Holistic analysis of social media user behavior in agricultural context: Bibliometric analysis and systematic review
Bibliographic record
Abstract
This research aims to understand how farmers, especially those with limited technological knowledge, utilize social media in their agricultural activities. The study also aims to identify the impact and responses of farmers to the use of social media in their agricultural practices. Additionally, the research discusses a conceptual framework that integrates internal and external factors in understanding social media user behavior. The research methodology employed is a systematic literature review using scientometric analysis. Bibliometric approaches, machine learning, and social network analysis are utilized to achieve research objectives. Data were obtained from the Scopus database, consisting of high-quality articles published between 2011 and 2023.The findings indicate that social media plays a significant role in influencing farmers' responses to the information they receive and their levels of trust, subsequently affecting their willingness to adopt smart agricultural technologies. Furthermore, the research highlights internal and external factors influencing social media user behavior in the agricultural context. The novelty of this research lies in its holistic approach that integrates cognitive and behavioral factors in understanding social media user behavior. Additionally, the study complements previous literature by addressing antecedents, mechanisms, and consequences of social media use by farmers, as well as identifying barriers they face in leveraging social media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.140 | 0.118 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".