Fang, Z.; Dudek, J.; Costas, R.: Facing the volatility of tweets in altmetric research (2022)
0.01
0.01479397 = product of:
0.02958794 = sum of:
0.02958794 = product of:
0.05917588 = sum of:
0.05917588 = weight(_text_:56 in 605) [ClassicSimilarity], result of:
0.05917588 = score(doc=605,freq=2.0), product of:
0.21469334 = queryWeight, product of:
4.1578603 = idf(docFreq=1879, maxDocs=44218)
0.051635534 = queryNorm
0.2756298 = fieldWeight in 605, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1578603 = idf(docFreq=1879, maxDocs=44218)
0.046875 = fieldNorm(doc=605)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- The data re-collection for tweets from data snapshots is a common methodological step in Twitter-based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID-19-related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. "Revived" tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers.
