Speaking Their Language: Language Inclusion and YouTube Agricultural Content Engagement in A frica
N. Peter Reeves et al.
Abstract
Efficiently educating farmers in effective agricultural practices is critical in resource‐limited developing countries. YouTube, with its broad accessibility and built‐in viewership tracking, presents a potential scalable platform for agricultural education. This study assesses how language inclusion policies affect engagement with agricultural YouTube content. We conducted a case study using a video campaign to address post‐harvest loss in Africa, featuring an educational animation translated into 14 Ghanaian, 35 Kenyan, and 16 Nigerian languages. The campaign was distributed through paid YouTube ads. Two inclusion policies were evaluated through computational simulations using real‐world data: equal opportunity (i.e., equal spending across languages) and equal outcome (i.e., adjusted spending to equalize viewership across languages). We also estimated viewership under two additional scenarios: using only the official language and using the most cost‐effective language for each country. The most cost‐effective campaigns coincided with the official language of English in Ghana (8.9 viewers per USD) and Nigeria (3.1 viewers per USD), but not in Kenya, where the most effective language campaign was Kikuyu (16.2 viewers/USD). Overall, the equal spending policy reduced viewership by 43%, while the equal outcome policy reduced viewership by 66% compared to campaigns in official languages. However, results show country‐specific trends. Differences in viewership between inclusion policies were minimal in Ghana and Nigeria. Conversely, in Kenya, the discrepancy in inclusion policy impact was more pronounced, suggesting that in certain regions, more inclusive policies are likely to significantly influence viewership levels. These findings highlight the importance of localized evaluations of inclusion policies in digital agricultural education.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.