skip to main content
10.1145/3614419.3644029acmconferencesArticle/Chapter ViewAbstractPublication PageswebsciConference Proceedingsconference-collections
research-article
Open access

Temporal Dynamics of User Engagement on Instagram: A Comparative Analysis of Album, Photo, and Video Interactions

Published: 21 May 2024 Publication History

Abstract

Despite Instagram being an integral part of many people’s lives, it is relatively less studied than many other platforms (e.g., Twitter and Facebook). Furthermore, despite offering diverse content formats for user expression and interaction, prior works have not studied the temporal dynamics of user engagement across albums, photos, and videos. To address this gap, we present a pioneering temporal comparative analysis that unveils nuanced patterns in user interactions across content types. Our analysis sheds light on interaction longevity and disparities among album, photo, and video engagement. Additionally, it offers empirical comparisons through statistical tests, examines contributing factors such as post and uploader characteristics, and analyzes content composition’s impact on user engagement. The findings reveal distinct temporal engagement patterns. Despite initial spikes in interactions post-upload, albums exhibit somewhat more sustained interest, while photos and videos have shorter engagement lifespans. Moreover, a consistent trend between shallow (likes) and deep (comments) interactions persists across content types. Notably, concise content, characterized by shorter descriptions and minimal hashtags/mentions, consistently drives higher engagement, emphasizing its relevance across all content formats. These insights deepen comprehension of temporal nuances in user engagement on Instagram, offering valuable guidance for content creators and marketers to tailor strategies that evoke immediate and sustained user interest.

Supplemental Material

MP4 File - PaperSession-4_Digital_Art_einzeln_Donnerstag_240605_ElinThorgren
Temporal Dynamics of User Engagement on Instagram: A Comparative Analysis of Album, Photo, and Video Interactions

References

[1]
[n. d.]. Starngage. https://starngage.com/plus/en-us. Accessed: 2023-03-31.
[2]
Kholoud Khalil Aldous, Jisun An, and Bernard J. Jansen. 2019. 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. 47–57.
[3]
A. Bakhshi, D. Shamma, and E. Gilbert. 2014. Faces engage us: Photos with faces attract more likes and comments on instagram. In Proc. CHI. 965–974.
[4]
Y. Borghol, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti. 2012. The untold story of the clones: Content-agnostic factors that impact YouTube video popularity. In Proc. ACM KDD. 1186–1194.
[5]
Y. Borghol, S. Mitra, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti. 2011. Characterizing and modelling popularity of user-generated videos. Performance Evaluation 68, 11 (2011), 1037–1055.
[6]
S. Carta, AS. Podda, DR. Recupero, R. Saia, and G. Usai. 2020. Popularity prediction of instagram posts. Information 11, 9 (2020), 453.
[7]
J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou. 2020. Retinaface: Single-shot multi-level face localisation in the wild. In Proc. IEEE/CVF CVPR. 5203–5212.
[8]
Olive Jean Dunn. 1964. Multiple comparisons using rank sums. Technometrics 6, 3 (1964), 241–252.
[9]
B. Efron. 1992. Bootstrap methods: another look at the jackknife. Springer.
[10]
E. Ferrara, R. Interdonato, and A. Tagarelli. 2014. Online popularity and topical interests through the lens of instagram. In Proc. ACM Conference on Hypertext and social media. 24–34.
[11]
K. Garimella and R. West. 2021. Evolution of Retweet Rates in Twitter User Careers: Analysis and Model. In Proc. AAAI ICWSM, Vol. 15. 1064–1068.
[12]
M. Gayberi and S.G. Oguducu. 2019. Popularity prediction of posts in social networks based on user, post and image features. In Proc. International Conference on Management of Digital EcoSystems. 9–15.
[13]
K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proc. IEEE CVPR. 770–778.
[14]
G. Huang, M. Mattar, T. Berg, and E. Learned-Miller. 2008. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in’Real-Life’Images: detection, alignment, and recognition.
[15]
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, 2017. Speed/accuracy trade-offs for modern convolutional object detectors. In Proc. IEEE CVPR. 7310–7311.
[16]
J. Huang, C. Wang, M. Su, Q. Dai, and MZA. Bhuiyan. 2018. Inspecting influences on likes and comments of photos in instagram. In Proc. IEEE SmartWorld. 938–945.
[17]
JY. Jang, K. Han, and D. Lee. 2015. No reciprocity in" liking" photos: analyzing like activities in instagram. In Proc. ACM conference on hypertext and social media. 273–282.
[18]
JY. Jang, K. Han, D. Lee, H. Jia, and P. Shih. 2016. Teens engage more with fewer photos: temporal and comparative analysis on behaviors in instagram. In Proc. ACM Conference on hypertext and social media. 71–81.
[19]
Cheonsoo Kim and Sung-Un Yang. 2017. Like, comment, and share on Facebook: How each behavior differs from the other. Public Relations Review 43, 2 (2017), 441–449.
[20]
W.H. Kruskal and W.A. Wallis. 1952. Use of ranks in one-criterion variance analysis. Journal of the American statistical Association 47, 260 (1952), 583–621.
[21]
TY. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie. 2017. Feature pyramid networks for object detection. In Proc. IEEE CVPR. 2117–2125.
[22]
Annukka K. Lindell. 2019. Left cheek poses garner more likes: the effect of pose orientation on Instagram engagement. Laterality 24, 5 (2019), 600–613. https://doi.org/10.1080/1357650X.2018.1556278 30526363.
[23]
H.B. Mann and D.R. Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics (1947), 50–60.
[24]
M. Mazloom, R. Rietveld, S. Rudinac, M. Worring, and W. Van Dolen. 2016. Multimodal popularity prediction of brand-related social media posts. In Proc. ACM Multimedia. 197–201.
[25]
A. Mohammadinodooshan and N. Carlsson. 2023. Effects of Political Bias and Reliability on Temporal User Engagement with News Articles Shared on Facebook. In Proc. PAM. Springer, 160–187.
[26]
O. Parkhi, A. Vedaldi, and A. Zisserman. 2015. Deep face recognition. British Machine Vision Association (2015).
[27]
Jürgen Pfeffer, Daniel Matter, and Anahit Sargsyan. 2023. The Half-Life of a Tweet. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 17. 1163–1167.
[28]
V. Pham, C. Pham, and T. Dang. 2020. Road damage detection and classification with detectron2 and faster R-CNN. In Proc. IEEE Big Data. 5592–5601.
[29]
Arthi Ramachandran, Lucy Wang, and Augustin Chaintreau. 2018. Dynamics and Prediction of Clicks on News from Twitter. In Proceedings of the 29th on Hypertext and Social Media (Baltimore, MD, USA) (HT ’18). Association for Computing Machinery, New York, NY, USA, 210–214. https://doi.org/10.1145/3209542.3209568
[30]
F. Sabate, J. Berbegal-Mirabent, A. Cañabate, and P. Lebherz. 2014. Factors influencing popularity of branded content in Facebook fan pages. European management journal 32, 6 (2014), 1001–1011.
[31]
S. Serengil. [n. d.]. DeepFace: A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python. https://github.com/serengil/deepface. Accessed 2023-05-22.
[32]
S. Serengil and A. Ozpinar. 2021. Hyperextended lightface: A facial attribute analysis framework. In Proc. International Conference on Engineering and Emerging Technologies. 1–4.
[33]
CrowdTangle Team. [n. d.]. CrowdTangle. https://www.crowdtangle.com/. Accessed: 2023-03-01.
[34]
Pier Paolo Tricomi, Marco Chilese, Mauro Conti, and Ahmad-Reza Sadeghi. 2023. Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms. In Proceedings of the 15th ACM Web Science Conference 2023 (Austin, TX, USA) (WebSci ’23). Association for Computing Machinery, New York, NY, USA, 346–356. https://doi.org/10.1145/3578503.3583623
[35]
L. Vassio, M. Garetto, C. Chiasserini, and E. Leonardi. 2021. Temporal dynamics of posts and user engagement of influencers on facebook and instagram. In Proc. IEEE/ACM ASONAM. 129–133.
[36]
Luca Vassio, Michele Garetto, Emilio Leonardi, and Carla Fabiana Chiasserini. 2022. Mining and modelling temporal dynamics of followers’ engagement on online social networks. Social Network Analysis and Mining 12, 1 (2022), 96.
[37]
K. Wang, P. Wang, X. Chen, Q. Huang, Z. Mao, and Y. Zhang. 2020. A feature generalization framework for social media popularity prediction. In Proc. ACM Multimedia. 4570–4574.
[38]
Lior Wolf, Tal Hassner, and Itay Maoz. 2011. Face recognition in unconstrained videos with matched background similarity. In Proc. IEEE CVPR. 529–534.
[39]
Y. Wu, A. Kirillov, F. Massa, WY. Lo, and R. Girshick. 2019. Detectron2. https://github.com/facebookresearch/detectron2.
[40]
Z. Zhang, T. Chen, Z. Zhou, J. Li, and J. Luo. 2018. How to become Instagram famous: Post popularity prediction with dual-attention. In Proc. IEEE Big Data. 2383–2392.
[41]
A. Zohourian, H. Sajedi, and A. Yavary. 2018. Popularity prediction of images and videos on Instagram. In Proc. International Conference on Web Research. 111–117.

Index Terms

  1. Temporal Dynamics of User Engagement on Instagram: A Comparative Analysis of Album, Photo, and Video Interactions

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      WEBSCI '24: Proceedings of the 16th ACM Web Science Conference
      May 2024
      395 pages
      This work is licensed under a Creative Commons Attribution International 4.0 License.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 21 May 2024

      Check for updates

      Author Tags

      1. Album
      2. Instagram
      3. Interactions
      4. Photo
      5. Temporal dynamics
      6. User engagement
      7. Video

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Data Availability

      PaperSession-4_Digital_Art_einzeln_Donnerstag_240605_ElinThorgren: Temporal Dynamics of User Engagement on Instagram: A Comparative Analysis of Album, Photo, and Video Interactions https://dl.acm.org/doi/10.1145/3614419.3644029#PaperSession-4_Digital_Art_einzeln_Donnerstag_240605_ElinThorgren.mp4

      Conference

      Websci '24
      Sponsor:
      Websci '24: 16th ACM Web Science Conference
      May 21 - 24, 2024
      Stuttgart, Germany

      Acceptance Rates

      Overall Acceptance Rate 245 of 933 submissions, 26%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 154
        Total Downloads
      • Downloads (Last 12 months)154
      • Downloads (Last 6 weeks)61
      Reflects downloads up to 22 Sep 2024

      Other Metrics

      Citations

      View Options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Get Access

      Login options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media