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A novel influence quantification model on Instagram using data science approach for targeted business advertising and better digital marketing outcomes

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Abstract

Instagram is one of the most popular and widely used social network platforms. It is used as a digital tool to connect with other users and also to share information and influence them for marketing and advertising purposes. The influence of popular users is broadly determined by post’s engagement rate in terms of likes, comments, and shares, and the number of followers as well. An objective and comprehensive measure of popularity is necessary to understand the factors that will help make an influencer marketing campaign more successful and beneficial for business activities. This research work attempts to take various features of an influencer account and Instagram posts dataset and develop a novel model that accurately quantifies and determines the influence of a user on Instagram. The research is based on datasets of top regional Instagram influencers and their posts based on categories signified through hashtags and captions. Our research attempts to develop a model using principal component analysis to quantify influence and using it to rank influencers. In our experiment, the proposed model after experimentation, gave the Instagram username “iqbaal.e” influence score as 874,712.9526, username “1nctdream” as 753,830.5847 and username “weareone. exo” as 668,054.4360. The proposed model ranks were compared with other ranks for Instagram users based on other measures such as follower rank etc. User names “huyitian”, “bintangemon” and “bimopd” are top social media influencers based on the proposed model for better business advertising and digital marketing outcomes with collected data and experiment context. This proposed approach gives an exploration for the stakeholders to quantify the impact of influencer in social media and demonstrate an innovative approach.

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Data availability

Authors declare that all the data being used in the experiment is declared in the manuscript.

References

  • Abidin C (2021) Mapping internet celebrity on Tiktok: exploring attention economies and visibility labours. Cult Sci J 12(1):77–103

    Article  MathSciNet  Google Scholar 

  • Arasu BS, Seelan BJB, Thamaraiselvan N (2020) A machine learning-based approach to enhancing social media marketing. Comput Electric Eng 86:106723

    Article  Google Scholar 

  • Argyris YA, Wang Z, Kim Y, Yin Z (2020) The effects of visual congruence on increasing consumers’ brand engagement: an empirical investigation of influencer marketing on Instagram using deep-learning algorithms for automatic image classification. Comput Hum Behav 112:106443

    Article  Google Scholar 

  • Arman AA, Sidik AP (2019) Measurement of engagement rate in Instagram (case study: Instagram Indonesian government ministry and institutions). In: 2019 international conference on ICT for smart society (ICISS), volume 7, pp 1–6. IEEE

  • Arora A, Bansal S, Kandpal C, Aswani R, Dwivedi Y (2019) Measuring social media influencer index-insights from Facebook, twitter and Instagram. J Retail Consum Serv 49:86–101

    Article  Google Scholar 

  • Belanche D, Flavián M, Ibáñez-Sánchez S (2020) Followers’ reactions to influencers’ Instagram posts. Span J Mark-ESIC 24(1):37–54

    Google Scholar 

  • Bentley K, Chu C, Nistor C, Pehlivan E, Yalcin T (2021) Social media engagement for global influencers. J Glob Mark 34(3):205–219

    Article  Google Scholar 

  • Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. Springer, Berlin

    Google Scholar 

  • Carta S, Podda AS, Recupero DR, Saia R, Usai G (2020) Popularity prediction of Instagram posts. Information 11(9):453

    Article  Google Scholar 

  • Chawla RN, Goyal P (2022) Emerging trends in digital transformation: a bibliometric analysis. Benchmark Int J 29(4):1069–1112

    Article  Google Scholar 

  • Deshpande T, Datta S, Shah R, Doshi V, Dongre D (2022) Multilevel credit card fraud detection using face recognition and machine learning. In: International conference on communication and intelligent systems, pp 103–114. Springer

  • Dorfman RG, Vaca EE, Mahmood E, Fine NA, Schierle CF (2018) Plastic surgery-related hashtag utilization on Instagram: implications for education and marketing. Aesthet Surg J 38(3):332–338

    Article  Google Scholar 

  • Ershov D, Mitchell M (2020) The effects of influencer advertising disclosure regulations: evidence from Instagram. In: Proceedings of the 21st ACM conference on economics and computation, pp 73–74

  • Evans D, Bratton S, McKee J (2021) Social media marketing. AG Printing & Publishing, London

    Google Scholar 

  • Goh K-Y, Heng C-S, Lin Z (2013) Social media brand community and consumer behavior: quantifying the relative impact of user-and marketer-generated content. Inf Syst Res 24(1):88–107

    Article  Google Scholar 

  • Gräve J-F (2019) What Kpis are key? Evaluating performance metrics for social media influencers. Soc Media+ Soc 5(3):2056305119865475

    Google Scholar 

  • Guarda T, Augusto MF, Victor JA, Mazón LM, Lopes I, Oliveira P (2021) The impact of Tiktok on digital marketing. In: Marketing and smart technologies: proceedings of ICMarkTech 2020, pp 35–44. Springer

  • Himelboim I, Golan GJ (2019) A social networks approach to viral advertising: the role of primary, contextual, and low influencers. Soc Media+ Soc 5(3):2056305119847516

    Google Scholar 

  • Huber GP (2009) A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making. In: Knowledge, groupware and the Internet, pp 221–254. Routledge

  • Hudders L, De Jans S (2022) Gender effects in influencer marketing: an experimental study on the efficacy of endorsements by same-vs. other-gender social media influencers on Instagram. Int J Advert 41(1):128–149

    Article  Google Scholar 

  • Jin SV, Muqaddam A, Ryu E (2019) Instafamous and social media influencer marketing. Market Intell Plan 37(5):567–579

    Article  Google Scholar 

  • Jin Y, Lin J-S, Gilbreath B, Lee Y-I (2017) Motivations, consumption emotions, and temporal orientations in social media use: a strategic approach to engaging stakeholders across platforms. Int J Strateg Commun 11(2):115–132

    Article  Google Scholar 

  • Kietzmann JH, Hermkens K, McCarthy IP, Silvestre BS (2011) Social media? Get serious! understanding the functional building blocks of social media. Bus Horiz 54(3):241–251

    Article  Google Scholar 

  • Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

    Article  Google Scholar 

  • Klostermann J, Plumeyer A, Böger D, Decker R (2018) Extracting brand information from social networks: integrating image, text, and social tagging data. Int J Res Mark 35(4):538–556

    Article  Google Scholar 

  • Kolo C, Haumer F (2018) Social media celebrities as influencers in brand communication: an empirical study on influencer content, its advertising relevance and audience expectations. J Digit Soc Media Market 6(3):273–282

    Google Scholar 

  • Kumar S (2023) A novel hybrid machine learning model for prediction of co2 using socio-economic and energy attributes for climate change monitoring and mitigation policies. Ecol Inf 77:102253

    Article  Google Scholar 

  • Kumar S, Kalia A, Sharma A (2018) Predictive analysis of alertness related features for driver drowsiness detection. In: Intelligent systems design and applications: 17th international conference on intelligent systems design and applications (ISDA 2017) held in Delhi, India, December 14–16, 2017, pp 368–377. Springer

  • Kumar S, Nisha Z (2022) Does social media feed tell about your mental state? A deep randomised neural network approach. In: 2022 international joint conference on neural networks (IJCNN), pp 1–8. IEEE

  • Kumar S, Pal SK, Singh RP (2018) Intra elm variants ensemble based model to predict energy performance in residential buildings. Sustain Energy Grids Netw 16:177–187

    Article  Google Scholar 

  • Kumar S, Pal SK, Singh RP (2018) A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes. Energy Build 176:275–286

    Article  Google Scholar 

  • Kumar S, Panwar S, Singh J, Sharma AK, Nisha Z (2022) iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic process. Int J Syst Assur Eng Manag 13(5):2572–2582

    Article  Google Scholar 

  • Laygo-Matsumoto S, Samonte MJ (2021) Philippine economic growth: GDP prediction using machine learning algorithms. In: 2021 4th international conference on computing and big data, pp 15–20

  • McClure C, Seock Y-K (2020) The role of involvement: investigating the effect of brand’s social media pages on consumer purchase intention. J Retail Consum Serv 53:101975

    Article  Google Scholar 

  • Muhammad SS, Dey BL, Kamal MM, Alwi SFS (2021) Consumer engagement with social media platforms: a study of the influence of attitudinal components on cutting edge technology adaptation behaviour. Comput Hum Behav 121:106802

    Article  Google Scholar 

  • Pancer E, Poole M (2016) The popularity and virality of political social media: hashtags, mentions, and links predict likes and retweets of 2016 us presidential nominees’ tweets. Soc Influ 11(4):259–270

    Article  Google Scholar 

  • Pang X, Zhou Y, Wang P, Lin W, Chang V (2020) An innovative neural network approach for stock market prediction. J Supercomput 76:2098–2118

    Article  Google Scholar 

  • Pansari A, Kumar V (2017) Customer engagement: the construct, antecedents, and consequences. J Acad Mark Sci 45:294–311

    Article  Google Scholar 

  • Purba KR, Asirvatham D, Murugesan RK (2020) An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram. ICT Express 6(3):243–248

    Article  Google Scholar 

  • Pylieva H, Baran I (2018) Kernel-pca-report. https://github.com/hpylieva/kernel-pca-report/tree/master

  • Sands S, Campbell CL, Plangger K, Ferraro C (2022) Unreal influence: leveraging AI in influencer marketing. Eur J Mark 56(6):1721–1747

    Article  Google Scholar 

  • Sokolova K, Perez C (2021) You follow fitness influencers on Youtube. but do you actually exercise? How Parasocial relationships, and watching fitness influencers, relate to intentions to exercise. J Retail Consum Serv 58:102276

    Article  Google Scholar 

  • Stieglitz S, Dang-Xuan L (2013) Social media and political communication: a social media analytics framework. Soc Netw Anal Min 3:1277–1291

    Article  Google Scholar 

  • Taillon BJ, Mueller SM, Kowalczyk CM, Jones DN (2020) Understanding the relationships between social media influencers and their followers: the moderating role of closeness. J Product Brand Manage 29(6):767–782

    Article  Google Scholar 

  • Tenkanen H, Di Minin E, Heikinheimo V, Hausmann A, Herbst M, Kajala L, Toivonen T (2017) Instagram, Flickr, or Twitter: assessing the usability of social media data for visitor monitoring in protected areas. Sci Rep 7(1):17615

    Article  Google Scholar 

  • Tiggemann M, Zaccardo M (2018) ‘Strong is the new skinny’: a content analysis of# fitspiration images on Instagram. J Health Psychol 23(8):1003–1011

    Article  Google Scholar 

  • Rehman MH, Liew CS, Abbas A, Jayaraman PP, Wah TY, Khan SU (2016) Big data reduction methods: a survey. Data Sci Eng 1:265–284

    Article  Google Scholar 

  • Vergeer M, Hermans L, Sams S (2013) Online social networks and micro-blogging in political campaigning: the exploration of a new campaign tool and a new campaign style. Party Polit 19(3):477–501

    Article  Google Scholar 

  • Vrontis D, Makrides A, Christofi M, Thrassou A (2021) Social media influencer marketing: a systematic review, integrative framework and future research agenda. Int J Consum Stud 45(4):617–644

    Article  Google Scholar 

  • Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246

    Article  Google Scholar 

  • Wibisono AI, Ruldeviyani Y (2021) Detecting social media influencers of airline services through social network analysis on twitter: a case study of the Indonesian airline industry. In: 2021 3rd East Indonesia conference on computer and information technology (EIConCIT), pp 314–319. IEEE

  • Yesiloglu S, Costello J (2020) Influencer marketing: building brand communities and engagement. Routledge, London

    Book  Google Scholar 

  • Yew RLH, Suhaidi SB, Seewoochurn P, Sevamalai VK (2018) Social network influencers’ engagement rate algorithm using Instagram data. In: 2018 fourth international conference on advances in computing, communication & automation (ICACCA), pp 1–8. IEEE

  • Zadeh AH, Sharda R (2014) Modeling brand post popularity dynamics in online social networks. Decis Support Syst 65:59–68

    Article  Google Scholar 

  • Zhang H, Zang Z, Zhu H, Uddin MI, Amin MA (2022) Big data-assisted social media analytics for business model for business decision making system competitive analysis. Inf Process Manage 59(1):102762

    Article  Google Scholar 

  • Zhang K, Wang W, Lv Z, Fan Y, Song Y (2021) Computer vision detection of foreign objects in coal processing using attention CNN. Eng Appl Artif Intell 102:104242

    Article  Google Scholar 

  • Zhou L, Jin F, Wu B, Chen Z, Wang CL (2023) Do fake followers mitigate influencers’ perceived influencing power on social media platforms? The mere number effect and boundary conditions. J Bus Res 158:113589

    Article  Google Scholar 

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Funding

The authors received a funding as faculty research grant (FRG) from the Institute of Eminence(IOE), University of Delhi.

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Authors and Affiliations

  1. Cluster Innovation Centre, University of Delhi, New Delhi, Delhi, India

    Sachin Kumar, Kartikey Saran, Yashu Garg, Gaurav Dubey & Shivam Goel

  2. Indraprastha Institute of Information Technology, Delhi, New Delhi, Delhi, India

    Alok Nikhil Jha

  3. Faculty of Engg and Natural Sciences, Western Norway University of Applied Sciences, Haugesund, Norway

    Ajit Kumar Verma

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  1. Sachin Kumar
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Correspondence to Sachin Kumar.

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Kumar, S., Saran, K., Garg, Y. et al. A novel influence quantification model on Instagram using data science approach for targeted business advertising and better digital marketing outcomes. Soc. Netw. Anal. Min. 14, 71 (2024). https://doi.org/10.1007/s13278-024-01230-z

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