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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
13/08/2024 |
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Data da última atualização: |
13/08/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SOARES, N. D.; BRAGA, R.; DAVID, J. M. N.; SIQUEIRA, K. B.; STROELE, V. |
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Afiliação: |
NEDSON D. SOARES, UNIVERSIDADE FEDERAL DE JUIZ DE FORA; REGINA BRAGA, UNIVERSIDADE FEDERAL DE JUIZ DE FORA; JOSE MARIA N. DAVID, UNIVERSIDADE FEDERAL DE JUIZ DE FORA; KENNYA BEATRIZ SIQUEIRA, CNPGL; VICTOR STROELE, UNIVERSIDADE FEDERAL DE JUIZ DE FORA. |
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Título: |
An approach to foster agribusiness marketing applying data analysis of social network. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 222, 109044, 2024. |
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DOI: |
https://doi.org/10.1016/j.compag.2024.109044 |
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Idioma: |
Inglês |
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Conteúdo: |
Context: Applying social network data analysis to the agribusiness context can be useful to increase profitability, mainly in the dairy derivatives niche. Problem: The dairy derivatives market needs to recover its profitability. After a 2.9% GDP growth in 2022,1 Canal Rural the economic projections indicated only 0.91 % in 2023. Specific strategies to foster this market need to be applied. Objective: To collect information from social networks to find influential people who appreciate dairy derivatives and can influence new potential consumers, we present the IntelDigitalMarketing architecture. Its features encompass social network analysis, recommendations, and context propagation. Through its use, influencers and user communities can be identified who address issues related to specific domains in different social networks, and who can disseminate information to foster specific market niches. Method: We used the Design Science Research methodology to conduct the study. The solution encompasses techniques such as complex networks, machine learning, and ontologies, to detect market trends. With IntelDigitalMarketing architecture, we processed social network data from X (formerly Twitter), Instagram, and YouTube. Results: The results showed that the solution can search for communities of digital influencers who talk about dairy derivatives, what they talk about, and the dissemination of information on these social networks. Contributions and impact: With the combination of techniques, we can detect new relevant relationships among users that are not detected by other similar solutions. In addition, the proposed solution is online and in realtime, making it easier to follow trends in social networks and with the potential to foster the Agribusiness market. MenosContext: Applying social network data analysis to the agribusiness context can be useful to increase profitability, mainly in the dairy derivatives niche. Problem: The dairy derivatives market needs to recover its profitability. After a 2.9% GDP growth in 2022,1 Canal Rural the economic projections indicated only 0.91 % in 2023. Specific strategies to foster this market need to be applied. Objective: To collect information from social networks to find influential people who appreciate dairy derivatives and can influence new potential consumers, we present the IntelDigitalMarketing architecture. Its features encompass social network analysis, recommendations, and context propagation. Through its use, influencers and user communities can be identified who address issues related to specific domains in different social networks, and who can disseminate information to foster specific market niches. Method: We used the Design Science Research methodology to conduct the study. The solution encompasses techniques such as complex networks, machine learning, and ontologies, to detect market trends. With IntelDigitalMarketing architecture, we processed social network data from X (formerly Twitter), Instagram, and YouTube. Results: The results showed that the solution can search for communities of digital influencers who talk about dairy derivatives, what they talk about, and the dissemination of information on these social networks. Contributions and impact: With the combination of tec... Mostrar Tudo |
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Palavras-Chave: |
Cheese mark; Dairy derivatives; Rede social. |
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Thesagro: |
Análise de Dados; Mercado; Produto Derivado do Leite; Queijo. |
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Thesaurus Nal: |
Agribusiness; Data analysis; Social networks. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02667naa a2200301 a 4500
001 2166490
005 2024-08-13
008 2024 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2024.109044$2DOI
100 1 $aSOARES, N. D.
245 $aAn approach to foster agribusiness marketing applying data analysis of social network.$h[electronic resource]
260 $c2024
520 $aContext: Applying social network data analysis to the agribusiness context can be useful to increase profitability, mainly in the dairy derivatives niche. Problem: The dairy derivatives market needs to recover its profitability. After a 2.9% GDP growth in 2022,1 Canal Rural the economic projections indicated only 0.91 % in 2023. Specific strategies to foster this market need to be applied. Objective: To collect information from social networks to find influential people who appreciate dairy derivatives and can influence new potential consumers, we present the IntelDigitalMarketing architecture. Its features encompass social network analysis, recommendations, and context propagation. Through its use, influencers and user communities can be identified who address issues related to specific domains in different social networks, and who can disseminate information to foster specific market niches. Method: We used the Design Science Research methodology to conduct the study. The solution encompasses techniques such as complex networks, machine learning, and ontologies, to detect market trends. With IntelDigitalMarketing architecture, we processed social network data from X (formerly Twitter), Instagram, and YouTube. Results: The results showed that the solution can search for communities of digital influencers who talk about dairy derivatives, what they talk about, and the dissemination of information on these social networks. Contributions and impact: With the combination of techniques, we can detect new relevant relationships among users that are not detected by other similar solutions. In addition, the proposed solution is online and in realtime, making it easier to follow trends in social networks and with the potential to foster the Agribusiness market.
650 $aAgribusiness
650 $aData analysis
650 $aSocial networks
650 $aAnálise de Dados
650 $aMercado
650 $aProduto Derivado do Leite
650 $aQueijo
653 $aCheese mark
653 $aDairy derivatives
653 $aRede social
700 1 $aBRAGA, R.
700 1 $aDAVID, J. M. N.
700 1 $aSIQUEIRA, K. B.
700 1 $aSTROELE, V.
773 $tComputers and Electronics in Agriculture$gv. 222, 109044, 2024.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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