The Algorithmic Archive project is a one year project funded by the Mellon Foundation. As part of the first Work Package, we explored how researchers from different disciplines use social media data to answer various research questions.

This post is the third in a three-part series presenting use cases drawn from research conducted as part of the Algorithmic Archive project.

We would like to thank the researchers who generously shared insights from their work.

Use Case – Study on the trustworthiness of social media visual content among young adults (TRAVIS project)[1]

Research questions and aim(s):

Trust And Visuality: Everyday digital practices (TRAVIS) is an ESRC project which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme. This research project that looks at how young adults experience, build and express trust in news and social media images related to wellbeing and health. It explores how and why people trust some visuals over others and how content creators establish trustworthiness through visual content. The TRAVIS project involves cross-national collaboration of multiple research teams located at different universities in UK and Europe. This includes the University of Oxford, in particular the Oxford team is based School of Geography and the Environment.

Social media data used:

The project included data collected indirectly from platforms including Facebook, Instagram, TikTok and YouTube (see below).

Tools and methods adopted:

Data collection from social media consisted of screenshots taken from the devices of interviewed young adults, as the TRAVIS project investigates the meaning of social media posts (visual content) via interviews with young adult users. The datasets generated from this method of collection counts around 400 screenshots, stored on an institutional cloud drive, which is accessible by the whole team.

[1] Further information about the TRAVIS project are available here: https://www.tlu.ee/en/bfm/researchmedit/trust-and-visuality-everyday-digital-practices-travis

The Algorithmic Archive project is a one year project funded by the Mellon Foundation. As part of the first Work Package, we explored how researchers from different disciplines use social media data to answer various research questions.

This post is the first in a three-part series presenting use cases drawn from research conducted as part of the Algorithmic Archive project.

We would like to thank the researchers who generously shared insights from their work.

Use Case: Network/cluster analysis to investigate the construction and influence of information trustworthiness within social movements on Twitter [1]

Research questions and aim(s):

The researcher wanted to explore the construction and influence of information trustworthiness within social media movements in the context of the Hong Kong protests and the #BlackLivesMatter movements. Social media platforms offer a digital space for social movements to facilitate the diffusion of critical information and the formation of networks, coordinating protests and reach a wider audience.

Social media data used:

This study focused on Twitter as it was used evenly by both social movements, and the researcher already had an established presence on this platform. Also, at the time of data collection (2020-2021), access to Twitter data for academic research was still relatively open to researchers.

For the purpose of this study, the researcher examined the follow and followers’ relationship of top accounts counting millions of followers that had been selected as big information disseminators, including organisations, individuals or accounts serving a particular niche or purpose.

Data collection was conducted at a specific point in time in 2021. Social media data quantitative analysis (e.g. cluster analysis) was complemented with qualitative data collected via an online survey.

Tools and methods adopted:

The researcher requested and obtained access to the Twitter API. However, high-level coding skills were required to access the data, which the researcher did not have at that time due to their predominantly qualitative research background. To address this, the researcher found and used a Go script called Nucoll[2], which is freely available on GitHub and enabled the researcher to collect the required data. Nucoll is a command-line tool that, according to its developer, retrieves data from Twitter using keyword instructions, for which the developer provided example queries and brief explanations. For each social movement, the researcher selected three organisations: one large organisation, one activist group, and one additional account that was relevant to the movement. Once these accounts were selected, they were processed through the script to capture all following/follower relationships and combine them into a graph for each protest analysed. Further data visualisation and analysis — including clustering and network analysis — were conducted using Gephi.

[1] Charlotte Im, The Construction and Influence of Information Trustworthiness in Social Movements, Doctoral Thesis, University College London (UCL), 2024.

[2] https://github.com/jdevoo/nucoll