- -

Emotion Dynamics of Public Opinions on Twitter

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Emotion Dynamics of Public Opinions on Twitter

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Naskar;Sngh;Kumar ...
Tamaño: 1.906Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: Emotion Dynamics ...
Tamaño: 5.356Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Naskar, Debashis es_ES
dc.contributor.author Singh, Sanasam Ranbir es_ES
dc.contributor.author Kumar, Durgesh es_ES
dc.contributor.author Nandi, Sukumar es_ES
dc.contributor.author Onaindia De La Rivaherrera, Eva es_ES
dc.date.accessioned 2021-05-14T03:31:32Z
dc.date.available 2021-05-14T03:31:32Z
dc.date.issued 2020-03 es_ES
dc.identifier.issn 1046-8188 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166339
dc.description.abstract [EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies. es_ES
dc.description.sponsorship The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India. es_ES
dc.language Inglés es_ES
dc.publisher Association for Computing Machinery es_ES
dc.relation.ispartof ACM Transactions on Information Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Emotion transition es_ES
dc.subject Influence measure es_ES
dc.subject Opinion discussion es_ES
dc.subject Social agreement es_ES
dc.subject Social dynamics es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Emotion Dynamics of Public Opinions on Twitter es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1145/3379340 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1145/3379340 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 24 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 38 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\404668 es_ES
dc.contributor.funder Indian Institute of Technology Guwahati es_ES
dc.contributor.funder Ministry of Electronics and Information Technology, India es_ES
dc.description.references Ahmed, S., Jaidka, K., & Cho, J. (2016). Tweeting India’s Nirbhaya protest: a study of emotional dynamics in an online social movement. Social Movement Studies, 16(4), 447-465. doi:10.1080/14742837.2016.1192457 es_ES
dc.description.references Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003). Machine Learning, 50(1/2), 5-43. doi:10.1023/a:1020281327116 es_ES
dc.description.references Araujo, T., Neijens, P., & Vliegenthart, R. (2016). Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands. International Journal of Advertising, 36(3), 496-513. doi:10.1080/02650487.2016.1173765 es_ES
dc.description.references Berger, J. (2011). Arousal Increases Social Transmission of Information. Psychological Science, 22(7), 891-893. doi:10.1177/0956797611413294 es_ES
dc.description.references Bi, B., Tian, Y., Sismanis, Y., Balmin, A., & Cho, J. (2014). Scalable topic-specific influence analysis on microblogs. Proceedings of the 7th ACM international conference on Web search and data mining. doi:10.1145/2556195.2556229 es_ES
dc.description.references Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. doi:10.1016/j.jocs.2010.12.007 es_ES
dc.description.references Chen, W., Wang, C., & Wang, Y. (2010). Scalable influence maximization for prevalent viral marketing in large-scale social networks. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’10. doi:10.1145/1835804.1835934 es_ES
dc.description.references Ding, Z., Jia, Y., Zhou, B., Zhang, J., Han, Y., & Yu, C. (2013). An Influence Strength Measurement via Time-Aware Probabilistic Generative Model for Microblogs. Lecture Notes in Computer Science, 372-383. doi:10.1007/978-3-642-37401-2_38 es_ES
dc.description.references Ding, Z., Wang, H., Guo, L., Qiao, F., Cao, J., & Shen, D. (2015). Finding Influential Users and Popular Contents on Twitter. Web Information Systems Engineering – WISE 2015, 267-275. doi:10.1007/978-3-319-26187-4_23 es_ES
dc.description.references Feldman Barrett, L., & Russell, J. A. (1998). Independence and bipolarity in the structure of current affect. Journal of Personality and Social Psychology, 74(4), 967-984. doi:10.1037/0022-3514.74.4.967 es_ES
dc.description.references Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), e0142390. doi:10.1371/journal.pone.0142390 es_ES
dc.description.references Hillmann, R., & Trier, M. (2012). Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. doi:10.1109/asonam.2012.88 es_ES
dc.description.references Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceedings of the 19th international conference on World wide web - WWW ’10. doi:10.1145/1772690.1772751 es_ES
dc.description.references Myers, S. A., Zhu, C., & Leskovec, J. (2012). Information diffusion and external influence in networks. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’12. doi:10.1145/2339530.2339540 es_ES
dc.description.references Nguyen, H. T., Ghosh, P., Mayo, M. L., & Dinh, T. N. (2017). Social Influence Spectrum at Scale. ACM Transactions on Information Systems, 36(2), 1-26. doi:10.1145/3086700 es_ES
dc.description.references Pal, A., & Counts, S. (2011). Identifying topical authorities in microblogs. Proceedings of the fourth ACM international conference on Web search and data mining - WSDM ’11. doi:10.1145/1935826.1935843 es_ES
dc.description.references Peng, S., Wang, G., & Xie, D. (2017). Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges. IEEE Network, 31(1), 11-17. doi:10.1109/mnet.2016.1500104nm es_ES
dc.description.references Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714 es_ES
dc.description.references Shi, J., Hu, P., Lai, K. K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites. Internet Research, 28(2), 393-418. doi:10.1108/intr-01-2017-0038 es_ES
dc.description.references Silva, A., Guimarães, S., Meira, W., & Zaki, M. (2013). ProfileRank. Proceedings of the 7th Workshop on Social Network Mining and Analysis - SNAKDD ’13. doi:10.1145/2501025.2501033 es_ES
dc.description.references Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. Journal of Management Information Systems, 29(4), 217-248. doi:10.2753/mis0742-1222290408 es_ES
dc.description.references Vardasbi, A., Faili, H., & Asadpour, M. (2017). SWIM. ACM Transactions on Information Systems, 36(1), 1-33. doi:10.1145/3072652 es_ES
dc.description.references Wang, Y., Li, Y., Fan, J., & Tan, K.-L. (2018). Location-aware Influence Maximization over Dynamic Social Streams. ACM Transactions on Information Systems, 36(4), 1-35. doi:10.1145/3230871 es_ES
dc.description.references Watts, D. J., & Dodds, P. S. (2007). Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research, 34(4), 441-458. doi:10.1086/518527 es_ES
dc.description.references Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010). TwitterRank. Proceedings of the third ACM international conference on Web search and data mining - WSDM ’10. doi:10.1145/1718487.1718520 es_ES
dc.description.references Wolfsfeld, G., Segev, E., & Sheafer, T. (2013). Social Media and the Arab Spring. The International Journal of Press/Politics, 18(2), 115-137. doi:10.1177/1940161212471716 es_ES
dc.description.references Yik, M. S. M., Russell, J. A., & Barrett, L. F. (1999). Structure of self-reported current affect: Integration and beyond. Journal of Personality and Social Psychology, 77(3), 600-619. doi:10.1037/0022-3514.77.3.600 es_ES
dc.description.references Zhang, J., Zhang, R., Sun, J., Zhang, Y., & Zhang, C. (2016). TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter. IEEE/ACM Transactions on Networking, 24(5), 2834-2846. doi:10.1109/tnet.2015.2494059 es_ES
dc.description.references 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. doi:10.1016/j.ijresmar.2016.07.003 es_ES
dc.description.references Ziegler, C.-N., & Lausen, G. (2005). Propagation Models for Trust and Distrust in Social Networks. Information Systems Frontiers, 7(4-5), 337-358. doi:10.1007/s10796-005-4807-3 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem