17 pages., Via online journal., Food waste has emerged as a major issue in the United States as the nation collectively sends more than 133 billion pounds of food to its landfills every year. In September 2015, the USDA and EPA announced an initiative to cut U.S. food waste in half by 2030. Between 2015 and 2016, nearly 100,000 posts about food waste have been published on Twitter, a microblogging platform that has been a hub of “slacktivism” since its inception in 2006. Using a conceptual framework of social cognitive theory, online activism, and crowdsourcing, we analyzed food waste conversation participants’ demographics, online communities, and proposed solutions. Data analysis was conducted with listening software Sysomos MAP and a qualitative content analysis of conversation content. The analysis revealed that more than 2,000 U.S. users engaged in the conversation, forming four discrete conversation communities led by influencers from government, news media, and environmental organizations. Proposed solutions to the food waste crisis included domestic or household behavior change, food-waste diversion and donation, recycling and upcycling, consumer education, and governmental action and policy. We recommend using Twitter to mine, test, and deploy solutions for combating food waste; engage with influential users; and disseminate materials for further research into the behavioral implications of online activism related to food waste.
Benavidez, Justin R. (author), Ribera, Luis A. (author), and Thayer, Anastasia (author)
Format:
Paper
Publication Date:
2020
Published:
International
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 201 Document Number: D11717
Notes:
Paper presented at the 2020 Agricultural and Applied Economics Association Annual Meeting, Kansas City, Missouri, July 26-28, 2020. 20 pages., Authors assessed the impact of tweets by U.S. President Donald Trump on agricultural commodity prices during the trade war with China. Results indicated tht days with high counts of tweets with keywords associated with the 2018-2019 trade war led to statistically significant structural breaks in the price series for hogs, corn, cotton, and soybeans.
Yagodin, Dmitry (author), Tegelberg, Matthew (author), Medeiros, Débora (author), and Russell, Adrienne (author)
Format:
Book chapter
Publication Date:
2017
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: D08856
Notes:
Pages 193-211 in Kunelius, Risto Eide, Elisabeth Tegelberg, Matthew Yagodin, Dmitry (eds.), Media and global climate knowledge: journalism and the IPCC. United States: Palgrave Macmillan, New York City, New York. 309 pages.
12 pages., Authors presented an algorithm to analyze the behaviour of users of Twitter involving the environment and health care. To illustrate, they presented a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 was used to develop a heuristic analysis of the validity of the information.
17 pages., Via online journal., Purpose: This paper presents economic and pedagogical motivations for adopting information and communications technology (ICT)-mediated learning networks in agricultural education and extension. It proposes a framework for networked learning in agricultural extension and contributes a theoretical and case-based rationale for adopting the networked learning paradigm.
Design/methodology/approach: A review of the literature highlights the economic and pedagogical need for adopting a networked learning approach. Two examples are described to instantiate the language for learning networks: a small community of farmers in India and large Twitter community of Australian farmers.
Findings: This paper reviews evidence that successful networked learning interventions are already occurring within agricultural extension. It provides a framework for describing these interventions and for helping future designers of learning networks in agricultural extension.
Practical implication: Facilitation of learning networks can serve to achieve efficient agricultural extension that connects farmers across distances for constructivist learning. To realize these benefits, designers of learning networks need to consider set design, social design and epistemic design.
Theoretical implication: This paper contributes a theoretical framework for designing, implementing and analysing learning networks in agriculture. It does this by integrating existing ideas from networked learning and applying them to the agricultural context through examples.
Originality/value: This paper contributes an understanding of the value of networked learning for extension in terms of economic and pedagogical benefits. It provides a language for talking about learning networks that is useful for future researchers and for practitioners.
