ArticlePloS one2017
The unbearable emptiness of tweeting-About journal articles.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- A role for qualitative methods in researching Twitter data on a popular science article's communication.Frontiers in research metrics and analytics · 2024Article
- Introducing the EMPIRE Index: A novel, value-based metric framework to measure the impact of medical publications.PloS one · 2022Article
- Are automated accounts driving scholarly communication on Twitter? a case study of dissemination of COVID-19 publications.Scientometrics · 2022Article
- If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations.International journal of public health · 2020Observational
- Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation.PLoS biology · 2020Article
- Measuring an individual researcher's impact: new directions and challenges.Evidence-based mental health · 2020Review
- Measuring the impact of pharmacoepidemiologic research using altmetrics: A case study of a CNODES drug-safety article.Pharmacoepidemiology and drug safety · 2020Review
- Article
- Analysis of highly tweeted dental journals and articles: a science mapping approach.British dental journal · 2019Article
- How significant are the public dimensions of faculty work in review, promotion and tenure documents?eLife · 2019Article
- Scientific Twitter: The flow of paleontological communication across a topic network.PloS one · 2019Article
- Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science.PloS one · 2019Article
- Does Twitter language reliably predict heart disease? A commentary on Eichstaedt et al. (2015a).PeerJ · 2018Article
- Citations and science.International journal of clinical pharmacy · 2017Article
- The unbearable emptiness of tweeting-About journal articles.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined. The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots. Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.