Social Media (SM) has transformed how content is shared, especially in the case of medicine. These platforms allow scientific literature to be shared and discussed. SM extends the reach of these scientific articles independent of their quality. The aim of this study was to discover whether sharing an article from Radiología on X (formerly known as Twitter) impacted its reach by analysing the number of downloads, taking into account the different article sections and types.
Material and methodsWe selected articles published in Radiología between 2020 and 2022. The articles were promoted by two X accounts: @SERAM_RX and @RevistaRADIOLO2, and downloads were analysed prior to and following dissemination on X.
ResultsA total of 100 articles were examined. There was a significant increase in downloads after dissemination on both X accounts (@SERAM_RX: median 49.50, IQR 18.50–73.50 vs median 76.50, IQR 23.75–19; P<,001 and @RevistaRADIOLO2: median 43, IQR 13.75–133 vs median 65,50, IQR 20,50–277,50; P<,001), with no significant differences between the two accounts. Posts from @RevistaRADIOLO2 generated more interactions (views, ‘likes’, reposts; P<,05). Clinical care articles were downloaded more from @SERAM_RX (@SERAM_RX: median 83, IQR 24–198 vs @RevistaRADIOLO2: median 38, IQR 16–79; P<.045).
ConclusionSharing articles on SM increases downloads. SM significantly increases the reach of scientific articles and the number of downloads, highlighting the importance of a solid editorial strategy for these platforms.
Las redes sociales (RRSS) han transformado la difusión de contenido, especialmente en medicina. Estas plataformas permiten compartir y debatir literatura científica. Las RRSS pueden potenciar la visibilidad de los artículos, lo que está relacionado con la calidad del trabajo. El objetivo del estudio fue conocer si la difusión de artículos de Radiología por la red social X (antes Twitter) influyó en la potenciación de su visibilidad a través del análisis del número de descargas, analizando diferencias dependiendo de la sección y los tipos de artículo, entre otros.
Material y métodosSe seleccionaron artículos de Radiología publicados entre 2020 y 2022. Los artículos fueron difundidos por dos cuentas de X: @SERAM_RX y @RevistaRADIOLO2, y se analizaron las descargas antes y después de la diffusion.
ResultadosSe estudiaron 100 artículos. Hubo un aumento significativo en las descargas tras la difusión en ambas cuentas de X (@SERAM RX: mediana 49,50, rango intercuartílico (RIC) 18,50–73,50 vs. mediana 76,50, RIC 23,75–19; P<,001 y @RevistaRADIOLO2: mediana 43, RIC 13,75–133 vs. mediana 65,50, RIC 20,50–277,50; P<,001), sin diferencias significativas entre las dos cuentas. Los tweets de @RevistaRADIOLO2 generaron más interacciones (visualizaciones, «me gusta», retweets; P<,05). Los artículos clínicos asistenciales se descargaron más desde @SERAM RX (@SERAM RX: mediana 83, RIC 24–198 vs. @RevistaRADIOLO2: mediana 38, RIC 16–79; P<,045).
ConclusiónLa difusión en RRSS aumenta las descargas de artículos. Las RRSS aumentan significativamente la visibilidad y descargas de artículos científicos, destacando la importancia de una estrategia editorial sólida en estas plataformas.
The way content is shared and disseminated has evolved dramatically since the advent of social media. In the field of medicine, this paradigm shift has been evident both in the dissemination of scientific literature and academic content aimed at medical specialists and residents,1 as well as in biomedical outreach to the general population.2,3 Through social media, authors can share links to their articles and conference presentations, ensuring that their work reaches other researchers and the general public.
In the case of scientific literature, social media is a meeting point for sharing and discussing publications.4 When an article gains attention on social media, it can go “viral”, attracting users who may not have found the publication through traditional channels. The loudness of these platforms and their relevance has led different companies to provide bibliometric indicators that include the impact of articles on social media, known as altmetrics.5 These indicators collect information from social media such as X (formerly Twitter), Facebook, LinkedIn and ResearchGate. However, these altmetrics are not comparable to traditional bibliometric indicators such as the widely used Impact Factor (IF) or the Citescore. Sharing an article on social media is not necessarily synonymous with quality, as any user can share the information and even the author can share it through different profiles to make it public.6
This revolution in the dissemination of research has led more and more scientific journals to establish profiles on the various social media platforms in order to promote their content among their followers and gain visibility in the scientific field. However, each journal uses different strategies to share its content depending on the target and type of social media.7 While most scientific journals are accessible by subscription, social media are free tools aimed at a global audience and this can have an impact on the number of article views8 and downloads.9 In terms of the impact of disseminating an article through social media on the number of citations, there is considerable variability depending on the different medical subspecialities.5
The main objective of this study was to find out whether the dissemination of a Radiología [Radiology] article through the social network X influenced the number of downloads, thereby boosting its visibility. It was also analysed whether or not there were differences in the number of downloads depending on the X profile from which it was disseminated, the section and the type of article.
Material and methodsSelection of articlesThis was a descriptive observational study. Inclusion criteria were established as those articles published in Radiología in the period 2020–2022 in both regular issues and supplements, from all sections.
Articles that had been shared on social media by any account in the three months before and three months after the “tweet” was posted were excluded. Editorials and letters to the editor were excluded. We also excluded advance online articles and those which had been included in any of the issues of Radiología in 2023.
The final selection was made by the Editor-in-Chief of the journal from all the articles that met the inclusion criteria. The main focus was on the reverse chronological factor (starting with the oldest articles within the period covered).
Social media outreach strategyThe articles were disseminated consecutively by two different X (previously known as Twitter) profiles: @SERAM_RX, which is the official account of the Sociedad Española de Radiología Médica (SERAM) [Spanish Society of Medical Radiology] and @RevistaRADIOLO2, official account of the journal Radiología, SERAM's mouthpiece. The number of followers of @RevistaRADIOLO2 on 2 December 2023 was lower than the SERAM account (9170 followers vs 5832 followers of the journal Radiología).
The articles began to be disseminated from @SERAM Rx on 25 September 2022 on a weekly basis (every Wednesday at 6:00 p.m.). The last one was disseminated on 27/09/2023. In addition to X, on the same day they were posted on Instagram, LinkedIn and Facebook.
They began to be posted from the Radiología profile in January 2023 (specifically on 31/01/2023) at a rate of two per week (every Tuesday at 6:00 p.m. and every Saturday at 12:00 p.m.). The latest was published on 09/09/2023. Radiología does not have a profile on other social media platforms, so dissemination was limited to X. In July and August, some advance online articles were published or had been recently published that did not meet the inclusion criteria and were therefore not analysed.
The link to the article was shared on the home page of Elsevier Radiología, through which SERAM members can access the Spanish version of the journal (https://www.elsevier.es/es-revista-radiologia-119). Access to the article from other databases such as ScienceDirect or PubMed was not shared.
Depending on the subject of the article, the respective SERAM sections and the author of the article were tagged if they had a profile on X.
Variables collectedType of articleAccording to journal guidelines, articles were classified into three groups: original (including research original and clinical original); review (including update, imaging radiology, resident's article, series, radiology today, humanities and radiology and special article); and short articles (short communication and scientific letters).
They were also classified according to the COVID subject matter and the different sections into: abdomen-genitourinary; training; management and quality; cardiothoracic imaging; paediatric imaging; breast; musculoskeletal; neuroradiology; vascular-interventional; ultrasound; and emergency.
Types of interactionIn the first week of December 2023, we consulted all tweets that had been created and collected the views, “retweets” and “likes” they had received.
DownloadsWe analysed the average number of downloads of each article included in the three months prior to its dissemination on X and its average number of downloads in the three months after its dissemination (the month in which it was published and the two following months).
Statistical analysisFrequency tables were used for descriptive analysis. Differences between proportions were analysed using Pearson's X2 test. In the description of the sample, variables that showed normal distribution were expressed as mean and standard deviation, while variables that did not follow normal distribution were expressed as median and interquartile range (IQR). To compare numerical variables between two related groups, the Wilcoxon test was used for non-normal distribution and the T-test for two related samples in the case of normal distribution. For comparisons between two independent numerical variables, the Mann-Whitney test or Student's t test was applied for independent samples, depending on whether or not the variables followed a normal distribution. A P-value <.05 was considered to indicate a statistically significant difference. Data were analysed by IBM SPSS Statistics software, version 29.0.2.0.
The following relationship analyses were established:
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Difference between the average number of downloads before and after dissemination on the social network X.
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If there was a difference between downloads depending on whether the article was disseminated through the @SERAM_RX or @RevistaRADIOLO2 profile on X.
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Whether there were differences in the average number of downloads between tweets posted on weekdays or weekends from @RevistaRADIOLO2 and weekdays or weekends in the overall sample.
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Relationship between the X account and interaction type (views, likes, retweets). For this analysis, nine articles were excluded (all disseminated through the @SERAM_RX account) for which no information on views was available.
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Whether there was a difference in downloads between article type (both by type of journal standards and by section).
A total of 100 articles published in the journal Radiología from 2020 to 2022 were disseminated on the social network X. Of the total number of articles, 50 were published through the @SERAM_RX account and the other 50 through the @RevistaRADIOLO2 profile. Table 1 summarises the characteristics of the articles disseminated.
Article characteristics (type of article, COVID subject matter and sections) disseminated through @SERAM_RX and @RevistaRADIOLO2.
| Account @SERAM_RX (n 50) | Account @RevistaRADIOLO2 (n 50) | P-value | |
|---|---|---|---|
| Type of article n (%) | 0.359 | ||
| Original | 14 (28) | 15 (30) | |
| Review | 34 (68) | 35 (70) | |
| Short articles | 2 (4) | 0 (0) | |
| COVID subject matter n (%) | 6 (12) | 4 (8) | .505 |
| Sections n (%) | 0.001 | ||
| Abdomen-GU | 9 (18) | 13 (26) | |
| Neurology | 9 (18) | 3 (6) | |
| Cardiothoracic | 10 (20) | 16 (32) | |
| Breast | 8 (16) | 1 (2) | |
| Paediatric | 2 (4) | 8 (16) | |
| IVR | 8 (16) | 1 (2) | |
| Management | 4 (8) | 4 (8) | |
| Musculoskeletal | 4 (8) | 0 (0) | |
| Healthcare n (%) | 45 (90) | 35 (70) | .012 |
GU: genitourinary; IVR: interventional vascular radiology.
Significant differences were found in the average number of downloads of articles before and after dissemination, both through the X account @SERAM_RX (median 49.50, IQR 18.50−73.50 vs median 76.50, IQR 23.75–196; P<.001) and the X account @RevistaRADIOLO2 (median 43, IQR 13.75−133 vs median 65.50, IQR 20.50−277.50; P<.001). This increase in downloads was seen in both original and review articles (@SERAM_RX: originals P.043, reviews P.001; @RevistaRADIOLO2: originals P.001, reviews P.001). The average number of downloads before and after dissemination by X account and type of article (original and review) is summarised in Table 2. However, overall there was no increase in the average number of downloads when comparing articles disseminated in one account to the other (P=.860). The data on the average number of article downloads in the three months prior to their dissemination on the social network X, in the first month and the subsequent two months following their dissemination, and the difference in the average number of downloads, are summarised in Table 3.
Comparison of the average number of article downloads before and after dissemination on X.
| Average number of downloads 3 months prior to dissemination Median (IQR) | Average number of downloads (that month and the following two months) Median (IQR) | P-value | |
|---|---|---|---|
| Account @SERAM_RX (n 50) | 49.50 (18.50−73.50) | 76.50 (23.50−198) | <.001 |
| Originals (n 14) | 45.50 (29−91.25) | 71.50 (24.50−134.25) | .043 |
| Reviews (n 34) | 52 (18.50−78.50) | 85.50 (22.75−243.75) | .001 |
| Account @RevistaRADIOLO2 (n 50) | 43 (13.75−133) | 65.50 (20.50−277.50) | <.001 |
| Originals (n 15) | 46 (14−354) | 56 (16−780) | .001 |
| Reviews (n 35) | 40 (13−100) | 67 (22−269) | <.001 |
IQR: interquartile range.
Comparison between the @SERAM_RX and @RevistaRADIOLO2 X accounts of the average number of downloads three months before dissemination of the article and that month and the following two months after dissemination of the article.
| Account @SERAM_RX (n 50) | Account @RevistaRADIOLO2 (n 50) | P-value | |
|---|---|---|---|
| Average number of downloads 3 months prior to dissemination Median (IQR) | 49.50 (18.50−73.50) | 43 (13.75−133) | .942 |
| Average number of downloads (that month and the following two months) Median (IQR) | 76.50 (23.50−198) | 65.50 (20.50−277.50) | .885 |
| Difference between the average number of downloads Median (IQR) | 24 (2.75−99.25) | 18.50 (4.75−145.25) | .860 |
IQR: interquartile range.
For the day of the week on which the article was posted from the @RevistaRADIOLO2 account (Tuesday or Saturday), there was no difference in the average number of downloads after the tweet (median 14, IQR 5–146; median 33, IQR 0–145; P.984). Analysing the average number of downloads in the first month and the subsequent two months following publication on X in the overall sample according to whether the tweet was posted during the week or at the weekend, we also found no statistically significant differences (median 22, IQR 4–109; median 18, IQR 1–133; P.796).
With regard to the interactions generated, a greater number of views (P.033), likes (P.003) and retweets (P.001) were found from the @RevistaRADIOLO2 account. For this analysis, nine articles disseminated through the @SERAM_RX account were excluded as not all the variables being studied were available. The interactions generated by users (views, retweets and likes), are summarised in Fig. 1. There were no statistically significant differences in tweet interactions according to whether they were posted on weekdays or weekends in the overall sample (views: median 2198, IQR 1256.75–3123; median 2511, IQR 1401–3335; P.269, retweets: median 6, IQR 4–11; median 8, IQR 5–12.5; P.174 and likes: median 9, IQR 16–26; median 16, IQR 12.5–29; P.321) or on the @RevistaRADIOLO2 account (views: median 2445, IQR 1488–3135; median 2511, IQR 1401–3335; P.899, retweets: median 9, IQR 6–13; median 9, IQR 5–13; P.526 and likes: median 21, IQR 14–29; median 23, IQR 13–29; P.846).
Bar charts representing the interactions generated by users with the articles disseminated through the @SERAM_RX and @RevistaRADIOLO2 X accounts. Variables are expressed as median (interquartile range).
For this analysis, nine articles disseminated through the @SERAM_RX account were excluded as not all the variables being studied were available.
Interestingly, articles on healthcare topics (excluding management, training and artificial intelligence [AI]) had a higher number of downloads after dissemination through the @SERAM_RX account compared to the @RevistaRADIOLO2 account (median 83, IQR 24–198; median 38, IQR 16–79, respectively; P.045). When analysing downloads according to the section to which the article belongs, for articles related to cardiothoracic imaging or specifically related to COVID-19, there were no statistically significant differences between the average number of downloads before and after dissemination between the two X accounts (@SERAM_RX median 101, IQR 33–242; @RevistaRADIOLO2 median 37, IQR 5–131; P.285). There were also no significant differences in downloads between the @SERAM_RX and @RevistaRADIOLO2 X accounts when analysing the three sections with the most articles (Table 4).
Comparison of the average number of downloads between the @SERAM_RX and @RevistaRADIOLO2 X accounts after dissemination, for the three sections with the most shared articles.
| Average number of downloads after dissemination in account @SERAM_RX Median (IQR) | Average number of downloads after dissemination in account @RevistaRADIOLO2 Median (IQR) | P-value | |
|---|---|---|---|
| Abdomen-GU | 99 (29.5−704) | 42 (25.50−183.50) | .443 |
| Neurology | 63 (16.50−182.5) | 105 (33−105) | .598 |
| Cardiothoracic | 175 (95.5−671.75) | 286 (67.50−1142) | .121 |
GU: genitourinary; IQR: interquartile range.
Our results show a significant increase in the number of downloads of articles from the journal Radiología following their dissemination on the social media platform X. There were no differences in the number of downloads between the two profiles used for the analysis, whether it was the official profile of the Sociedad Española de Radiología Médica (@SERAM_RX) or the journal's own profile (@RevistaRADIOLO2). However, there were differences in user interactions with the content, with a higher number of views, retweets and likes for tweets published from the @RevistaRADIOLO2 account.
These findings are in line with much of the literature.9–11 Sharing articles through official social media channels makes it easier to reach a wider audience. However, it should be borne in mind that these results are limited to sharing and downloading, and do not necessarily reflect an overall increase in the uptake or audience engagement with the publishing journal.12 Similarly, a higher number of visits and downloads does not reflect the scientific quality of the article, as it has been found that withdrawn articles have a greater impact on Altmetrics13 due to their controversial component.
We should point out that the articles with which we have compared our results are not radiological in profile, as we did not find any studies in our area that analyse the impact of social media on article downloads.
The @RevistaRADIOLO2 profile had more retweets (sharing content), likes and views than the @SERAM_RX profile, despite having a smaller number of followers (>6000 followers for @RevistaRADIOLO2 vs >9500 followers for @SERAM_RX at the time of writing). This behaviour is difficult to explain, but it may be multifactorial, as the @SERAM_RX profile is not exclusive to scientific publications and also shares institutional information; while the @RevistaRADIOLO2 profile shares almost exclusively editorial content with a lower frequency of publication, perhaps allowing more interaction time per post.
At the same time, only articles categorised as healthcare-related (clinical articles not including management, training or AI) were downloaded more frequently from the @SERAM_RX profile. There is not much literature on the subject, but it does seem that educational articles and case reports tend to be shared more often and have more interactions on social media than scientific articles.14 This can be used to help determine editorial strategies to identify which article types are most in demand or arouse readers' curiosity.
Our study is not without limitations. The download analysis was carried out from the publisher's local website (Elsevier) and not from ScienceDirect; this limitation is due to the fact that the hyperlink or link used comes from Elsevier directly. While @RevistaRADIOLO2 only disseminates its content on X, SERAM has profiles on other social media and this may interfere with download rates from their respective profiles, and may overestimate the impact of @SERAM_RX on X. We should also point out the intrinsic limitations of the sample size; although sufficient to establish general trends, it does not allow us to analyse subspecialities with sufficient statistical power to identify areas of interest to our readers. Finally, the study was limited to downloads and views, without exploring citation rates or other impact metrics.
ConclusionThe dissemination of scientific content through social media significantly increases the number of downloads and the visibility of articles. It is very important to establish an editorial strategy, with a constant presence on social media that guarantees the availability of content at all times.
CRediT authorship contribution statement- 1
Person responsible for the integrity of the study: CGV.
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Study conception: CGV.
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Study design: CGV.
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Data collection: CGV and Elsevier.
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Data analysis and interpretation: ESA and BDX.
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Statistical processing: ESA and BDX.
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Literature search: DHG.
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Drafting of the article: DHG, ESA, BDX and CGV.
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Critical review of the manuscript with intellectually relevant contributions: DHG, ESA, BDX and CGV.
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Approval of the final version: DHG, ESA, BDX and CGV.
The authors declare that they did not receive any funding to carry out this study.
