24 pgs., In order to stay relevant in an online world, Extension must properly use social networking platforms to effectively reach diverse audiences regarding agricultural and natural resource issues. However, few studies have focused on how Extension uses Facebook to effectively accomplish its goal. This study’s purpose was to explore how Utah State University Extension Sustainability uses Facebook to engage followers. The researchers conducted a quantitative content analysis of 504 messages posted to the USU Extension Sustainability Facebook page. Graphics and links were the most common post characteristics used by the organization. Text-only posts and posts containing videos were utilized the least. Food was the most common area of sustainability discussed on the page. Posts containing videos, shared content, or that tagged other Facebook pages in messages experienced statistically significantly higher user engagement than posts without those characteristics. Posts containing hashtags experienced statistically significantly lower engagement. Neutral sentiment appeared in the majority of posts. Additionally, information seeking was the most dominant communicative function among the posts. Neither the type of sentiment nor communicative functions were significantly connected to engagement. Future research should determine changes in knowledge, attitudes, intentions, and behavior as a result of exposure to, and engagement with, the Facebook page. Additionally, a qualitative study determining consumers’ attitudes toward Facebook content can provide a deeper understanding of the audience’s thought processes and content preferences. Page administrators should craft engaging content that builds community among followers.
5 pages, via Online journal, The social media service Instagram is a popular public platform, but often underused tool to reach new demographics, reduce barriers, and perpetuate science-based information in extension. In the U.S. Intermountain West, Instagram was the top-rated platform for sharing information by predominantly new and female farmers. This article provides recommendations on key behaviors, goal setting, and quantifying impact on Instagram for extension programming. Accounts should target one niche or market, a consistent and personal voice, and regular communication (new content at least three times weekly). Unique and productive connections between extension personnel, community leaders, farmers, students, and public influencers expands programming. Tracking program accounts, including the number of followers and engagement rates, can assess program impacts and target market needs.
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.