Twitter analytics Sample Clauses

Twitter analytics. Twitter analytics offers the following data, which will be useful for the @togethersci Twitter account: • Tweet impressions • Profile visits • Mentions • Number of followers • Engagements (how many replied, clicked on links) • Which tweet earned the most impressions This allows us to monitor roughly how many people are reading tweets at any given moment, which should be the size of our audience. However, for tweets in quick succession, ‘impressions’ are likely to be the same people so cannot be added together. It is therefore best to take the highest number of impressions for a given period of time and assume that this is the size of the audience. Similarly, we can assume that ‘followers’ and ‘impressions’ are likely to be the same people, so we should simply take the largest number for a given period of time, not add them up. This data will allow us to analyse what types of tweet are successful in creating engagement: for example, which tweets get the most impressions/RTs/replies (and we can then analyse: are these links to our work or to other news sites, questions, interesting statements?), and the interests of our followers. We can judge that if a high percentage of our followers’ ‘top interest’ is science, or a very high percentage ‘male’, we are unlikely to be reaching our target audiences. However, Twitter users do not provide this information to Twitter and it is based on analysis of their activities, so we should assume these figures will have some margin of error. We may be better able to judge whether we are reaching target audiences by our interactions with people on social media, for example by the types of questions people ask. We monitor impressions not only for the @togethersci account, but also relevant accounts of the DITOs consortium members, for example @mhaklay, and @IAmCiSci. These figures will be collated over time every quarter to show our increase in audience numbers and target audience members reached.
Sample 1
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