Adoption of Juncao Technology in the Developing Countries

The primary purpose of the study is to investigate how media framing shapes the adoption of Juncao technology. The research is based on a self-built corpus of the 97 English-language news reports (totaling 55,216 tokens) published between 2019 and 2025, collected using “Juncao technology” as the key search term. The 13 countries included in the corpus account for an estimated 514 million people (roughly 6–7% of the global population), spanning small island states to large African nations and providing broad geographic and demographic coverage. Analytical work was carried out using Python scripts — specifically employing BERTopic for theme mining and the NRC Emotion Lexicon for emotion analysis — along with the corresponding result datasets generated by these scripts. Not applicable Response Rates: Not applicable Presence of Common Scales: Not applicable A purposive sampling method was used to select the sample. Using "Juncao technology" as the key search term, English-language news reports published between 2019 and 2025 were collected from media outlets in 13 targeted Global South countries (primary recipients of Juncao technology), resulting in a final sample of 97 news reports. All English-language news reports centered on Juncao technology, published between 2019 and 2025 by media outlets in 13 Global South countries (Africa: Ethiopia, Tanzania, South Africa, Rwanda, Botswana, Kenya; Asia: Malaysia, the Philippines; Oceania: Papua New Guinea, Fiji, Vanuatu; the Americas: Jamaica, Guyana). Smallest Geographic Unit: Countries include 13 Global South nations web scraping; The data collection includes two main components: 1) Original English-language news reports (centered on Juncao technology) stored in Excel files, which were collected via keyword searches from media outlets in 13 Global South countries between 2019 and 2025; 2) Python scripts (used for thematic mining via BERTopic and emotion analysis via the NRC Emotion Lexicon) and their generated result datasets (also in Excel format). The scripts contain detailed comments on running environments, dependencies (listed in a separate requirements.txt file), and core analytical logic.