Abstract
The massive volume of social data generated by the extensive adaptation of social media platforms proposes exceptional opportunities to understand opinion trends, customer behavior, and public sentiment. Nonetheless, extracting actionable insights from these complex datasets poses a massive challenge. This research article explores various Artificial Intelligence (AI) methods, such as natural language processing, machine learning, and network analysis, to crack the power of social media data. In time, evidence-based, data-driven information enables us to understand better sentiments, opinions, topics, and trends in digital media culture. The author used a three-step process in this research article: first, existing literature materials were explored using exclusion and inclusion criteria. Second, a survey study was conducted with 200 respondents. Besides, multiple regression analyses for statistical significance were conducted. Lastly, a classroom experiment was conducted with a marketing analytics class with 27 students. Results of the analysis revealed that the combination of independent variables significantly predicted the use of artificial intelligence for converting social media data into actionable insights, F (3, 196) = 67.143, p < .001, accounting for 51% (R2 = .507) of the variance in the use of AI. The variables of NLP (β = −.285, p < .001), machine learning (β = .407, p < .001) and network analysis (β = .275, p < .001) were statistically significant predictors. Findings indicated that using AI to transform social media data into actionable insights carries significant value for businesses, marketing practitioners, and decision-makers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kang, Y., Cai, Z., Tan, C.-W., Huang, Q., Liu, H.: Natural language processing (NLP) in management research: a literature review. J. Manag. Anal. 7(2), 139–172 (2020). https://doi.org/10.1080/23270012.2020.1756939
Mackenzie, A.: The production of prediction: what does machine learning want?. Eur. J. Cult. Stud. 18, 429–445 (2015). https://doi.org/10.1177/1367549415577384
Scott, J.: What is social network analysis? Bloomsbury Academic, London (2012). https://doi.org/10.5040/9781849668187
Ledro, C., Nosella, A., Vinelli, A.: Artificial intelligence in customer relationship management: literature review and future research directions. J. Bus. Ind. Mark. 37(13), 48–63 (2022). https://doi.org/10.1108/JBIM-07-2021-0332
Wamba, S.F., Queiroz, M.M., Guthrie, C., Braganza, A.: Industry experiences of artificial intelligence (AI): benefits and challenges in operations and supply Chain management. Prod. Plan. Control 33(16), 1493–1497 (2022). https://doi.org/10.1080/09537287.2021.1882695
Khatua, A., Khatua, A., Chi, X., Cambria, E.: Artificial Intelligence, social media and supply chain management: the way forward. Electronics 10(19), 2348 (2021). https://doi.org/10.3390/electronics10192348
Somani, S., van Buchem, M.M., Sarraju, A.: Artificial Intelligence – enabled analysis of statin-related topics and sentiments on social media. JAWA Netw. Open 6(4), e239747 (2023). https://doi.org/10.1001/jamanetworkopen.2023.9747
Helo, P., Hao, Y.: Artificial intelligence in operations management and supply chain management: an exploratory case study. Prod. Plan. Control 33(16), 1573–1590 (2020). https://doi.org/10.1080/09537287.2021.1882690
Beheshti, B., et al.: AI-enabled processes: the age of artificial intelligence and big data. In: Hacid, H., et al. (eds.) Service-Oriented Computing – ICSOC 2021 Workshops. ICSOC 2021. Lecture Notes in Computer Science, pp. 321–335. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-14135-5_29
Jyoti, R.: Unlock the true power of data analytics with artificial intelligence. In: Data Analytics and AI. Taylor and Francis Group (2021). https://doi.org/10.1201/9781003019855-2
Dwivedi, Y.K., Wang, Y.: Artificial intelligence for B2B marketing: challenges and opportunities. Ind. Mark. Manag. 105, 109–113 (2022). https://doi.org/10.1016/j.indmarman.2022.06.001
Gera, R., Alok, K.: Artificial Intelligence in consumer behaviour: a systematic literature review of empirical research papers published in marketing journals (2000–2021). Acad. Mark. Stud. J. 27(1), 1–16 (2023)
Basri, W.: Examining the impact of Artificial Intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. Int. J. Comput. Intell. Syst. 13, 142–152 (2020). https://doi.org/10.2991/ijcis.d.200127.002
Nadkarni, P.M., Ohno-Machado, L., Chapman, W.W.: Natural language processing: an introduction. J. Am. Med. Inf. Assoc. 18, 544–551 (2011). https://doi.org/10.1136/amiajnl-2011-000464
Ghouri, M., Mani, V., Amin, M., Kamble, S.S.: The micro foundations of social media use: artificial intelligence integrated routine model. J. Bus. Res. 144, 80–92 (2022). https://doi.org/10.1016/j.jbusres.2022.01.084
Brynjolfsson, E., Mitchell, T.: What can machine learning do? workforce implications. Science 358, 1530–1534 (2017). https://doi.org/10.1126/science.aap8062
Hayes, J.L., Britt, B.C., Evans, W., Rush, S.W., Towery, N.A., Adamson, A.C.: Can social media listening platforms’ artificial intelligence be trusted? examining the accuracy of Crimson Hexagon’s (Now Brandwatch Consumer Research’s) AI-Driven Analyses. J. Advert. 50(1), 81–91 (2020). https://doi.org/10.1080/00913367.2020.1809576
El Naqa, Murphy, M.J.: What is machine learning? In: Machine Learning in Radiation Oncology, pp. 3–11 (2015). https://doi.org/10.1007/978-3-319-18305-3_1
van Duijn, M., Vermunt, J.K.: What is special about social network analysis? Methodology 2, 2–6 (2006). https://doi.org/10.1027/1614-2241.2.1.2
Li, J., Ye, Z., Zhang, C.: Study on the interaction between big data and artificial intelligence. Syst. Res. Behav. Sci. 39(3), 641–648 (2022). https://doi.org/10.1002/sres.2878
Rodgers, S.: Themed issue introduction: promises and perils of artificial intelligence and advertising. J. Advert. 50, 1–10 (2021). https://doi.org/10.1080/00913367.2020.1868233
Scott, J.: Social network analysis: developments, advances, and prospects. Social Netw. Anal. Mining 1, 21–26 (2011). https://doi.org/10.1007/s13278-010-0012-6
Mrsic, L.: Impact of Artificial Intelligence on DOOH advertising: Message-persuasion level enhancement using illusion board and personalized insights. In: International Conference on Intelligent Computing & Optimization (2022). https://doi.org/10.1007/978-3-031-19958-5_14
Taherdoost, H., Madanchian, M.: Artificial Intelligence and sentiment analysis: a review in competitive research. Computers 12(2), 37 (2023). https://doi.org/10.3390/computers12020037
Roetzer, P., Kaput, M.: Marketing Artificial Intelligence: AI, Marketing, and the Future of Business. BenBella Books, Dallas (2022)
Schmitt, M.: Data analytics in the metaverse: Business value creation with artificial intelligence and data-driven decision making. SSRN (2023). https://doi.org/10.2139/ssrn.4385347
Thayyib, P.V., Mamilla, R., Khan, M., Humaira, F., Mohd, A., Imran, A.: State-of-the-Art of Artificial Intelligence and big data analytics reviews in five different domains: a bibliometric summary. Sustainability 15(5), 4026 (2023). https://doi.org/10.3390/su15054026
Haleem, M., Javaid, M., Qadri, M.A., Singh, R.P., Suman, R.: Artificial intelligence (AI) applications for marketing: a literature-based study. Int. J. Intell. Netw. 3, 119–132 (2022). https://doi.org/10.1016/j.ijin.2022.08.005
Wu, L., Dodoo, N.A., Wen, J.W., Ke, L.: Understanding Twitter conversations about artificial intelligence in advertising based on natural language processing. Int. J. Advert. 41(4), 685–702 (2021). https://doi.org/10.1080/02650487.2021.1920218
van Esch, P., Cui, Y., Jain, S.P.: Stimulating or Intimidating: the effect of AI-Enabled in-store communication on consumer patronage Likelihood. J. Advert. 50, 63–80 (2021). https://doi.org/10.1080/00913367.2020.1832939
Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., Potapov, D.: Digital analytics: modeling for insights and new methods. J. Interact. Mark. 15(1), 26–43 (2022)
Arasu, S.B., Seelan, B.J.B., Thamaraiselvan, N.: A machine learning-based approach to enhancing social media marketing. Comput. Electr. Eng. 86, 10673 (2020). https://doi.org/10.1016/j.compeleceng.2020.106723
Thomas, V.L., Fowler, K.: Close encounters of the AI kind: use of AI influencers as brand endorsers. J. Advert. 50, 11–25 (2020). https://doi.org/10.1080/00913367.2020.1810595
Bharadiya, J.P.: Driving business growth with Artificial Intelligence and business intelligence. Int. J. Comput. Sci. Technol. 6(4), 28–44 (2022)
Tanev, S., Blackbright, H.: Artificial Intelligence and innovation management. In: Series on Technology Management, vol. 38. World Scientific Publication Europe (2022). https://doi.org/10.1142/q0334
Campbell, C., Plangger, K., Sands, S., Kietzmann, J.: Preparing for an era of deepfakes and AI-generated ads: a framework for understanding responses to manipulated advertising. J. Advert. 51, 22–38 (2022). https://doi.org/10.1080/00913367.2021.1909515
Chintalapati, S., Pandey, S.K.: Artificial intelligence in marketing: a systematic literature review. Int. J. Mark. Res. 64(1), 38–68 (2021). https://doi.org/10.1177/14707853211018428
Beheshti, B., Benatallah, Q., Sheng, Z., Schiliro, F.: Intelligent knowledge lakes: the age of Artificial Intelligence and Big Data. In: Web Information Systems Engineering (2020). https://doi.org/10.1007/978-981-15-3281-8_3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gabelaia, I. (2024). The Use of Artificial Intelligence to Convert Social Media Data into Actionable Insights. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2023. Lecture Notes in Networks and Systems, vol 913. Springer, Cham. https://doi.org/10.1007/978-3-031-53598-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-53598-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53597-0
Online ISBN: 978-3-031-53598-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)