The DOI matches its Crossref record; 86 of the 90 references it cites were found in a registry or an index.
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Open accessAbstract
We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as Likes, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers’ path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook’s EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook’s behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality–related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2902 . This paper was accepted by Chris Forman, information systems.
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“The algorithms do this by monitoring the amount of time particular kinds of content keep the particular user engaged with the platform, and they use that data to continuously adjust the content so that the particular user remains engaged with the platform for ever lengthening periods of time”
From Ethics of the Attention Economy: The Problem of Social Media Addiction (2021) · cites this paper as Lee, Hosanagar, & Nair, 2018
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Conceptualizing, Measuring, and Managing Customer-Based Brand Equity
Keller, K. L. (1993) · Journal of Marketing · doi:10.2307/1252054 · Cited by 14,763
Generalized Additive Models: An Introduction with R
Wood, S. N. (2017) · doi:10.1201/9781315370279 · Cited by 13,852
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691610393980 · Cited by 9,996
Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Dimensions of Brand Personality
Aaker, J. L. (1997) · Journal of Marketing Research · doi:10.2307/3151897 · Cited by 7,843
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4 of the 90 references couldn’t be matched to a known paper.
Research works printed but not linked: 10
- 10.1177/002224379703400304 · DOI
- Bagwell K, 2007, Handbook of Industrial Organization, 3, 1701
- Creamer M, 2012, AdAge.
- Ellickson PB, 2010, Quant. Marketing Econom., 10, 1
- 10.1177/002224299305700101 · DOI
- Liu X, Lee D, Srinivasan K (2017) Large scale cross category analysis of consumer review content on sales conversion leveraging deep learning. Working Paper 16-09, NET Institute, New York.
- Malik HH, 2011, Proc. 20th ACM Internat. Conf. Inform. Knowledge Management, 2497
- 10.2307/1250490 · DOI
- Terlep S, 2012, Wall Street Journal
- Weiss M, 2000, The Value of Brand Personalities: The Phenomenon of the Strategic Positioning of Brands
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Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102168 · Cited by 4,608
“Chen and Lee (2018) investigated the use of Snapchat for social media marketing while targeting young consumers.”
BackgroundA strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“At the feeling level, more real-time and accurate emotion sensing from posted messages can better engage customers and provide a better interaction experience (Hartmann et al. 2019; Lee et al. 2018).”
BackgroundMethodEngaged to a Robot? The Role of AI in Service
Huang, M.-H., & Rust, R. T. (2021) · Journal of Service Research · doi:10.1177/1094670520902266 · Cited by 1,185
The Role of Social Media Content Format and Platform in Users’ Engagement Behavior
Shahbaznezhad, H., Dolan, R., & Rashidirad, M. (2021) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2020.05.001 · Cited by 618
Integrating Marketing Communications: New Findings, New Lessons, and New Ideas
Batra, R., & Keller, K. L. (2016) · Journal of Marketing · doi:10.1509/jm.15.0419 · Cited by 836
Metadata
- Authors
- Lee, D., Hosanagar, K., & Nair, H. S.
- Affiliations
- Carnegie Mellon University (US)
- University of Pennsylvania (US)
- Stanford University (US)
- Venue
- Management Science
- Published
- 2018-11
- Volume
- 64
- Issue
- 11
- Pages
- 5105-5131
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W3125510825
- Semantic Scholar
- 49ae2abdba289d0823cf3440b4320a76e9a580c0
- Cited by
- 1,069
- References
- 90
- Citations by year
- 2026: 109 · 2025: 141 · 2024: 155 · 2023: 138 · 2022: 158 · 2021: 146 · 2020: 87 · 2019: 69 · 2018: 29
- FWCI
- 131.60
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Management Science (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- ScholarlyCommons (University of Pennsylvania) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- social media advertising, advertising content, customer engagement, Facebook, brand personality, humor, emotions, conversion, behavioral targeting, natural language processing
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