The Altmetric Attention Score is a weighted count of the amount of attention we've picked up but we also take into account many other factors depending on the type of source the attention comes from. Below are the most common modifiers that affect the Altmetric Attention Score:
News outlets are assigned a tier, based on the reach we determine that outlet to have. The amount a news mention contributes to the score depends on the tier for that news source. This means that a mention from a popular national news outlet such as The New York Times will contribute more to the score than a news mention from a smaller, more niche publication such as 2Minute Medicine.
The scoring for Wikipedia articles is static. This means that if a research output is mention in one Wikipedia post, the score for that paper will increase by 3. However, if a research output is mentioned in more than one Wikipedia post, the score will remain 3. This is because a reference to a research output in a Wikipedia post that may also mention lots of other outputs in its bibliography is not really comparable to a mainstream news story about the findings of one research output, in terms of reach and attention. Part of the rationale behind the Wikipedia scoring is also to prevent gaming; we wanted to prevent a situation where researchers could potentially bias their scores by retrospectively adding references to their research outputs in lots of different Wikipedia posts.
Mentions in policy documents are scored per source. A mention of a research output in a policy document has a default score contribution of 3. This means that if an output is mentioned in more than one policy document from the same policy source (e.g. gov.uk), the score would increase by 3. However, if an output is mentioned in two policy documents from two different policy sources (e.g. gov.uk and the International Monetary Fund) the score would increase by 6.
The scoring for Open Syllabus attention is static. This means that if a research output is mentioned in one syllabi, the score for that paper will increase by 1. However, if a research output is mentioned in more than one syllabi, the score will remain 1.
Mentions in patent citations are scored per jurisdiction. A mention of a research output in a patent has a default score contribution of 3. If the publication is then mentioned in another patent from a different jurisdiction the score will increase to 6. If the publication is mentioned by 10 patents from the same jurisdiction then the contribution remains 3.
Twitter and Sina Weibo
For Twitter and Sina Weibo, re-tweets and re-posts count for 0.85, rather than 1, as they are secondhand attention rather than original attention. The combined total of these re-tweets or re-posts will always be rounded up to the nearest whole number.
With Twitter posts, we apply modifiers to the score based on three principles:
reach - how many people are likely to see the tweet - this is based on the number of followers attached to the account.
promiscuity - how often does this person tweet about research outputs?
bias - is this person/account tweeting about lots of papers from the same journal domain, thereby suggesting promotional intent?
These modifiers mean that a Tweet from a publisher journal account will count for less than a tweet from a researcher who is unconnected to the paper and is sharing it more organically. This can also work the other way - if a hugely influential figure were to tweet about a research output, this could contribute 1.1 to the score, which would then be rounded up to 2.
Mentions that never count towards the score
This applies to Mendeley and CiteULike readers, as we can't display the actual profiles, and we want all our data to be fully auditable.
Only the first mention from a source counts towards the score. If a news source publishes multiple stories then only the first one will contribute to the Altmetric Attention Score for that particular output.