ABSTRACT
What made you want to wear the clothes you are wearing? Where is the place you want to visit for your next-coming holiday? Why do you like the music you frequently listen to? If you are like most people, you probably made these decisions as a result of watching influencers on social media. Furthermore, influencer marketing is an opportunity for brands to take advantage of social media using a well-defined and well-designed social media marketing strategy. However, choosing the right influencers is not an easy task. With more people gaining an increasing number of followers in social media, finding the right influencer for an E-commerce company becomes paramount. In fact, most marketers cite it as a top challenge for their brands. To address the aforementioned issues, we proposed a data-driven micro-influencer ranking scheme to solve the essential question of finding out the right micro-influencer. Specifically, we represented brands and influencers by fusing their historical posts' visual and textual information. A novel k-buckets sampling strategy with a modified listwise learning to rank model were proposed to learn a brand-micro-influncer scoring function. In addition, we developed a new Instagram brand micro-influencer dataset, consisting of 360 brands and 3,748 micro-influencers, which can benefit future researchers in this area. The extensive evaluations demonstrate the advantage of our proposed method compared with the state-of-the-art methods.
- Christy Ashley and Tracy Tuten. 2015. Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychology & Marketing , Vol. 32, 1 (2015), 15--27.Google ScholarCross Ref
- Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to rank: from pairwise approach to listwise approach. In Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML), Corvallis, Oregon, USA. 129--136.Google Scholar
- Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. 2012. SVDFeature: a toolkit for feature-based collaborative filtering. Journal of Machine Learning Research , Vol. 13, Dec (2012), 3619--3622.Google ScholarDigital Library
- Eun-Kyong Choi, Deborah Fowler, Ben Goh, and Jingxue Yuan. 2016. Social media marketing: applying the uses and gratifications theory in the hotel industry. Journal of Hospitality Marketing & Management , Vol. 25, 7 (2016), 771--796.Google ScholarCross Ref
- Van Dang, Michael Bendersky, and W Bruce Croft. [n. d.]. Two-Stage Learning to Rank for Information Retrieval. ([n. d.]).Google Scholar
- Marijke De Veirman, Veroline Cauberghe, and Liselot Hudders. 2017. Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International Journal of Advertising , Vol. 36, 5 (2017), 798--828.Google ScholarCross Ref
- Laurence Dessart, Cleopatra Veloutsou, and Anna Morgan-Thomas. 2015. Consumer engagement in online brand communities: a social media perspective. Journal of Product & Brand Management , Vol. 24, 1 (2015), 28--42.Google ScholarCross Ref
- Nathaniel J Evans, Joe Phua, Jay Lim, and Hyoyeun Jun. 2017. Disclosing Instagram influencer advertising: The effects of disclosure language on advertising recognition, attitudes, and behavioral intent. Journal of Interactive Advertising , Vol. 17, 2 (2017), 138--149.Google ScholarCross Ref
- Aleksandr Farseev, Kirill Lepikhin, Kenny Powar, Eu Khoon Ang, and Hendrik Schwartz. 2018. SoMin. ai: Social Multimedia Influencer Discovery Marketplace.. In ACM Multimedia Conference on Multimedia Conference, Demo .Google Scholar
- Reto Felix, Philipp A Rauschnabel, and Chris Hinsch. 2017. Elements of strategic social media marketing: A holistic framework. Journal of Business Research , Vol. 70 (2017), 118--126.Google ScholarCross Ref
- Francesco Gelli, Tiberio Uricchio, Marco Bertini, Alberto Del Bimbo, and Shih-Fu Chang. 2015. Image popularity prediction in social media using sentiment and context features. In Proceedings of the 23rd ACM international conference on Multimedia. ACM, 907--910.Google ScholarDigital Library
- Francesco Gelli, Tiberio Uricchio, Xiangnan He, Alberto Del Bimbo, and Tat-Seng Chua. 2018. Beyond the Product: Discovering Image Posts for Brands in Social Media. In ACM Multimedia Conference on Multimedia Conference. 465--473.Google ScholarDigital Library
- Morgan Glucksman. 2017. The rise of social media influencer marketing on lifestyle branding: A case study of Lucie Fink. Elon Journal of Undergraduate Research in Communications , Vol. 8, 2 (2017), 77--87.Google Scholar
- Khim-Yong Goh, Cheng-Suang Heng, and Zhijie Lin. 2013. Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Information Systems Research , Vol. 24, 1 (2013), 88--107.Google ScholarCross Ref
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 173--182.Google ScholarDigital Library
- Linda D Hollebeek, Mark S Glynn, and Roderick J Brodie. 2014. Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of interactive marketing , Vol. 28, 2 (2014), 149--165.Google ScholarCross Ref
- Linus Juhlin and Miretta Soini. 2018. How do influencer marketers affect brand associations?: a semiotic Instagram study in the sports fashion industry.Google Scholar
- Jin-Hwa Kim, Kyoung Woon On, Woosang Lim, Jeonghee Kim, Jung-Woo Ha, and Byoung-Tak Zhang. 2017. Hadamard Product for Low-rank Bilinear Pooling. In International Conference on Learning Representations, ICLR, Toulon, France, Conference Track Proceedings .Google Scholar
- Hang Li. 2011. A short introduction to learning to rank. IEICE TRANSACTIONS on Information and Systems , Vol. 94, 10 (2011), 1854--1862.Google ScholarCross Ref
- Yung-Ming Li, Cheng-Yang Lai, and Ching-Wen Chen. 2011. Discovering influencers for marketing in the blogosphere. Information Sciences (2011), 5143--5157.Google Scholar
- Tie-Yan Liu et almbox. 2009a. Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval , Vol. 3, 3 (2009), 225--331.Google Scholar
- Tie-Yan Liu et almbox. 2009b. Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval , Vol. 3, 3 (2009), 225--331.Google Scholar
- Tianyi Luo, Dong Wang, Rong Liu, and Yiqiao Pan. 2015. Stochastic Top-k ListNet. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 676--684.Google ScholarCross Ref
- Rakesh Mallipeddi, Subodha Kumar, Chelliah Sriskandarajah, and Yunxia Zhu. 2018. A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers. Fox School of Business Research Paper 18-042 (2018).Google Scholar
- Masoud Mazloom, Robert Rietveld, Stevan Rudinac, Marcel Worring, and Willemijn Van Dolen. 2016. Multimodal popularity prediction of brand-related social media posts. In Proceedings of the 24th ACM international conference on Multimedia. ACM, 197--201.Google ScholarDigital Library
- Nina Michaelidou, Nikoletta Theofania Siamagka, and George Christodoulides. 2011. Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands. Industrial marketing management , Vol. 40, 7 (2011), 1153--1159.Google Scholar
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In International Conference on Learning Representations, ICLR, Scottsdale, Arizona, USA, Workshop Track Proceedings . http://arxiv.org/abs/1301.3781Google Scholar
- Stefan Olof Lagrosen and Kerstin Grundén. 2014. Social media marketing in the wellness industry. The TQM Journal , Vol. 26, 3 (2014), 253--260.Google ScholarCross Ref
- Kristy Sammis, Cat Lincoln, and Stefania Pomponi. 2015. Influencer marketing for dummies .John Wiley & Sons.Google Scholar
- Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web. ACM, 111--112.Google ScholarDigital Library
- Noam Segev, Noam Avigdor, and Eytan Avigdor. 2018. Measuring influence on Instagram: a network-oblivious approach. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 1009--1012.Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations, ICLR, San Diego, CA, USA, Conference Track Proceedings . http://arxiv.org/abs/1409.1556Google Scholar
- Tracy L Tuten and Michael R Solomon. 2017. Social media marketing .Sage.Google Scholar
- Chad Witkemper, Choong Hoon Lim, and Adia Waldburger. 2012. Social media and sports marketing: Examining the motivations and constraints of Twitter users. Sport Marketing Quarterly , Vol. 21, 3 (2012).Google Scholar
- Steven Woods. 2016 (accessed April 1, 2019). #Sponsored: The Emergence of Influencer Marketing . https://trace.tennessee.edu/utk_chanhonoproj/1976/Google Scholar
- Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008. Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning. ACM, 1192--1199.Google ScholarDigital Library
- Matthew D. Zeiler and Rob Fergus. 2013. Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. In International Conference on Learning Representations, ICLR, Scottsdale, Arizona, USA, Conference Track Proceedings .Google Scholar
- Nathalie Zietek. 2016. Influencer Marketing: the characteristics and components of fashion influencer marketing.Google Scholar
Recommendations
Foundations of consumer engagement with social media influencers
This article is a systematic review of recent and relevant journal articles on consumer engagement with social media influencers using the PRISMA protocol. The review in this article reveals several noteworthy findings pertaining to the factors (...
Product Placements by Micro and Macro Influencers on Instagram
Social Computing and Social Media. Communication and Social CommunitiesAbstractInfluencer marketing is considered one of the most promising marketing strategies in the age of digital transformation of media. The social platform Instagram offers a huge opportunity for companies to market their products and services through ...
Asymmetric Promotion Effects and Brand Positioning
Several studies have shown that promotions of national brands yield more effect than those of store brands e.g., Allenby and Rossi 1991, Blattberg and Wisniewski 1989. However, the evolution of price-quality data available from Consumer Reports over the ...
Comments