Abstract
Social media analytics is the process of deriving meaning from social media data to make better business decisions. Social media platforms offer businesses new insights into their strategies through social media analytics. Social media data analytics involves extracting, cleansing, transforming, and loading social data for further analysis. The data can be analyzed to identify patterns and trends in social media use. This information can be used to improve user experience on social media and to target advertising and content to specific user groups. The key performance indicators (KPIs) for social media analysis depend on the organization’s goals and objectives for using social media. Several KPIs can measure the success of Social Media Analytics initiatives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aldous, K.K., An, J., Jansen, B.J.: The challenges of creating engaging content: Results from a focus group study of a popular news media organization. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, CHI EA ’19, pp. 1–6. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290607.3312810
Aldous, K.K., An, J., Jansen, B.J.: Stylistic features usage: similarities and differences using multiple social networks. In: International Conference on Social Informatics, pp. 309–318. Springer (2019)
Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019)
Aldous, K.K., An, J., Jansen, B.J.: Measuring 9 emotions of news posts from 8 news organizations across 4 social media platforms for 8 months. ACM Trans. Soc. Comput. (TSC) 4(4), 1–31 (2022)
Aldous, K.K., An, J., Jansen, B.J.: What really matters?: characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics. Behav. Inf. Technol. 1–24 (2022)
Aljumah, A.I.: Examining the effect of social media interaction, e-WOM, and public relations: assessing the mediating role of brand awareness. Int. J. Data Netw. Sci. 7(1), 467–476 (2023)
Barbier, G., Liu, H.: Data mining in social media. Soc. Netw. Data Anal. 327–352 (2011)
Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30, 89–116 (2015)
Behrendt, S., Richter, A., Trier, M.: Mixed methods analysis of enterprise social networks. Comput. Netw. 75, 560–577 (2014)
Beigi, G., Hu, X., Maciejewski, R., Liu, H.: An overview of sentiment analysis in social media and its applications in disaster relief. In: Sentiment Analysis and Ontology Engineering: An Environment of Computational Intelligence, pp. 313–340 (2016)
Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G.: Network analysis in the social sciences. Science 323(5916), 892–895 (2009)
Chauhan, P., Sharma, N., Sikka, G.: The emergence of social media data and sentiment analysis in election prediction. J. Ambient. Intell. Humaniz. Comput. 12, 2601–2627 (2021)
Choi, J., Yoon, J., Chung, J., Coh, B.Y., Lee, J.M.: Social media analytics and business intelligence research: a systematic review. Inf. Process. Manag. 57(6), 102279 (2020)
Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)
Gammoudi, F., Sendi, M., Omri, M.N.: A survey on social media influence environment and influencers identification. Soc. Netw. Anal. Mining 12(1), 145 (2022)
Hyrynsalmi, S., Seppänen, M., Aarikka-Stenroos, L., Suominen, A., Järveläinen, J., Harkke, V.: Busting myths of electronic word of mouth: the relationship between customer ratings and the sales of mobile applications 10(2), 1–18 (2015). Publisher: Multidisciplinary Digital Publishing Institute
Jung, S.G., Salminen, J., Jansen, B.J.: Engineers, aware! commercial tools disagree on social media sentiment: Analyzing the sentiment bias of four major tools. In: Proceedings of the ACM on Human-Computer Interaction (EICS), vol. 6, pp. 1–20 (2022)
Li, F., Larimo, J., Leonidou, L.C.: Social media in marketing research: theoretical bases, methodological aspects, and thematic focus. Psychol. Market. 40(1), 124–145 (2023)
Makarenkov, V., Guy, I., Hazon, N., Meisels, T., Shapira, B., Rokach, L.: Implicit dimension identification in user-generated text with lstm networks. Inf. Process. Manage. 56(5), 1880–1893 (2019)
Matsa, K., Shearer, E.: News use across social media platforms 2017. Pew Research Center (2018)
Mostafa, M.M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)
Thonet, T., Cabanac, G., Boughanem, M., Pinel-Sauvagnat, K.: Users are known by the company they keep: Topic models for viewpoint discovery in social networks. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pp. 87–96. ACM (2017)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Jansen, B.J., Aldous, K.K., Salminen, J., Almerekhi, H., Jung, Sg. (2024). The Foundations of Social Media Analytics. In: Understanding Audiences, Customers, and Users via Analytics. Synthesis Lectures on Information Concepts, Retrieval, and Services. Springer, Cham. https://doi.org/10.1007/978-3-031-41933-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-41933-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41932-4
Online ISBN: 978-3-031-41933-1
eBook Packages: Synthesis Collection of Technology (R0)