Business Relationship Network Model from Social Reactions Data
Resumo
One of the primary ways to expand a business or to keep it stable during a crisis is to create partnerships with other companies. With that, this study presents results regarding a new data model, which explores user reactions on social media to indicate strategic business partnerships. Th ere are three main contributions of this study to the literature: (i) a business relationship network model; (ii) a business community detection algorithm; and (iii) a business outlier detection algorithm. The evaluation of the contributions was performed exploring real data of approximately 280 million user reactions on Facebook. Results suggest that business partnership recommendation is possible using the information available in social media.
Referências
WH Bergquist, J. Betwee, D Meuel (1995). Building strategic relationships: How to extend your organization’s reach through partnerships, alliances, and joint ventures. San Francisco, USA: Jossey-Bass Publishers.
D. Elmuti, Y. Kathawala (2001). An overview of strategic alliances. Management decision, MCB UP Ltd, v. 39, n. 3, p. 205–218.
K. J. Trainor et al. (2014) Social media technology usage and customer relationship performance: A capabilities-based examination of social crm. Journal of Business Research, Elsevier, v. 67, n. 6, p. 1201–1208.
R. Agnihotri, et al. (2016) Social media: Influencing customer satisfaction in b2b sales. Industrial Marketing Management, Elsevier, v. 53, p. 172–180.
S. Hudson, K. Th al (2013). The impact of social media on the consumer decision process: Implications for tourism marketing. Journal of Travel & Tourism Marketing, Taylor & Francis, v. 30, n. 1-2, p. 156–160.
J. Cranshaw, et al (2012). The livehoods project: Utilizing social media to understand the dynamics of a city. In: Proc. of ICWSM’12. Dublin, Ireland.
T. H. Silva et al (2014). You are what you eat (and drink): Identifying cultural boundaries by analyzing food and drink habits in foursquare. In: Proc. of ICWSM’14. Ann Arbor, USA.
W. Mueller, et al. (2017) Gender matters! analyzing global cultural gender preferences for venues using social sensing. EPJ Data Science, v. 6, n. 1, p. 5.
S. Brito, et al. (2018) Cheers to untappd! preferences for beer reflect cultural differences around the world. In: Proc. of AMCIS’18. New Orleans, USA.
J. Lin et al. (2016) Where is the goldmine?: Finding promising business locations through facebook data analytics. In: ACM. Proc. of Hypertext’16. Halifax, Canada, p. 93–102.
D. Karamshuk, et al. (2013) Geo-spotting: Mining online location -based services for optimal retail store placement. In: Proc. of ACM KDD’13. Chicago, Illinois, USA. p. 793–801. ISBN 978-1-4503-2174-7.
D. L. Hoffman, M . Fodor (2010). Can you measure the roi of your social media marketing? MIT Sloan Management Review, Massachusetts Institute of Technology, Cambridge, MA, v. 52, n. 1, p. 41.
T. L. Tuten,; M. R. Solomon (2017). Social media marketing. Thousand Oaks, CA, USA: Sage.
P.N. Tan, M. Steinbach, V. Kumar (2005). Introduction to data mining. 1st. ISBN 0-321-32136-7
U. N. Raghavan, R. Albert, S. Kumara (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical review E, APS, v. 76, n. 3, p. 036106.
D.P. Tsutsumi, A.T. Fenerich, T. H. Silva (2018). Identificando a relação virtual entre empresas explorando reações de usuários no facebook. In: Proc. of CoUrb’18. Campos do Jordão, Brazil.
D. Elmuti, Y. Kathawala (2001). An overview of strategic alliances. Management decision, MCB UP Ltd, v. 39, n. 3, p. 205–218.
K. J. Trainor et al. (2014) Social media technology usage and customer relationship performance: A capabilities-based examination of social crm. Journal of Business Research, Elsevier, v. 67, n. 6, p. 1201–1208.
R. Agnihotri, et al. (2016) Social media: Influencing customer satisfaction in b2b sales. Industrial Marketing Management, Elsevier, v. 53, p. 172–180.
S. Hudson, K. Th al (2013). The impact of social media on the consumer decision process: Implications for tourism marketing. Journal of Travel & Tourism Marketing, Taylor & Francis, v. 30, n. 1-2, p. 156–160.
J. Cranshaw, et al (2012). The livehoods project: Utilizing social media to understand the dynamics of a city. In: Proc. of ICWSM’12. Dublin, Ireland.
T. H. Silva et al (2014). You are what you eat (and drink): Identifying cultural boundaries by analyzing food and drink habits in foursquare. In: Proc. of ICWSM’14. Ann Arbor, USA.
W. Mueller, et al. (2017) Gender matters! analyzing global cultural gender preferences for venues using social sensing. EPJ Data Science, v. 6, n. 1, p. 5.
S. Brito, et al. (2018) Cheers to untappd! preferences for beer reflect cultural differences around the world. In: Proc. of AMCIS’18. New Orleans, USA.
J. Lin et al. (2016) Where is the goldmine?: Finding promising business locations through facebook data analytics. In: ACM. Proc. of Hypertext’16. Halifax, Canada, p. 93–102.
D. Karamshuk, et al. (2013) Geo-spotting: Mining online location -based services for optimal retail store placement. In: Proc. of ACM KDD’13. Chicago, Illinois, USA. p. 793–801. ISBN 978-1-4503-2174-7.
D. L. Hoffman, M . Fodor (2010). Can you measure the roi of your social media marketing? MIT Sloan Management Review, Massachusetts Institute of Technology, Cambridge, MA, v. 52, n. 1, p. 41.
T. L. Tuten,; M. R. Solomon (2017). Social media marketing. Thousand Oaks, CA, USA: Sage.
P.N. Tan, M. Steinbach, V. Kumar (2005). Introduction to data mining. 1st. ISBN 0-321-32136-7
U. N. Raghavan, R. Albert, S. Kumara (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical review E, APS, v. 76, n. 3, p. 036106.
D.P. Tsutsumi, A.T. Fenerich, T. H. Silva (2018). Identificando a relação virtual entre empresas explorando reações de usuários no facebook. In: Proc. of CoUrb’18. Campos do Jordão, Brazil.
Publicado
30/11/2020
Como Citar
TSUTSUMI, Diego Paolo; SILVA, Thiago Henrique.
Business Relationship Network Model from Social Reactions Data. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 26. , 2020, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 19-22.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2020.13055.
