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Social network analysis of spreading and exchanging information on Twitter: the case of an agricultural research and education centre in Mexico
The Journal of Agricultural Education and Extension ( IF 2.654 ) Pub Date : 2021-04-20 , DOI: 10.1080/1389224x.2021.1915829
Norman Aguilar-Gallegos 1, 2 , Laurens Klerkx 2 , Leticia Elizabeth Romero-García 3 , Enrique Genaro Martínez-González 1 , Jorge Aguilar-Ávila 1
Affiliation  

ABSTRACT

Purpose – This paper aims to contribute to deepening insights on social media in agricultural research by presenting a case study on how a research and education centre in Mexico connected, communicated, and interacted in a research and education community on Twitter.

Design/methodology/approach – By using a Social Network Analysis (SNA) approach, 1585 tweets were analysed. The data was gathered using NodeXL. Afterwards, SNA was performed through the igraph R package.

Findings – The dynamic of virtual interaction around an agricultural research and education centre shows that different roles are needed to connect and foster the interaction on Twitter, as well as to spread information broader. While some accounts are functioning as creators of information, others act as consumers and diffusers of it, and a few more accounts play both roles.

Practical Implications – To increase the size and strengthen the interaction of a network on Twitter, it is advisable to have the support of other accounts and to promote its use among potential users. This requires capabilities to develop a social media strategy and pro-actively engage new followers.

Theoretical Implications – Through a SNA approach, it is possible to understand better the functions of weak and strong ties in virtual settings, i.e. the former to extend the network, and the latter to strengthen it. These ties can enhance information dissemination and enable exchanges on social media.

Originality/value – This paper applies SNA as a theoretical and methodological framework to demonstrate that the interactions among users are different when a whole network is analysed, and when it is divided into the mentions and retweets networks. By doing this, hidden patterns are revealed.



中文翻译:

Twitter信息传播与交流的社会网络分析:以墨西哥某农业研究与教育中心为例

摘要

目的——本文旨在通过展示墨西哥研究和教育中心如何在 Twitter 上的研究和教育社区中连接、交流和互动的案例研究,有助于加深对农业研究中社交媒体的见解。

设计/方法/方法——通过使用社交网络分析 (SNA) 方法,分析了 1585 条推文。数据是使用 NodeXL 收集的。之后,通过 igraph R 包执行 SNA。

调查结果——围绕农业研究和教育中心的虚拟互动动态表明,需要不同的角色来连接和促进 Twitter 上的互动,以及更广泛地传播信息。虽然一些账户充当信息的创造者,但其他账户充当信息的消费者和传播者,还有一些账户扮演这两种角色。

实际意义——为了增加 Twitter 网络的规模和加强网络互动,建议获得其他帐户的支持并在潜在用户中推广其使用。这需要制定社交媒体战略并积极吸引新追随者的能力。

理论意义——通过 SNA 方法,可以更好地理解弱联系和强联系在虚拟环境中的作用,即前者用于扩展网络,后者用于加强网络。这些联系可以加强信息传播,促进社交媒体上的交流。

原创性/价值——本文将 SNA 用作理论和方法框架,以证明当分析整个网络以及将其分为提及和转发网络时,用户之间的交互是不同的。通过这样做,隐藏的模式被揭示出来。

更新日期:2021-04-20
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