Exploring Twitter Discourse around the Use of Artificial Intelligence to Advance Agricultural Sustainability

Abstract

This paper presents an exploration of public discourse surrounding the use of artificial intelligence (AI) in agriculture, specifically related to precision agriculture techniques. (1) Advancements in the use of AI have increased its implementation in the agricultural sector, often framed as a sustainable solution for feeding a growing global population. However, lessons learned from previous agricultural innovations indicate that new technologies may face public scrutiny and suspicion, limiting the dissemination of the innovation. Using systems thinking approaches can help to improve the development and dissemination of agricultural innovations and limit the unintended consequences of innovations within society. (2) To analyze the current discourse surrounding AI in agriculture, a content analysis was conducted on Twitter using Meltwater to select tweets with specific reach and engagement. (3) Seven themes resulted from the analysis: precision agriculture and digital technology innovation; transformation and the future of agriculture; accelerate solutions, solve challenges; data management and accessibility; transforming crop management, prioritizing adoption; and AI and sustainability. (4) The discourse on AI in agriculture on Twitter was overwhelmingly positive, failing to account for the potential drawbacks or limits of the innovation. This paper examines the limits of the current communication and outreach across environmental, economic, social, cultural, political, and behavioral contexts.

Bibliometric and Social Network Analysis on the Use of Satellite Imagery in Agriculture: An Entropy-Based Approach

“…Two-way communication between scientists and the public should be incorporated into effective communication and scientific outreach efforts to improve not only the reputation of particular innovations but also an appropriate development by listening to audiences and tailoring innovations to their needs [26]. A virtual forum where this type of communication can be implemented is represented by online social network platforms [27]. In this sense, it could be very interesting to take a look at how the use of satellite imagery in agriculture is presented in online social networks.…”

Data Analysis in Social Networks for Agribusiness: A Systematic Review

“…Then, they use topological analysis to understand the relationships that one keyword has with another. As for the SMA methods, the following were identified: (i) time analysis, where a timeline of social media is made, identifying the number of publications during a period [6], [31], [38], [39]; (ii) textual analysis, where social media keywords were studied using natural language processing (NLP) [37], [38], [40], [39]; (iii) statistical analysis, where the studies used hypothesis tests, means and percentages [41], [42], [32], [43]; (iv) sentiment analysis, which aims to identify and extract subjective information from social media by combining NLP and machine learning techniques to assign weighted emotional scores [6], [31], [44], [43], [38], [39]; (v) geographic analysis, where media upload coordinates are used to map their geographic locations [38], [39]; and finally (vi) demographic analysis, where age and gender of the social media user are estimated using their first and last name as input [6]. Figure . 4 illustrates this analysis and shows a radar graph of the popularity of the SNA and SMA techniques.…”

“…We analyzed the selected papers to extract the researcher's metrics to understand how the methods proposed in primary studies were evaluated. Most articles (∼83%) [6], [31], [32], [38], [39], [40], [41], [42], [43], [45] used statistical methods such as total publications, means, and percentages to describe the results and evaluate the proposal. However, none of them reported an experiment dedicated to evaluating the proposed method.…”

“…In addition, they describe the importance of understanding the influence of social media on people and opportunities for management agencies, such as messages in public health campaigns. In [38] the authors explore Artificial Intelligence (AI) in agriculture. Based on SMAs, the authors conclude that AI techniques in agriculture are positive.…”

Teaching Systems Thinking Concepts with Hypothetical Case Scenarios: An Exploration in Agricultural Education

“…In an increasingly interconnected and unpredictable world, systems thinking is a skillset that can identify the root of complex problems and propose interventions to improve outcomes (Rutherford, 2019). Systems thinking approaches can be described as those that emphasize the interdependence of component parts of a dynamic system and their interactions with other social, cultural, environmental, economic, political, and behavioral systems (Mahaffy et al, 2018;Sanders et al, 2021). Pauley et al (2019) called for ANR scholars and practitioners to "foster complex adaptive systems thinking among their stakeholders through education and outreach" (p. 142).…”