Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach

By Sérgio Moro, Paulo Rita, Bernardo Vala. 2016

218 citations recorded by the source corpus.

Subfields: Information Systems and Management, Marketing, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

In Marketing

In Sociology and Political Science

Citations in the corpus

Athlete branding via social media: examining the factors influencing consumer engagement on Instagram (2020, unknown): “A data mining approach (cf. Moro et al., 2016) may be beneficial to tease out the impact of these posting behaviours, while employing mixed-methods research designs in subsequent investigations could also provide a richer understanding of why fans do, and do not, engage with athlete Instagram posts.”
Social media in marketing: A review and analysis of the existing literature (2017, background): “…(e.g. Hoffman and Fodor, 2010; Kim et al., 2015; Okazaki and Taylor, 2013; Rathore et al., 2016; Saxena and Khanna, 2013; Zeng and Gerritsen, 2014; Felix et al., 2016; Hofacker and Belanche, 2016; Anandaa et al., 2016; Wu, 2016; Gulbahar and Yildirim, 2016; Movsisyan, 2016; Yadav et al., 2016).”
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning (2018, unknown): “Linear regression is performed on Facebook metrics dataset [22], which has 500 samples with multiple attributes related to posts published on a cosmetics brand.”
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance (2020, background): “Dataset p n Abalone Age [abalone] Waugh (1995) 8 4177 Bike Sharing [bike] Fanaee-T and Gama (2014) 11 731 Bioston Housing [boston] Harrison Jr and Rubinfeld (1978) 13 506 Concrete Compressive Strength [concrete] Yeh (1998) 8 1030 CPU Performance [cpu] Ein-Dor and Feldmesser (1987) 7 209 Conventional”
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis (2018, background): “Social etworks such as Facebook and Twitter are having a huge impact on nfluencing customers’ decisions, leading organizations and brands o incorporate information originated in such platforms in their arketing solutions (Moro et al., 2016).”