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2. Social Network Analysis for efficient delivery of Agricultural Extension Services
- Collection:
- Agricultural Communications Documentation Center (ACDC)
- Contributers:
- Tripathi, Rohit (author) and Devi, Kalyanee (author)
- Format:
- Conference paper
- Publication Date:
- 2020-01-01
- Published:
- International: IEEE
- Location:
- Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 203 Document Number: D12217
- Journal Title:
- 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
- Journal Title Details:
- 2020
- Notes:
- Social Networks like Facebook, Twitter, YouTube and WhatsApp are now becoming very popular tools that are used for sharing the latest and important farming based information in different part of India. These tools are now widely used by Agricultural Extension and Advisory services to interact with the farmers for exchanging agricultural related information in India. The most influential farmers in a network can disseminate the information to the less central farmers of the network. The extension functionaries that promote the agricultural innovation will share the information with the most central members which in turn will share it with maximum number of the members of a social network. Social Network Analysis (SNA) acts as an efficient analytical tool that helps us to understand the relationship between farmer stakeholders and the importance of a farmer's position in the entire network. In this paper, a structural analysis of the Social Network is performed over two datasets, namely, Facebook-like dataset and Twitter Lists dataset. Here, we have studied the importance of individual nodes in the network through various centrality measures. SNA properties like centralities are used to represent the most central nodes that can act as a good influence spreader in the network. In this paper, initially the k-core decomposition method is used to find a set of influential nodes among all the nodes in the network. It is done to reduce the computational time. Our simulation shows that the nodes with higher Page Rank centrality can activate more members in a network as compared to other centrality measures. For Information diffusion, we have used Linear Threshold (LT) Model to understand the influence spread of the central farmers in the network.