Facebook: Posts, Comments, Likes, and Shares

Can apply to: Any research output with a unique URL.

Metric definition: User-generated actions in Facebook that relate to the sharing of research outputs, including the number of posts linking to a scholarly product, and the number of comments, likes, and shares generated by these posts. A post is counted when a Facebook user shares a link to a scholarly product. A comment occurs when a user adds text below another user’s post that expresses an opinion or remark. Likes are calculated when a Facebook user chooses the “like” option beneath a post. A share is counted when a Facebook user chooses an option to repost to their profile another user’s post, thus sharing content with their own network of Facebook connections, either with or without added commentary.

Metric calculation: Various altmetric aggregators have slightly different ways of calculating Facebook metrics. For instance, Altmetric.com only counts posts linking to research outputs from public pages, while PlumX also counts posts, likes, and other private interactions that have been anonymized via the Facebook Graph API.

Data sources: Facebook, as collected and parsed by Altmetric and PlumX. Data is also available and auditable using the Facebook Graph API.

Appropriate use cases: Interactions on Facebook contribute positively to the online visibility of scholarship and its ability to be traced back to a source. However, studies have produced different findings regarding the appropriateness of interpreting Facebook metrics to indicate scholarly or public interest and engagement with research. As such, counts should be considered in context, e.g. alongside qualitative data about who is interacting with Facebook posts, or the content of comments or shares if available.

Limitations: As mentioned above, different data providers have different methods for calculating Facebook metrics. Altmetric and Impactstory (which share the same altmetric data) only track a fraction of Facebook activity for research, because they track public shares of scholarly products. By contrast, PlumX includes data about private Facebook interactions, but its counts cannot be independently audited for context or accuracy. Additionally, research outputs with more than one associated URL are difficult to track accurately. Facebook metrics also do not capture posts that discuss a research output but do not include an associated URL.

Inappropriate use cases: Facebook-based indicators should not be used as an indicator of future citations, and they should not be used as measures of scientific quality or impact. Multiple studies on the scholarly use of social media have found that the correlation between Facebook metrics and article citations is weak, which suggests Facebook posts, comments, likes, and shares may evidence different or less formal kinds of interest and use of scholarship. Additionally, while posts, comments, likes and shares are each unique forms of engagement on the Facebook platform, their counts are sometimes combined by data providers, which makes meaningful interpretation of Facebook indicators more difficult.

Available metric sources: Altmetric, Impactstory, Facebook Graph API, PlumX

Transparency: Altmetric.com and Impactstory report Facebook shares on a manually curated set of public Facebook pages, meaning the shares are fully auditable. PlumX links out to public Facebook activity, where possible, but also reports the number of private shares, comments, and likes, which are not auditable.

Website: https://developers.facebook.com/

Timeframe: Facebook was publicly launched in February 2004. In theory, research of any age can be shared on Facebook.

Further Reading:

Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: A review of the literature. Journal of the Association for Information Science and Technology, 68(9), 2037–2062. https://doi.org/10.1002/asi.23833

Zahedi, Z., & Costas, R. (2018). General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators. PLOS ONE, 13(5), e0197326. https://doi.org/10.1371/journal.pone.0197326

Last Updated: June 16, 2020