Analysis of Users\' Interaction with E-Marketers\' Posts on Instagram Social Network

Moradi, Mohamm Analysis of Users\' Interaction with E-Marketers\' Posts on Instagram Social Network. Human Information Interaction, 2024, vol. 11, n. 3, pp. 21-39. [Journal article (Paginated)]

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English abstract

Social networks and their increasing influence among different users in all parts of the world have made these networks become suitable tools for advertising and e-commerce. Today, businesses have come to understand that social networks are and will continue to be a means of doing business. Instagram is a popular social network based on video and images. This social network is known as one of the powerful marketing tools. The number of views, likes and comments on social networks, including Instagram, plays a significant role in customer decision-making; Because they pay attention to the opinions and reception of other audiences towards that product or post and are influenced. This research analyzes what factors create posts with different levels of popularity. For this purpose, the factors affecting the number of views, likes and comments in an Instagram social network post are extracted and their weight and importance are calculated based on the regression model. Finally, the decision tree model is presented for forecasting and management in order to increase the number of visits, likes and comments. Methods and Materoal In this research, the type of research is based on the purpose of applied research. At first, library studies have been used in order to extract factors affecting the amount of visits, likes and comments in Instagram social network marketing posts. The statistical population includes all articles related to the factors affecting visits, likes and comments. The probability sampling method of simple random samples has been used and 30 articles in this field have been reviewed. Then, the data related to the factors identified from the previous stage have been extracted from the pages of big marketers on the Instagram social network. Then, using the extracted data and using the regression model, the weight and importance of each factor affecting the number of visits, likes, and comments of Instagram social network marketers' posts has been calculated. Finally, a decision tree model has been created to predict the status (rate of visits, likes and comments) of a marketing post on the Instagram social network based on the characteristics of that post. Resultss and Discussion Directly, factors such as the number of posts, the number of followers, the type of post, the content of the post and the time of the post are potential factors that affect the number of views, likes and comments. According to the obtained results, the "post content with survey" factor with a positive sign and a coefficient of 420,290.616 had the most positive effect on the label, which is the number of visits to a post. The factor "discount post content" with a positive sign and a coefficient of 5417.751 has had the most positive effect on the label, which is the liking of a post. The factor "discount post content" with a positive sign and a coefficient of 2164.016 has had the most positive effect on the label, which is the amount of comments on a post. Also, the type of image post with a regression coefficient of 565.153 and a negative sign in the investigation of factors affecting the number of comments shows that the use of video posts will increase the comments and interaction of customers. Conclusion Most of the researches conducted, such as Gkikas et al (2022), Torbarina, Jelenc & Brklja�i� (2020), Wahid & Gunarto (2022), etc., only investigated the influence of a few specific factors on the likes and comments of social media posts, and a comprehensive set of factors has not been investigated. Also, these factors were only for checking likes or opinions and not checking both cases. Most importantly, in the studies conducted, only the positive or negative impact of a factor on the number of likes and opinions has been discussed, and their importance has not been determined. In this research, various factors affecting the number of visits, likes and comments of social network posts were investigated. Also, the importance of each factor was determined. In addition, a decision tree model was presented to manage related pages and posts in order to achieve increased likes and comments. Based on the extracted effective factors, calculating the weight and importance of each factor and the created decision tree model, posts can be managed to increase the number of visits, likes and comments

Item type: Journal article (Paginated)
Keywords: Social Networks, E-Commerce, User Behavior Analysis, Data Mining.
Subjects: D. Libraries as physical collections. > DC. Public libraries.
Depositing user: HII Journal Human Information Interaction
Date deposited: 25 Feb 2026 17:52
Last modified: 25 Feb 2026 17:52
URI: http://hdl.handle.net/10760/47543

References

Abu-Shanab, E., Al-Sharafi, M. A., & Al-Emran, M. (2024). The influence of network externality

and fear of missing out on the continuous use of social networks: a cross-country comparison.

International Journal of Human–Computer Interaction, 40(15), 4058-4070.

https://doi.org/10.1080/10447318.2023.2208990

Aldous, K. K., An, J., & Jansen, B. J. (2019, July). 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). https://doi.org/10.1609/icwsm.v13i01.3208

AlRawashdeh, H., Shwedeh, F., & Abdallah, S. (2017, July). How post time and type affect user

engagement on public profiles in the Arab World. In Proceedings of the 2017 IEEE/ACM

International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 554-

561). https://doi.org/10.1145/3110025.3110108

Anand, K., Urolagin, S., & Mishra, R. K. (2021). How does hand gestures in videos impact social

media engagement-Insights based on deep learning. International Journal of Information

Management Data Insights, 1(2), 100036. https://doi.org/10.1016/j.jjimei.2021.100036

Antonakopoulou, S., & Veglis, A. (2021). How post time and post type affect the engagement on

Facebook: The case of a national media organization. Observatorio (OBS*), 15(4).

https://doi.org/10.15847/obsOBS15420211856

Dhanesh, G., Duthler, G., & Li, K. (2022). Social media engagement with organization-generated

content: Role of visuals in enhancing public engagement with organizations on Facebook and

Instagram. Public Relations Review, 48(2), 102174.

https://doi.org/10.1016/j.pubrev.2022.102174

Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., & Goodman, S. (2019). Social media

engagement behavior: A framework for engaging customers through social media content.

European journal of marketing, 53(10), 2213-2243. https://doi.org/10.1108/EJM-03-2017-

0182

Fu, G. (2005). Modeling water availability and its response to climatic change for the Spokane River

Watershed. Washington State University. https://hdl.handle.net/2376/413

Gkikas, D. C., Tzafilkou, K., Theodoridis, P. K., Garmpis, A., & Gkikas, M. C. (2022). How do text

characteristics impact user engagement in social media posts: Modeling content readability,

length, and hashtags number in Facebook. International Journal of Information Management

Data Insights, 2(1), 100067. https://doi.org/10.1016/j.jjimei.2022.100067

Harandi, A. O., & MirzaeianKhamseh, P. (2023). Explaining the Role of Company's Social Media

Marketing on Customer Engagement: Examining Brand Experience and Customer Age

Generation. , 2(3), 337-365. (in Persian) https://doi.org/10.22059/mmr.2024.369197.1071

Huang, J., Wang, C., Su, M., Dai, Q., & Bhuiyan, M. Z. A. (2018, October). Inspecting influences

on likes and comments of photos in instagram. In 2018 IEEE SmartWorld, Ubiquitous

Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing &

Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation

(SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 938-945). IEEE.

https://doi.org/10.1109/SmartWorld.2018.00168

Jaakonmäki, R., Müller, O., & Vom Brocke, J. (2017, January). The impact of content, context, and

creator on user engagement in social media marketing. In Proceedings of the Annual Hawaii

International Conference on System Sciences (Vol. 50, pp. 1152-1160). IEEE Computer Society

Press. https://doi.org/10125/41289

Joo, S., Lu, K., & Lee, T. (2020). Analysis of content topics, user engagement and library factors in

public library social media based on text mining. Online information review, 44(1), 258-277.

https://doi.org/10.1108/OIR-11-2018-0345

Kontsevaia, D. B., & Berger, P. D. (2017). Analyzing factors affecting the success of social media

posts for B2B networks: a fractional-factorial design approach. International Journal of

Business, Economics and Management, 4(6), 112-123.

https://doi.org/10.18488/journal.62.2017.46.112.123

Labbafi, S., Roshandel, T., & Mohammadi, D. (2018). Role of Social Network in marketing; use of

the Taylor and Okazaki framework (Case Study). Media Studies, 12(3), 103-114. (in Persian)

https://www.magiran.com/p1973516

Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on

social media: Evidence from Facebook. Management science, 64(11), 5105-5131.

https://doi.org/10.1287/mnsc.2017.2902

León-Alberca, T., Renés-Arellano, P., & Aguaded, I. (2024). Digital Marketing and Technology

Trends: Systematic Literature Review on Instagram. In International Conference On

Communication And Applied Technologies (pp. 309-318). Springer, Singapore.

https://doi.org/10.1007/978-981-99-7210-4_29

Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content

and social media engagement. Journal of marketing research, 57(1), 1-19.

https://doi.org/10.1177/0022243719881113

Misra, A., Dinh, T. D., & Ewe, S. Y. (2024). The more followers the better? The impact of food

influencers on consumer behaviour in the social media context. British Food Journal.

https://doi.org/10.1108/BFJ-01-2024-0096

Monacho, B. C., & Slamet, Y. U. L. I. U. S. (2023). The Effect of Influencer Engagement Rate in

Increasing Followers of Instagram Official Account. Jurnal Komunikasi: Malaysian Journal of

Communication, 39(2), 373-388. https://ejournal.ukm.my/mjc/issue/view/1605

Moon, S., & Yoo, S. (2022). Are More Followers Always Better? The Non-Linear Relationship

between the Number of Followers and User Engagement on Seeded Marketing Campaigns in

Instagram. Asia Marketing Journal, 24(2), 62-77. https://doi.org/10.53728/2765-6500.1589

Peng, Y., & Lu, L. (2024). Untangling influence: The effect of follower-followee comparison on

social media engagement. Journal of Retailing and Consumer Services, 78, 103747.

https://doi.org/10.1016/j.jretconser.2024.103747

Pourazad, N., Stocchi, L., & Narsey, S. (2023). A comparison of social media influencers’ KPI

patterns across platforms: Exploring differences in followers and engagement on Facebook,

Instagram, YouTube, TikTok, and Twitter. Journal of Advertising Research, 63(2), 139-159.

https://doi.org/10.2501/JAR-2023-008

Schultz, C. D. (2016, August). Driving likes, comments, and shares on social networking sites: how

post characteristics affect brand interactions in apparel retailing. In Proceedings of the 18th

Annual International Conference on Electronic Commerce: e-Commerce in Smart connected

World (pp. 1-9). https://doi.org/10.1145/2971603.2971612

Shahbaznezhad, H., Dolan, R., & Rashidirad, M. (2021). The role of social media content format and

platform in users’ engagement behavior. Journal of Interactive Marketing, 53(1), 47-65.

https://doi.org/10.1016/j.intmar.2020.05.001

Stepaniuk, K. (2015). The relation between destination image and social media user engagement–

theoretical approach. Procedia-Social and Behavioral Sciences, 213, 616-621.

https://doi.org/10.1016/j.sbspro.2015.11.459

Sun, Y., Jia, R., Razzaq, A., & Bao, Q. (2024). Social network platforms and climate change in

China: Evidence from TikTok. Technological Forecasting and Social Change, 200, 123197.

https://doi.org/10.1016/j.techfore.2023.123197

Tassawa, C. (2019). Factors influencing Facebook page posts’ likes, shares, and comments in sport

marketing. ABAC Journal, 39(3), 56-69.

www.assumptionjournal.au.edu/index.php/abacjournal/article/view/4201

Thomson, T. J., & Greenwood, K. (2017). I “like” that: Exploring the characteristics that promote

social media engagement with news photographs. Visual Communication Quarterly, 24(4), 203-

218. https://doi.org/10.1080/15551393.2017.1388701

Thorgren, E., Mohammadinodooshan, A., & Carlsson, N. (2024, May). Temporal Dynamics of User

Engagement on Instagram: A Comparative Analysis of Album, Photo, and Video Interactions.

In Proceedings of the 16th ACM Web Science Conference (pp. 224-234).

https://doi.org/10.1145/3614419.3644029

Torbarina, M., Jelenc, L., & Brkljačić, I. (2020). Increasing consumer engagement on social

networks: Social media influencer’s followers ‘like’to see a face in a post. Market-Tržište,

32(Special Issue), 67-81. https://doi.org/10.22598/mt/2020.32.spec-issue.67

Van Bavel, J. J., Robertson, C. E., Del Rosario, K., Rasmussen, J., & Rathje, S. (2024). Social media

and morality. Annual review of psychology, 75(1), 311-340. https://doi.org/10.1146/annurevpsych-022123-110258

Vassio, L., Garetto, M., Chiasserini, C., & Leonardi, E. (2021, November). Temporal dynamics of

posts and user engagement of influencers on Facebook and Instagram. In Proceedings of the

2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and

Mining (pp. 129-133). https://doi.org/10.1145/3487351.3488340

Vassio, L., Garetto, M., Leonardi, E., & Chiasserini, C. F. (2022). Mining and modelling temporal

dynamics of followers’ engagement on online social networks. Social Network Analysis and

Mining, 12(1), 96. https://doi.org/10.1007/s13278-022-00928-2

Wahid, R. M., & Gunarto, M. (2022). Factors driving social media engagement on Instagram:

Evidence from an emerging market. Journal of Global Marketing, 35(2), 169-191.

https://doi.org/10.1080/08911762.2021.1956665

Wies, S., Bleier, A., & Edeling, A. (2023). Finding goldilocks influencers: How follower count drives

social media engagement. Journal of Marketing, 87(3), 383-405.

https://doi.org/10.1177/00222429221125131

Xiang, H., Xie, M., & Fang, Y. (2024). Study on the architecture space-social network characteristics

based on social network analysis: A case study of Anshun Tunpu settlement. Ain Shams

Engineering Journal, 15(1), 102333. https://doi.org/10.1016/j.asej.2023.102333


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