Dataset Information

If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations.

ABSTRACT: Objectives: We previously reported that random assignment of scientific articles to a social media exposure intervention did not have an effect on article downloads and citations. In this paper, we investigate whether longer observation time after exposure to a social media intervention has altered the previously reported results.

Methods: For articles published in the International Journal of Public Health between December 2012 and December 2014, we updated article download and citation data for a minimum of 24-month follow-up. We re-analysed the effect of social media exposure on article downloads and citations.

Results: There was no difference between intervention and control group in terms of downloads (p?=?0.72) and citations (p=?0.30) for all papers and when we stratified by open access status.

Conclusions: Longer observation time did not increase the relative differences in the numbers of downloads and citations between papers in the social media intervention group and papers in the control group. Traditional impact metrics based on citations, such as impact factor, may not capture the added value of social media for scientific publications.

SUBMITTER: Tonia T

PROVIDER: S-EPMC7716910 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations.

Tonia Thomy T Van Oyen Herman H Berger Anke A Schindler Christian C Künzli Nino N

International journal of public health 20201107 9

<h4>Objectives</h4>We previously reported that random assignment of scientific articles to a social media exposure intervention did not have an effect on article downloads and citations. In this paper, we investigate whether longer observation time after exposure to a social media intervention has altered the previously reported results.<h4>Methods</h4>For articles published in the International Journal of Public Health between December 2012 and December 2014, we updated article download and cit ...[more]

PMID: 33159529

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Project description:BackgroundEngaging influential stakeholders in meaningful exchange is essential for pharmaceutical companies aiming to improve care. At a time where opportunities for face-to-face engagement are limited, the ability to interact, learn and generate actionable insights through digital channels such as Twitter, is of considerable value.AimThe aim of this study was to evaluate digital engagement among global diabetes mellitus researchers.Materials and methodsWe identified every global tweet (20,614,515) and scientific publication (44,135) regarding diabetes mellitus from 1 August 2018 to 1 August 2020. Through author matching we combined datasets, resulting in a list of digitally active scientific authors. Generalised linear modelling identified factors predicting their digital engagement.FindingsGlobally, 2686 diabetes researchers used Twitter to discuss the management of diabetes mellitus, posting 110,346 diabetes-related tweets. As Twitter followers increased, so did tweet frequency (p < 0.001), retweets (p < 0.001) and replies (p < 0.001) to their content. Publication count (overall/per month) and proportion of first/last authorships were unrelated to tweet frequency and the likelihood of being retweeted or replied to (p > 0.05). Those with the most academic co-authors were significantly less likely to tweet than those with smaller networks (< 50; p = 0.001). Finally, those publishing most frequently on specific themes, including insulin (p = 0.041) and paediatrics (p < 0.001), were significantly more likely to tweet about these themes.ConclusionAcademic expertise and seniority cannot be assumed as proxies for digital influence. Those aiming to promote science and obtain digital insights regarding condition management should consider looking beyond well-known 'key opinion leaders' to perhaps lesser known 'digital opinion leaders' with smaller academic networks, who are likely to specialise in the delivery of highly specific content to captive audiences.

Project description:BACKGROUND:Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information. OBJECTIVE:The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail. METHODS:A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets' metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed. RESULTS:A total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet's author was tagged as favorited by the community. CONCLUSIONS:The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida-a decorative element in Arabic writing used to justify the text within lines-were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.

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Dataset Information

If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations.

Publications

If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations.

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