Customer Engagement Prediction on Social Media: A Graph Neural Network Method
Published Online:27 Sep 2024https://doi.org/10.1287/isre.2021.0281
References
- (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
- (2010) Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy. Inform. Sci. 180(9):1555–1570.Crossref, Google Scholar
- (2019) Predicting audience engagement across social media platforms in the news domain. Social Informatics 11th Internat. Conf. SocInfo 2019 Proc. 11 (Springer, Berlin, Heidelberg), 173–187.Google Scholar
- (2019) Customer behavior analysis using real-time data processing: A case study of digital signage-based online stores. Asia Pacific J. Marketing Logist. 31(1):265–290.Crossref, Google Scholar
- (1999) A dynamic model of purchase timing with application to direct marketing. J. Amer. Statist. Assoc. 94(446):365–374.Crossref, Google Scholar
- (2016) Motivations to interact with brands on Facebook—Toward a typology of consumer–brand interactions. J. Brand Management 23(2):153–178.Crossref, Google Scholar
- (2015) Generating brand awareness in online social networks. Comput. Human Behav. 50:600–609.Crossref, Google Scholar
- (1998) A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. Marketing Sci. 17(1):45–65.Link, Google Scholar
- (2009) Cumulative gains model quality metric. J. Appl. Math. Decision Sci. 2009:1–14.Crossref, Google Scholar
- (2009) Handling class imbalance in customer churn prediction. Expert Systems Appl. 36(3):4626–4636.Crossref, Google Scholar
- (2011) A dynamic decision support system to predict the value of customer for new product development. Decision Support Systems 52(1):178–188.Crossref, Google Scholar
- (2012) Why you are more engaged: Factors influencing twitter engagement in occupy wall street. Proc. Internat. AAAI Conf. Web Social Media, vol. 6 (AAAI Press, Palo Alto, CA), 423–426.Google Scholar
- (2019) A novel social recommendation method fusing user’s social status and homophily based on matrix factorization techniques. IEEE Access 7:18783–18798.Crossref, Google Scholar
- (2016) Mining brand perceptions from twitter social networks. Marketing Sci. 35(3):343–362.Link, Google Scholar
- (2023) Chataug: Leveraging ChatGPT for text data augmentation. Preprint, submitted February 25, https://arxiv.org/abs/2302.13007.Google Scholar
- (2014) Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. J. Brand Management 21(6):495–515.Crossref, Google Scholar
- (2017) Consumers’ social media brand behaviors: Uncovering underlying motivators and deriving meaningful consumer segments. Psych. Marketing 34(5):580–592.Crossref, Google Scholar
- Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. Proc. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 135–144.Google Scholar
- Dor LE, Halfon A, Kantor Y, Levy R, Mass Y, Rinott R, Shnarch E, Slonim N (2018) Semantic relatedness of Wikipedia concepts–Benchmark data and a working solution. Proc. Eleventh Internat. Conf. Language Resources Evaluation (LREC 2018) (European Language Resources Association (ELRA), Miyazaki, Japan).Google Scholar
- (2000) Moviemod: An implementable decision-support system for prerelease market evaluation of motion pictures. Marketing Sci. 19(3):226–243.Link, Google Scholar
- (2003) You are what they eat: The influence of reference groups on consumers’ connections to brands. J. Consumer Psych. 13(3):339–348.Crossref, Google Scholar
- (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. Proc. Web Conf. 2020 (ACM, New York), 2331–2341.Google Scholar
- (2023) Chatgpt outperforms crowd workers for text-annotation tasks. Proc. Natl. Acad. Sci. USA 120(30):e2305016120.Google Scholar
- (2020) Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view. Proc. 43rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 79–88.Google Scholar
- (2021) Let’s give them something to talk about: Which social media engagements predict purchase frequency? J. Interactive Marketing 56(1):83–95.Crossref, Google Scholar
- (2017) Inductive representation learning on large graphs. Adv. Neural Inform. Processing Systems 17:1024–1034.Google Scholar
- (2015) Mining the change of customer behavior in dynamic markets. Inform. Tech. Management 16(2):117–138.Crossref, Google Scholar
- (2016) The influence of social media interactions on consumer–brand relationships: A three-country study of brand perceptions and marketing behaviors. Internat. J. Res. Marketing 33(1):27–41.Crossref, Google Scholar
- (2013) The impact of user interactions in social media on brand awareness and purchase intention: The case of mini on Facebook. J. Production Brand Management 22(5/6):342–351.Crossref, Google Scholar
- (2009) Measuring campaign performance by using cumulative gain and lift chart. SAS Global Forum 19.Google Scholar
- (2019) Attention is not explanation. Preprint, submitted February 26, https://arxiv.org/abs/1902.10186.Google Scholar
- (2015) Feature-based approaches to semantic similarity assessment of concepts using Wikipedia. Inform. Processing Management 51(3):215–234.Crossref, Google Scholar
- (2013) Advertising and consumers’ communications. Marketing Sci. 32(2):294–309.Link, Google Scholar
- (2012) Increasing the ROI of social media marketing. MIT Sloan Management Rev. 54(1):54115.Google Scholar
- (2019) Predicting dynamic embedding trajectory in temporal interaction networks. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1269–1278.Google Scholar
- (2021) How to catch customers’ attention? A study on the effectiveness of brand social media strategies in digital customer engagement. Frontiers Psych. 12:800766.Crossref, Google Scholar
- (2001) Birds of a feather: Homophily in social networks. Annual Rev. Sociol. 27(1):415–444.Crossref, Google Scholar
- Mei X, Meng C, Liu H, Kong Q, Ko T, Zhao C, Plumbley MD, Zou Y, Wang W (2024) Wavcaps: A ChatGPT-assisted weakly-labelled audio captioning dataset for audio-language multimodal research. IEEE/ACM Trans. Audio Speech Language Processing 32:3339–3354.Google Scholar
- (2012) Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. J. Marketing 76(6):105–120.Crossref, Google Scholar
- (2017) Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Inform. Management 54(1):25–37.Crossref, Google Scholar
- (1992) A catastrophe model for developing service satisfaction strategies. J. Marketing 56(3):83–95.Crossref, Google Scholar
- (2016) Examining context-specific social media marketing strategies. Asia Pacific J. Marketing Logist. 26(1):143–162.Google Scholar
- (2014) Glove: Global vectors for word representation. 2014 Conf. Empirical Methods Natural Language Processing (EMNLP) (Association for Computational Linguistics, Doha, Qatar), 1532–1543.Google Scholar
- (2014) Say yes to Facebook and get your customers involved! Relationships in a world of social networks. Bus. Horizons 57(6):695–702.Crossref, Google Scholar
- (2014) Deepwalk: Online learning of social representations. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 701–710.Google Scholar
- (2008) An evolving information system based on data mining knowledge to support customer relationship management. 2008 IEEE Sympos. Adv. Management Inform. Globalized Enterprises (AMIGE) (IEEE, Piscataway, NJ), 1–5.Google Scholar
- (2014) The influence of content type of Facebook messages on the effectiveness of the message “and the moderating role of consumer brand relationship”. Master’s thesis, University of Twente, Enschede, Netherlands.Google Scholar
- (2018) Semantic concept model using Wikipedia semantic features. J. Inform. Sci. 44(4):526–551.Crossref, Google Scholar
- (2015) The development of Facebook’s competitive advantage for brand awareness. Procedia Econom. Finance 24:589–597.Crossref, Google Scholar
- (2020) Customer engagement in social media: A framework and meta-analysis. J. Acad. Marketing Sci. 48(6):1211–1228.Crossref, Google Scholar
- (2018) Modeling relational data with graph convolutional networks. Eur. Semantic Web Conf. (Springer-Verlag, Berlin, Heidelberg), 593–607.Google Scholar
- (2022) Predicting stages in omnichannel path to purchase: A deep learning model. Inform. Systems Res. 33(2):429–445.Link, Google Scholar
- (2020) Trade-offs in online advertising: Advertising effectiveness and annoyance dynamics across the purchase funnel. Inform. Systems Res. 31(1):102–125.Link, Google Scholar
- (2021) Social media brand posts and customer engagement. J. Brand Management 28(6):685–699.Crossref, Google Scholar
- (2019) Heterogeneous graph attention network. World Wide Web Conf. (ACM, New York), 2022–2032.Google Scholar
- (2014) A survey of collaborative filtering based social recommender systems. Comput. Comm. 41:1–10.Crossref, Google Scholar
- (2023) Can ChatGPT reproduce human-generated labels? A study of social computing tasks. Preprint, submitted April 20, https://arxiv.org/abs/2304.10145.Google Scholar

