[Supplementary data] Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming

Georgina Tarrant; Taranpreet Singh Rai; Luke Boyden; Kennedy Mwacalimba; Raymond Tiernan; Peter Kimeli; Travis Lee Street; Alasdair James Charles Cook; Kevin Wells

doi:10.5281/zenodo.16813625

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[Supplementary data] Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming

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[Supplementary data] Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming

Georgina Tarrant, Taranpreet Singh Rai, Luke Boyden, Kennedy Mwacalimba, Raymond Tiernan, Peter Kimeli, Travis Lee Street, Alasdair James Charles Cook and Kevin Wells

Zenodo

24/08/2025

DOI: https://doi.org/10.5281/zenodo.16813625

Abstract

social listening

animal health

gender

Livestock

Sub-Saharan Africa

Supplementary materials to accompany our VeriXiv preprint "Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming". Social media listening (SML) data were collected from X (formerly Twitter), blogs, forums, Reddit and Facebook pages using Pulsar Platform™. SML posts were collected if they met the bespoke keyword search criteria and originated in or mentioned one or more of ten Sub-Saharan African countries: Ethiopia, Ghana, Côte d'Ivoire (Ivory Coast), Kenya, Nigeria, Senegal, Tanzania, Uganda, Zambia, Zimbabwe. The searches were grouped into 4 themes: (1) Women in livestock and farming; (2) Challenges faced by women in livestock and farming; (3) Perceptions of disease health and control measures; (4) Women’s training, education and interventions in livestock. The data was scraped using Pulsar Platform. Section 1: Supplementary data Overview of topic modelling outputs by theme for all four themes. Each theme has sub-themes with titles and text descriptions. No direct quotes are used as these are provided in the preprint. Section 2: Supplementary figures and tables Further information on in-country demographics (gender and rural/urban populations) and internet penetration compared to population. Summary tables with the number of collected social media posts by country and totals for posts that mention one of the countries of interest. Author gender distribution for collected posts. References Reference list for data sources used.

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DatasetSupplementary materials to accompany our VeriXiv preprint "Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming". Social media listening (SML) data were collected from X (formerly Twitter), blogs, forums, Reddit and Facebook pages using Pulsar Platform™. SML posts were collected if they met the bespoke keyword search criteria and originated in or mentioned one or more of ten Sub-Saharan African countries: Ethiopia, Ghana, Côte d'Ivoire (Ivory Coast), Kenya, Nigeria, Senegal, Tanzania, Uganda, Zambia, Zimbabwe. The searches were grouped into 4 themes: (1) Women in livestock and farming; (2) Challenges faced by women in livestock and farming; (3) Perceptions of disease health and control measures; (4) Women’s training, education and interventions in livestock. The data was scraped using Pulsar Platform. Section 1: Supplementary data Overview of topic modelling outputs by theme for all four themes. Each theme has sub-themes with titles and text descriptions. No direct quotes are used as these are provided in the preprint. Section 2: Supplementary figures and tables Further information on in-country demographics (gender and rural/urban populations) and internet penetration compared to population. Summary tables with the number of collected social media posts by country and totals for posts that mention one of the countries of interest. Author gender distribution for collected posts. References Reference list for data sources used.CC BY-SA V4.0, Open Access

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DatasetSupplementary materials to accompany our VeriXiv preprint "Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming".Social media listening (SML) data were collected from X (formerly Twitter), blogs, forums, Reddit and Facebook pages using Pulsar Platform™. SML posts were collected if they met the bespoke keyword search criteria and originated in or mentioned one or more of ten Sub-Saharan African countries: Ethiopia, Ghana, Côte d'Ivoire (Ivory Coast), Kenya, Nigeria, Senegal, Tanzania, Uganda, Zambia, Zimbabwe. The searches were grouped into 4 themes: (1) Women in livestock and farming; (2) Challenges faced by women in livestock and farming; (3) Perceptions of disease health and control measures; (4) Women’s training, education and interventions in livestock. The data was scraped using Pulsar Platform.Section 1: Supplementary dataOverview of topic modelling outputs by theme for all four themes. Each theme has sub-themes with titles and text descriptions. No direct quotes are used as these are provided in the preprint.Section 2: Supplementary figures and tablesFurther information on in-country demographics (gender and rural/urban populations) and internet penetration compared to population.Summary tables with the number of collected social media posts by country and totals for posts that mention one of the countries of interest.Author gender distribution for collected posts.ReferencesReference list for data sources used.CC BY-SA V4.0, Restricted. Access maybe granted on request

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Title: [Supplementary data] Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming
Creators: Georgina Tarrant (Corresponding Author) - University of Surrey School of Veterinary Medicine
Taranpreet Singh Rai (Investigation) - University of Surrey, School of Computer Science and Electronic Engineering
Luke Boyden (Investigation) - University of Surrey, School of Computer Science and Electronic Engineering
Kennedy Mwacalimba (Sponsor) - Zoetis
Raymond Tiernan (Project Administration) - Zoetis
Peter Kimeli (Writing - review and editing) - University of Nairobi
Travis Lee Street (Investigation) - University of Surrey, School of Veterinary Medicine
Alasdair James Charles Cook (Writing - review and editing) - University of Surrey, School of Veterinary Medicine
Kevin Wells (Supervisor) - University of Surrey, School of Computer Science and Electronic Engineering
Publisher: Zenodo
Publication Date: 24/08/2025
Grants: INV-045195, Gates Foundation (United States, Seattle) - BMGF
Identifiers: 991047166002346
Academic Unit: School of Veterinary Medicine; School of Computer Science and Electronic Engineering
Language: English
Resource Type: Dataset
SDG (Scopus 2023): SDG 5; SDG 10; SDG 2; SDG 1;SDG 8; SDG 9; SDG 12

[Supplementary data] Harvesting social media and using large language models to analyse online discourse: Developing methodology to explore the challenges faced by Sub-Saharan African women in livestock farming

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