EDUCATION IN AGRICULTURE ON THE SOCIAL NETWORK "TWITTER"

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F23%3A96791" target="_blank" >RIV/60460709:41110/23:96791 - isvavai.cz</a>

Výsledek na webu

<a href="https://library.iated.org/view/PILAROVA2023EDU" target="_blank" >https://library.iated.org/view/PILAROVA2023EDU</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21125/iceri.2023.1437" target="_blank" >10.21125/iceri.2023.1437</a>

Jazyk výsledku

angličtina

Název v původním jazyce

EDUCATION IN AGRICULTURE ON THE SOCIAL NETWORK "TWITTER"

Popis výsledku v původním jazyce

The main objective of this study is to perform an analysis of the structure of communication on the Twitter social network, specifically within the domain of education in agriculture, using topic analysis, sentiment analysis, and trend analysis. Through these techniques, the study seeks to identify the most prevalent themes and discussions within the community. By conducting a thorough analysis of communication within this specific domain, the study seeks to contribute to a better understanding of the role of social media in education and knowledge dissemination in the field of agriculture. This study analyzed education-related communications in agriculture on Twitter using two methodologies: Latent Dirichlet Allocation (LDA) and the Framework for Social Media Analysis Based on Hashtag Research (SMAHR). LDA is an unsupervised machine learning technique that extracts topics from text, while SMAHR focuses on analyzing hashtags. The study used Python Gensim module for LDA topic modeling and identified prevalent hashtags associated with education in agriculture using SMAHR. Previous studies have used these techniques for analyzing various topics. The present study employed the Twitter API to extract messages, commonly referred to as 'Tweets', from the Twitter database. The data collection period spanned from January 1st, 2010, to December 30th, 2022. Tweets were programmatically captured using a Python script, which specified the following conditions for inclusion: all Tweets containing the keywords ["education"] AND ["agribusiness" OR "agriculture"]. Our analysis of the Twitter data revealed a number of prevalent topics and themes related to education in agriculture. These included sustainable agriculture practices, animal welfare and ethics, access to resources and education, the use of technology and innovation, community engagement and involvement, the role of agriculture in food security and nutrition, climate change and its impact on agriculture, and agricultural policy and government regulation. Overall, these findings highlight the diverse and complex nature of discussions related to education in agriculture on Twitter. They suggest that social media platforms like Twitter can be an important source of information and insight for policymakers, educators, and researchers interested in promoting sustainable agriculture practices, improving access to education and resources, and addressing challenges related to animal welfare, food security, and climate change. Further research in this area could help to deepen our understanding of the factors shaping these discussions and inform efforts to promote more effective and sustainable agricultural practices.

Název v anglickém jazyce

EDUCATION IN AGRICULTURE ON THE SOCIAL NETWORK "TWITTER"

Popis výsledku anglicky

The main objective of this study is to perform an analysis of the structure of communication on the Twitter social network, specifically within the domain of education in agriculture, using topic analysis, sentiment analysis, and trend analysis. Through these techniques, the study seeks to identify the most prevalent themes and discussions within the community. By conducting a thorough analysis of communication within this specific domain, the study seeks to contribute to a better understanding of the role of social media in education and knowledge dissemination in the field of agriculture. This study analyzed education-related communications in agriculture on Twitter using two methodologies: Latent Dirichlet Allocation (LDA) and the Framework for Social Media Analysis Based on Hashtag Research (SMAHR). LDA is an unsupervised machine learning technique that extracts topics from text, while SMAHR focuses on analyzing hashtags. The study used Python Gensim module for LDA topic modeling and identified prevalent hashtags associated with education in agriculture using SMAHR. Previous studies have used these techniques for analyzing various topics. The present study employed the Twitter API to extract messages, commonly referred to as 'Tweets', from the Twitter database. The data collection period spanned from January 1st, 2010, to December 30th, 2022. Tweets were programmatically captured using a Python script, which specified the following conditions for inclusion: all Tweets containing the keywords ["education"] AND ["agribusiness" OR "agriculture"]. Our analysis of the Twitter data revealed a number of prevalent topics and themes related to education in agriculture. These included sustainable agriculture practices, animal welfare and ethics, access to resources and education, the use of technology and innovation, community engagement and involvement, the role of agriculture in food security and nutrition, climate change and its impact on agriculture, and agricultural policy and government regulation. Overall, these findings highlight the diverse and complex nature of discussions related to education in agriculture on Twitter. They suggest that social media platforms like Twitter can be an important source of information and insight for policymakers, educators, and researchers interested in promoting sustainable agriculture practices, improving access to education and resources, and addressing challenges related to animal welfare, food security, and climate change. Further research in this area could help to deepen our understanding of the factors shaping these discussions and inform efforts to promote more effective and sustainable agricultural practices.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICERI2023 Proceedings

ISBN

978-84-09-55942-8

ISSN

2340-1095

e-ISSN

—

Počet stran výsledku

10

Strana od-do

5784-5793

Název nakladatele

IATED Academy

Místo vydání

Valencia, Spain

Místo konání akce

Seville

Datum konání akce

1. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—