

Lou, Chen; Kim, Hye-Kyung (2019). "Fancying the new rich and famous? Explicating the roles of influencer content, credibility, and parental mediation in adolescents´ parasocial relationship, materialism, and purchase intentions". Frontiers in psychology, v. 10, 2567. https://doi.org/10.3389/fpsyg.2019.02567

Lowe-Calverley, Emily; Grieve, Rachel (2021). Do the metrics matter? An experimental investigation of Instagram influencer effects on mood and body dissatisfaction. Body image, v. 36. https://doi.org/10.1016/j.bodyim.2020.10.003

Mueller, Juergen; Stumme, Gerd (2017). "Predicting rising follower counts on Twitter using profile information". In: Proceedings of the 2017 ACM on web science conference, pp. 121-130. https://doi.org/10.1145/3091478.3091490

Nemes, Lázsló; Kiss, Attila (2021). "Social media sentiment analysis based on COVID-19". Journal of information and telecommunication, v. 5, n. 1. https://doi.org/10.1080/24751839.2020.1790793

Nesi, Jacqueline; Choukas-Bradley, Sophia; Prinstein, Mitchell (2018). "Transformation of adolescent peer relations in the social media context: Part 1 - A theoretical framework and application to dyadic peer relationships". Clinical child and family psychology review, v. 21, n. 3, pp. 267-294. https://doi.org/10.1007/s10567-018-0261-x

Riquelme, Fabián; González-Cantergiani, Pablo (2016). "Measuring user influence on Twitter: a survey". Information processing & management, v. 52, n. 5, pp. 949-975. https://doi.org/10.1016/j.ipm.2016.04.003

Sailunaz, Kasfha; Alhajj, Reda (2019). "Emotion and sentiment analysis from Twitter text". Journal of computational science, v. 36, 101003. https://doi.org/10.1016/j.jocs.2019.05.009

Sánchez-Rada, Juan-Fernando; Iglesias, Carlos-Ángel (2019). "Social context in sentiment analysis: formal definition, overview of current trends and framework for comparison". Information fusion, v. 52, pp. 344-356. https://doi.org/10.1016/j.inffus.2019.05.003

Santamarí­a-de-la-Piedra, Elena; Meana-Peón, Rufino (2017). "Redes sociales y fenómeno influencer. Reflexiones desde una perspectiva psicológica". Miscelánea Comillas. Revista de ciencias humanas y sociales, v. 75, n. 147, pp. 443-469. https://revistas.comillas.edu/index.php/miscelaneacomillas/article/view/8433

Scolari, Carlos A. (2008). Hipermediaciones: elementos para una teorí­a de la comunicación digital interactiva. Barcelona: Editorial Gedisa. ISBN: 978 84 9784 410 9

Social Media Family (2021). VII Estudio sobre los usuarios de Facebook, Twitter, Instagram y LinkedIn en España. https://bit.ly/2Wpbfco

Stever, Gayle; Lawson, Kevin (2013). "Twitter as a way for celebrities to communicate with fans: implications for the study of parasocial interaction". North American journal of psychology, v. 15, n. 2, pp. 339-355. https://bit.ly/3BfDqKi

Suárez-Álvarez, Rebeca; Garcí­a-Jiménez, Antonio (2021). "Centennials en TikTok: tipologí­a de ví­deos. Análisis y comparativa España-Gran Bretaña por género, edad y nacionalidad". Revista latina de comunicación social, v. 79. https://www.doi.org/10.4185/RLCS-2021-1503

Tauhid, Syafi-Muhammad; Ruldeviyani, Yova (2020). "Sentiment analysis of Indonesians response to influencer in social media". In: 2020 7th International conference on information technology, computer, and electrical engineering (Icitacee), pp. 90-95. https://doi.org/10.1109/ICITACEE50144.2020.9239218

Vizcaí­no-Verdú, Arantxa; Aguaded, Ignacio (2020). "Análisis de sentimiento en Instagram: polaridad y subjetividad de cuentas infantiles". ZER: revista de estudios de comunicación, v. 25, n. 48, pp. 213-229. https://doi.org/10.1387/zer.21454

Wallner, Gí¼nter; Kriglstein, Simone; Drachen, Anders (2019). "Tweeting your destiny: profiling users in the Twitter landscape around an online game". In: 2019 IEEE conference on games (CoG). https://doi.org/10.1109/CIG.2019.8848079

Yuste, Bárbara (2015). "Las nuevas formas de consumir información de los jóvenes". Revista de estudios de juventud, n. 108, pp. 179-191. http://goo.gl/eqg9UF

Zarei, Koosha; Ibosiola, Damilola; Farahbakhsh, Reza; Gilani, Zafar; Garimella, Kiran; Crespi, Ní¶el; Tyson, Gareth (2020). "Characterising and detecting sponsored influencer posts on Instagram". In: 2020 IEEE/ACM international conference on advances in social networks analysis and mining (Asonam), pp. 327-331. https://doi.org/10.1109/ASONAM49781.2020.9381309

Publicado

2022-11-30

Cómo citar

Catalina-Garcí­a, B., & Suárez-Álvarez, R. (2022). Twitter interaction between audiences and influencers. Sentiment, polarity, and communicative behaviour analysis methodology . Profesional De La información Information Professional, 31(6). https://doi.org/10.3145/epi.2022.nov.18

Número

Sección

Artí­culos de investigación / Research articles