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Exploring the advertising elements of electronic word-of-mouth in social media: an example of game reviews

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Abstract

The influence of social communities has quietly surpassed traditional media. Electronic word-of-mouth (eWOM) is far greater than in other forms of traditional advertising. More and more enterprises hire key opinion leaders (KOLs) to write product-related comments, ho** to influence the purchasing behavior of other users in the community. In fact, the power of text reviews on social media is more powerful than traditional advertising models. For in-app advertising, it is one of the important issues to understand the focus of ad viewers to improve the effectiveness of advertising and then enhance the click-through rate (CTR) of in-App ads. However, relatively few studies focus on studying what elements should be contained in a successful commercial review on social media. Consequently, this study will treat social media reviews as a kind of new advertising modes and attempt to find the contained elements of ads in these text comments by using natural language processing (NLP), latent semantics analysis (LSA), and matrix diagram techniques. The discovered elements of positive comments (commercial reviews) will be compared to those in negative reviews (true authentic voices of customers). Based on the results, we can provide advertising companies with suggestions when hiring KOLs to write recommendation reviews.

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References

  1. **g P, Cai Y, Wang B, Wang B, Huang J, Jiang C, Yang C (2023) Listen to social media users: Mining Chinese public perception of automated vehicles after crashes. Transport Res F: Traffic Psychol Behav 93:248–265

    Article  Google Scholar 

  2. Chang J-R, Chen L-S, Chang C-W (2020) New term weighting methods for classifying textual sentiment data. Intl J Appl Sci Eng 17(3):257–268

    MathSciNet  Google Scholar 

  3. Chang, J.-R., Liang, H.-Y., Chen, L.-S., Chang, C.-W. (2020b) Novel feature selection approaches for improving the performance of sentiment classification, J Ambient Intell Human Computhttps://doi.org/10.1007/s12652-020-02468-z

  4. Chang J-R, Chen L-S, Lin L-W (2021) A Novel Cluster based Over-sampling Approach for Classifying Imbalanced Sentiment Data. IAENG Int J Comput Sci 48(4):1118–1128

    Google Scholar 

  5. Chowdhary KR (2020) Fundamentals of artificial intelligence, In Fundamentals of Artificial Intelligence, Springer Indiahttps://doi.org/10.1007/978-81-322-3972-7

  6. Chen PT, Hsieh HP (2012) Personalized mobile advertising: Its key attributes, trends, and social impact. Technol Forecast Soc Chang 79:543–557

    Article  ADS  Google Scholar 

  7. Chen W-K, Chen L-S, Pan Y-T (2021a) A text mining-based framework to discover the important factors in text reviews for predicting the views of live streaming, Appl Soft Comput, Vol. 111, 107704

  8. Chen W-K, Riantama D, Chen L-S (2021) Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry. Sustainability 13(1):268

    Article  Google Scholar 

  9. Dhun, Dangi (2023) Influencer Marketing: Role of Influencer Credibility and Congruence on Brand Attitude and eWOM. J Internet Commerce 22(sup1):S28–S72. https://doi.org/10.1080/15332861.2022.2125220

    Article  Google Scholar 

  10. eMarketer (2020) Digital ad spending 2019. Available at https://www.emarketer.com/content/global-digital-ad-spending-2019

  11. Filieri R, Galati F, Raguseo E (2021) The impact of service attributes and category on eWOM helpfulness: An investigation of extremely negative and positive ratings using latent semantic analytics and regression analysis. Comput Human Behav 114:106527

    Article  Google Scholar 

  12. Gao C, Zeng J, Sarro F, Lo D, King I, Lyu MR (2021) Do users care about ad’s performance costs? Exploring the effects of the performance costs of in-app ads on user experience. Inform Softw Technol 132:106471

    Article  Google Scholar 

  13. Gonzalvez-Cabañas JC, Mochón F (2016) Operating an Advertising Programmatic Buying Platform: A Case Study, International Journal of Interactive Multimedia and Artificial. Intelligence 3(6):6–15

    Google Scholar 

  14. Hsiao Y-H, Hsiao Y-T (2021) Online review analytics for hotel quality at macro and micro levels. Ind Manag Data Syst 121(2):268–289

    Article  Google Scholar 

  15. Huynh-Cam T-T, Nalluri V, Chen L-S, Yang Y-Y (2022) IS-DT: A New Feature Selection Method for Determining the Important Features in Programmatic Buying. Big Data Cognit Comput 6(4):118

    Article  Google Scholar 

  16. Jain S, Seeja KR, **dal R (2020) A new methodology for computing semantic relatedness: Modified latent semantic analysis by fuzzy formal concept analysis. Procedia Comput Sci 167:1102–1109

    Article  Google Scholar 

  17. Jorgensen JJ, Ha Y (2019) The Influence of Electronic Word of Mouth via Social Networking Sites on the Socialization of College-Aged Consumers. J Interact Advert 19(1):29–42. https://doi.org/10.1080/15252019.2018.1533500

    Article  Google Scholar 

  18. José MP, Silvia SB, Carla RM, Joaquin AM (2013) Key factors of teenagers’ mobile advertising acceptance. Ind Manag Data Syst 113(5):732–749

    Article  Google Scholar 

  19. Kim HJ, Chan-Olmsted S (2022) Influencer Marketing and Social Commerce: Exploring the Role of Influencer Communities in Predicting Usage Intent. J Interact Advert 22(3):249–268. https://doi.org/10.1080/15252019.2022.2111243

    Article  Google Scholar 

  20. Kwon H, Park Y (2018) Proactive development of emerging technology in a socially responsible manner: Data-driven problem solving process using latent semantic analysis. J Eng Tech Manage 50:45–60

    Article  Google Scholar 

  21. Li J, Ni X, Yuan Y, Wang FY (2018) A hierarchical framework for ad inventory allocation in programmatic advertising markets. Electron Commer Res Appl 31:40–51

    Article  Google Scholar 

  22. Liu Q, Lu J, Zhang G, Shen T, Zhang Z, Huang H (2021) Domain-specific meta-embedding with latent semantic structures. Inf Sci 555:410–423

    Article  MathSciNet  Google Scholar 

  23. Janssen L, Schouten AP, Croes EAJ (2022) Influencer advertising on Instagram: product-influencer fit and number of followers affect advertising outcomes and influencer evaluations via credibility and identification. Int J Advert 41(1):101–127. https://doi.org/10.1080/02650487.2021.1994205

    Article  Google Scholar 

  24. Martínez-Huertas JÁ, Olmos R, León JA (2021) Enhancing topic-detection in computerized assessments of constructed responses with distributional models of language. Expert Syst Appl 185:115621

    Article  Google Scholar 

  25. Maseeh HI, Jebarajakirthy C, Pentecost R, Ashaduzzaman M, Arli D, Weaven S (2021) A meta-analytic review of mobile advertising research. J Bus Res 136:33–51

    Article  Google Scholar 

  26. Maslowska E, Ohme J, Segijn CM (2021) Attention to Social Media Ads: The Role of Consumer Recommendations and Smartphones. J Interact Advert 21(3):283–296. https://doi.org/10.1080/15252019.2021.1997675

    Article  Google Scholar 

  27. Nalluri V, Mayopu RG, Chen L-S (2023) Modelling the key attributes for improving customer repurchase rates through Mobile Advertisements using a Fuzzy mixed approach. J Model Manag. https://doi.org/10.1108/JM2-02-2023-0022

    Article  Google Scholar 

  28. Parali U, Zontul M, Ertugrul DC (2019) Information Retrieval Using the Reduced Row Echelon Form of a Term-Document Matrix. J Internet Technol 20(4):1037–1046. https://doi.org/10.3966/160792642019072004004

    Article  Google Scholar 

  29. Pathan AF, Prakash C (2021) Unsupervised Aspect Extraction Algorithm for opinion mining using topic modeling. Global Transit Proc 2(2):492–499

    Article  Google Scholar 

  30. Saima, Altaf Khan (2021) Effect of Social Media Influencer Marketing on Consumers’ Purchase Intention and the Mediating Role of Credibility. J Promot Manag 27(4):503–523. https://doi.org/10.1080/10496491.2020.1851847

    Article  Google Scholar 

  31. Salehudin I, Alpert F (2022) To pay or not to pay: understanding mobile game app users’ unwillingness to pay for in-app purchases. J Res Interact Mark 16(4):633–647

    Google Scholar 

  32. Samuel A, White GRT, Thomas R, Jones P (2021) Programmatic advertising: An exegesis of consumer concerns. Comput Human Behav 116:106657

    Article  Google Scholar 

  33. Sezgen E, Mason KJ, Mayer R (2019) Voice of airline passenger: A text mining approach to understand customer satisfaction. J Air Transp Manag 77:65–74

    Article  Google Scholar 

  34. Shehu E, Nabout NA, Clement M (2021) The risk of programmatic advertising: Effects of website quality on advertising effectiveness. Int J Res Mark 38:663–677

    Article  Google Scholar 

  35. Shen CW, Ho J-T (2020) Technology-enhanced learning in higher education: A bibliometric analysis with latent semantic approach. Comput Human Behav 104:106177

    Article  Google Scholar 

  36. Suleman RM, Korkontzelos I (2021) Extending latent semantic analysis to manage its syntactic blindness. Expert Syst Appl 165:114130

    Article  Google Scholar 

  37. Sung E (2021) The effects of augmented reality mobile app advertising: Viral marketing via shared social experience. J Bus Res 122:75–87

    Article  Google Scholar 

  38. Whitea GRT, Samuel A (2019) Programmatic Advertising: Forewarning and avoiding hype-cycle failure. Technol Forecast Soc Chang 144:157–168

    Article  Google Scholar 

  39. Yu B, Xu Z-B, Li C-H (2008) Latent semantic analysis for text categorization using neural network. Knowl Based Syst 21(8):900–904

    Article  Google Scholar 

  40. Yun JT, Duff BRL, Vargas PT, Sundaram H, Himelboim I (2020) Computationally Analyzing Social Media Text for Topics: A Primer for Advertising Researchers. J Interact Advert 20(1):47–59. https://doi.org/10.1080/15252019.2019.1700851

    Article  Google Scholar 

  41. Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826

    Article  Google Scholar 

Download references

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Correspondence to Long-Sheng Chen.

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Mayopu, R.G., Wang, YY. & Chen, LS. Exploring the advertising elements of electronic word-of-mouth in social media: an example of game reviews. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18642-w

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