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Influencer Detection Through Social Network Analysis on Twitter of the Indonesian Smartphone Industry

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Proceedings of 7th ASRES International Conference on Intelligent Technologies (ICIT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 685))

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Abstract

In the modern era, the company's marketing strategy has included the use of social media to increase product sales. For a business, social media can give both tangible and intangible benefits. The role of influencers is undeniably important in reviewing smartphones to generate significant sales profits, therefore identifying the proper influencer is a challenge for companies. Influencers in the smartphone industry are commonly selected based on several factors. This case research aims to show how the public, particularly on Twitter, is discussing the brand's smartphone products. This study aims to identify actors who play a key role in promoting smartphone products. The In-Degree, Outdegree, and Betweenness Centrality measures are used in this study to identify influencers using the Social Network Analysis approach. It was found that some influencers are also thought to have the ability to raise a brand's product awareness. There are 3 types of influencers, a real influencer who like to review smartphone product, an account that focuses on educating people about the specification of a gadget, and a person who often retweet Samsung smartphone product.

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References

  1. 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: 3rd East Indonesia conference on computer and information technology (EIConCIT 2021), pp 314–319. https://doi.org/10.1109/EIConCIT50028.2021.9431876

  2. Prado-Romero MA, Oliva AF, Hernández LG (2018) Identifying twitter users influence and open mindedness using anomaly detection. In: Hernández Heredia Y, Milián Núñez V, Ruiz Shulcloper J (eds) Progress in artificial intelligence and pattern recognition. IWAIPR 2018. LNCS, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_19

  3. Majeed S, Qamar U, Farooq A (2019) State of art techniques for social influence analysis: a systematic literature review. In: Proceedings. - 2018 international conference on frontiers information technology. FIT 2018, pp 200–205. https://doi.org/10.1109/FIT.2018.00042

  4. Oueslati W, Arrami SA, Dhouioui Z, Massaabi M (2021) Opinion leaders’ detection in dynamic social networks. Concurr Comput Pract Exp 33(1):1–17. https://doi.org/10.1002/cpe.5692

    Article  Google Scholar 

  5. Bamakan SMH, Nurgaliev I, Qu Q (2019) Opinion leader detection: a methodological review. Expert Syst Appl 115:200–222. https://doi.org/10.1016/j.eswa.2018.07.069

    Article  Google Scholar 

  6. Pudjajana AM, Manongga D, Iriani A, Purnomo HD (2018) Identification of influencers in social media using social network analysis (SNA). In: 2018 international seminar on research of information technology and intelligent systems, ISRITI 2018, pp 400–404. https://doi.org/10.1109/ISRITI.2018.8864458

  7. Cossu, JV, Dugue N, Labatut V (2015) Detecting real-world influence through twitter. In: Proceedings - 2nd European network intelligence conference, ENIC 2015, pp 83–90, https://doi.org/10.1109/ENIC.2015.20

  8. Belák V, Lam S, Hayes C (2012) Targeting online communities to maximise information diffusion. In: WWW’ 2012 – Proceedings of 21st annual World wide web conference companion, pp 1153–1160. https://doi.org/10.1145/2187980.2188255

  9. Tauhid SM, Ruldeviyani Y (2020) Sentiment analysis of indonesians response to influencer in social media. In: 7th international conference on information technology, computer, and electrical engineering – Proceedings, no 1, pp 90–95. https://doi.org/10.1109/ICITACEE50144.2020.9239218

  10. Wagh R (2018) Survey on sentiment analysis using twitter dataset Rasika. In: 2018 Second international conference on electronics, communication and aerospace technology, ICECA, p [1] “wagh2018.pdf.”

    Google Scholar 

  11. Ennaji FZ, El Fazziki A, El Alaoui El H, Abdallaoui DB, Sadgal M (2018) Opinion leaders’ prediction for monitoring the product reputation. Int J Web Inf Syst 14(4):524–544. https://doi.org/10.1108/IJWIS-03-2018-0016

    Article  Google Scholar 

  12. Visansiriku V, Kitisin S (2018) Identifying influencers with ensemble classification approach on twitter. In: 2018 22nd international conference on computer science, ICSEC 2018, pp 1–4. https://doi.org/10.1109/ICSEC.2018.8712720

  13. Jain L, Katarya R, Sachdeva S (2020) Opinion leader detection using whale optimization algorithm in online social network. Expert Syst Appl 142:113016. https://doi.org/10.1016/j.eswa.2019.113016

    Article  Google Scholar 

  14. Wu Y, Duan Z (2015) Social network analysis of international scientific collaboration on psychiatry research. Int J Ment Health Syst 9(1):1–10. https://doi.org/10.1186/1752-4458-9-2

    Article  Google Scholar 

  15. Chakraborty A, Dutta T, Mondal S, Nath A (2018) Application of graph theory in social media. Int J Comput Sci Eng 6(10):722–729. https://doi.org/10.26438/ijcse/v6i10.722729

    Article  Google Scholar 

  16. Iglesias JA, Garcia-Cuerva A, Ledezma A, Sanchis A (2017) Social network analysis: evolving Twitter mining. In: 2016 IEEE international conference on systems, man, and cybernetics SMC, pp 1809–1814. https://doi.org/10.1109/SMC.2016.7844500

  17. Recuero R, Zago G, Soares F (2019) Using social network analysis and social capital to identify user roles on polarized political conversations on twitter. Soc Media Soc 5(2). https://doi.org/10.1177/2056305119848745

  18. Huang PY, Liu HY, Chen CH, Cheng PJ (2013) The impact of social diversity and dynamic influence propagation for identifying influencers in social networks. In: Proceedings - 2013 IEEE/WIC/ACM international conference on web intelligence WI 2013, vol 1, pp 410–416. https://doi.org/10.1109/WI-IAT.2013.58

  19. Zimbra D, Abbasi A, Zeng D, Chen H (2018) The state-of-the-art in twitter sentiment analysis. ACM Trans Manag Inf Syst 9(2):1–29. https://doi.org/10.1145/3185045

    Article  Google Scholar 

  20. Grandjean M (2016) A social network analysis of Twitter: mapping the digital humanities community. Cogent Arts Humanit 3(1):1–14. https://doi.org/10.1080/23311983.2016.1171458

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Indonesia, Jakarta, Indonesia

    Retiana Fadma Pertiwi Sinaga & Indra Budi

Authors
  1. Retiana Fadma Pertiwi Sinaga
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  2. Indra Budi
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Corresponding author

Correspondence to Indra Budi .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Gwalior, Gwalior, Madhya Pradesh, India

    Karm Veer Arya

  2. National Institute of Technology, Agartala, Agartala, Tripura, India

    Vipin Kumar Tripathi

  3. Universidad Nacional Mayor de San Marcos, Lima, Peru

    Ciro Rodriguez

  4. University of Pembangunan Jaya, Jakarta, Jakarta, Indonesia

    Eddy Yusuf

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sinaga, R.F.P., Budi, I. (2023). Influencer Detection Through Social Network Analysis on Twitter of the Indonesian Smartphone Industry. In: Arya, K.V., Tripathi, V.K., Rodriguez, C., Yusuf, E. (eds) Proceedings of 7th ASRES International Conference on Intelligent Technologies. ICIT 2022. Lecture Notes in Networks and Systems, vol 685. Springer, Singapore. https://doi.org/10.1007/978-981-99-1912-3_9

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