Data for A retrospective feasibility study of agricultural education in Africa using YouTube multilingual animations

<p>There is a critical need for widespread information dissemination of agricultural best practices in Africa.  Literacy, language and resource barriers often impede such information dissemination.  Culturally and linguistically localized, computer-animated training videos placed on YouTube and promoted through paid advertising is a potential tool to help overcome these barriers.  The goal of this study is to assess the feasibility of reaching language-diverse populations in Africa using this new type of information dissemination channel.  As a case study, cost estimates were obtained for YouTube ad campaigns of a video to prevent post-harvest loss through safe food storage using sanitized jerrycan containers.  Seventy-three video variants were created for the most common 16 languages in Ghana, 35 languages in Kenya, and 22 languages in Nigeria.  Using these videos, campaigns were deployed country wide or focused on zones of influence that represent economically underdeveloped regions known to produce beans suitable for jerrycan storage.  Using data collected from YouTube ad campaigns, language-specific models were created for each country to estimate how many viewers could be reached per US dollar spent.  Separate models were created to estimate the number of viewers who watched 25% and 75% of the video (most of video without end credits), reflecting different levels of engagement.  For language campaigns with both country wide and zone of influence areas of deployment, separate region-specific models were created. </p>

<p>GoogleAd daily reports were gererated and stored as csv files with the date of the campaign serving as the file name. Matlab code was created to compile the data into the JerrycanV16fromGoogleAd14.xlsx spreadsheet, which was used for data analysis.</p>

<p>Note that the raw data files have two different sets of headers. The headers for data collected before 2021-09-15 contains columns 'Currency' and 'Impressions', while the data collected after that date contain columns 'Currency code' and 'Impr.'.</p>