Source: https://ifpri.libguides.com/research-metrics/altmetrics-score
Research Impact Metrics
Altmetric Attention Score: Three factors
The Altmetric Attention Score is an automatically calculated, weighted count of all of the attention a research output has received. It is based on 3 main factors:
- Volume: The more posts mentioning an output, the higher its attention score. It only counts one mention from each person per source. If you tweet about the same article twice, Altmetric will ignore everything but the first.
- Sources: Higher profile posts are worth more than lower profile ones in score terms. For example, a newspaper article contributes more than a blog post which contributes more than a tweet.
- Authors: Who authored each post is important. Altmetric looks at how often the author of each mention talks about scholarly articles, at whether or not there’s any bias towards a particular journal or publisher. For example, a science communicator sharing research to new audiences may carry more weight than a journal tweeting the same link.
From time to time you might notice that the Altmetric Attention Score for your paper fluctuates, or goes down. This can happen when the original author of the mentions deletes their post when we remove posts which have been flagged as spam, or occasionally when we add new sources so need to re-weight our scoring algorithm.
Altmetric Attention Score: Weighted count
The Altmetric Attention Score is a weighted count derived from an automated algorithm to reflect the relative reach of each type of source. Some sources have more weight points than the others. For example, a newspaper story is more likely to bring attention to the research output than a tweet.
The Altmetric Attention Score always has to be a whole number. This means that mentions that contribute less than 1 to the score sometimes get rounded up to one. So, if Altmetric picked up one Facebook post for a paper, the score would increase by 1, but if it picked up 3 more Facebook posts for that same article, the score would still only increase by 1.
Weight points by the source:
|
News |
8 |
|
Blog |
5 |
|
Policy document (per source) |
3 |
|
Patent |
3 |
|
Wikipedia |
3 |
|
Twitter (tweets and retweets) |
1 |
|
Peer review (Publons, Pubpeer) |
1 |
|
Weibo (not trackable since 2015, but historical data kept) |
1 |
|
Google+ (not trackable since 2019, but historical data kept) |
1 |
|
F1000 |
1 |
|
Syllabi (Open Syllabus) |
1 |
|
LinkedIn (not trackable since 2014, but historical data kept) |
0.5 |
|
Facebook (only a curated list of public Pages) |
0.25 |
|
|
0.25 |
|
Pinterest (not trackable since 2013, but historical data kept) |
0.25 |
|
Q&A (Stack Overflow) |
0.25 |
|
Youtube |
0.25 |
|
Number of Mendeley readers |
0 |
|
Number of Dimensions and Web of Science citations |
0 |
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