Social media and the social sciences: methods and tools for academic research

Social media as a research tool has gained in popularity in recent years. Those new to the field may wish to know about the key methodologies and tools that can be used for the analysis of data. This post will provide a round-up of popular methods and tools for the analysis of social media data.

Although there will be a number of ways to build custom scripts for the analysis of data; it remains that pre-existing tools remain popular for social science scholars as it removes the need for development skills. Twitter remains the most utilised platform and will be the focus of this post.

New event for 2019! Social Media & Digital Humanities: Methods/Approaches For Social Scientists

As a wrote in my 2015 LSE Impact blog post:

• Sentiment analysis works well with social media data, as posts may be  consistent in length
• Time series analysis is normally used when examining posts overtime to see when a peak of social media posts may occur
• Network analysis is used to visualize the connections between people and to better understand the structure of the conversation.
• Machine learning methods may work well with social media data because of the volume of tweets
• Qualitative analysis methods  (such as thematic and content analysis) are rare for social media research, however, they can often offer up more depth than quantitative methods.

Read more here 

In terms of popular tools for the analysis of social media data a list of popular tools is populated below:

An overview of tools for 2019

The tools above can be used in a manner to conduct academic research that many may believe that is not possible!

Published research that may be of interest:

Downing, J & Ahmed, W (2019). #MacronLeaks as a “warning shot” for European democracies: challenges to election blackouts presented by social media and election meddling during the 2017 French presidential election.

Vidal-Alaball J., Fernandez-Luque L., Marin Gomez FX., Ahmed W (2019). A New Tool for Public Health Opinion: Using Twitter Polls for Insight into Telemedicine. JMIR Formative Research. ( 2018 Journal Stats from Web of Science)

Ahmed, W., Bath, P.A, Sbaffi, L., Demartini, G. (2019). Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data. Health Information and Libraries Journal (2018 impact factor ).

Ahmed, W., & Lugovic, S. (2019). Social Media Analytics: Analysis and Visualisation of News Diffusion using NodeXL. Online Information Review. (2018 Impact Factor: 1.928).

Zhang, Z., & Ahmed, W (2018). A Comparison of Information Sharing Behaviours across 379 Health Conditions on Twitter. International Journal of Public Health. (2018 impact factor 2.373) 

Ahmed, W. (2018) Public Health Implications of #ShoutYourAbortion. Public Health Journal.  (2018 Impact Factor 1.696). 

Ahmed, W. (2018) Using Social Media Data for Research: An Overview of Tools. Journal of Communication Technology.

Samuel, G., Ahmed, W., Kara, H., Jessop, C., Quinton, S., & Sanger, S. (2018). Is It Time to Re-Evaluate the Ethics Governance of Social Media Research?. Journal of Empirical Research on Human Research Ethics, 1556264618793773.

Ahmed, W., Bath, P.A., & Demartini G (2017) Using Twitter as a data source: An overview of ethical challenges.  Advances in Research Ethics and Integrity (Eds). Emerald Books.