A new algorithm for detecting communities in social networks based on content and structure information

Social media network analysis of Smallholder livestock farming communities in the United Kingdom

The creation of targeted policies and actions to help small-scale livestock keepers and reduce the risks associated with disease outbreaks in this sector is hampered by the scarcity of information about smallholder farmers. Smallholders play a crucial part in disease outbreaks containment, hence there is a need for better monitoring methods that take this population into account while gathering data. According to the literature, these communities frequently use social media as a channel for communication and information exchange. In this study we conducted social network analysis of an influential smallholder within the UK and visualised the user follower network. Additionally, we performed influential user analysis, Twitter user categorisation, and community detection to uncover more insights into the livestock farming networks. Our findings reveal distinct communities within the smallholder farming sector and identify influential users with the potential to impact information dissemination and animal health practices. The study also highlights the role of community structure in surveillance and control of animal diseases and emphasises the need for further research to refine our understanding of these communities and their unique characteristics. This work contributes to the growing body of literature on small-scale livestock farming in the UK and underscores the importance of incorporating smallholder communities into disease surveillance and control efforts.

Mining GitHub for research and education: challenges and opportunities

Purpose

This study aims to highlight the challenges and opportunities of using GitHub as a data source in both research and programming education.

Design/methodology/approach

This study provides general overview of the challenges and opportunities faced while conducting empirical research using GitHub as a data source. The challenges and opportunities are framed using the input–process–output model of open-source software.

Findings

GitHub data accessed from the application programming interface (API) can have several limitations, which can be overcome by Web scraping and using external data repositories such as GHArchive and GHTorrent. There are also several idiosyncrasies about GitHub that researchers need to be aware of to be able to use the data effectively, which can represent an opportunity for research. The challenges and opportunities are summarized for the licenses, community, development process and product of free/libra and open-source software communities hosted on GitHub.

Originality/value

This study provides a summary of GitHub-related challenges and opportunities that researchers can leverage to improve their empirical research. Furthermore, this summary can be a valuable resource for instructors that plan to use GitHub as a data source in their data-focused programming courses.