Twitter

By N. Paradis, M. Knoll, C. Shah, C. Lambert, G. Delouya, H. Bahig, D. Taussky. American Journal of Clinical Oncology, 2020

1,294 citations recorded by the source corpus.

Objective: The objective of this study was to examine the correlation between Twitter mentions and the number of academic citations of radiation oncology articles. Materials and Methods: We reviewed all 178 clinical manuscripts of the 2 most important radiation oncology journals and “Brachytherapy,” and all clinical manuscripts relating to radiation oncology from the top 10 impact factor oncology journals, published between January and February 2018. We collected the record of citations utilizing Scopus and Google Scholar platforms and the number of times an article was tweeted about using the “Altmetric Bookmarklet.” χ2 test was used to compare distributions between groups and the Pearson coefficient was used for correlations between the Twitter metrics and academic citations. Results: Overall, 71% of all articles were tweeted about at least once. There was a significant correlation between the number of tweets and the number of citations in Google Scholar (r=0.55, P<0.001) and in Scopus (r=0.59, P<0.001). The 11% of articles with a prepublication Twitter “buzz” (defined as an article with ≥10 tweets before publication) had 3.6 times more citations in Scopus (mean: 14.8 vs. 4.2, P<0.001) and 2.9 times more citations in Google Scholar (17.8 vs. 6.0, P<0.001) when compared with papers with no “buzz.” Conclusions: Presence on Twitter was correlated with the number of academic citations of an article in radiation oncology. This suggests that Twitter is being utilized by the oncology community as a platform to discuss and disseminate high impact scientific articles. The correlation between Twitter and increasing the number of citations of an article through larger dissemination and exposure requires further studies.

Subfields: General Health Professions, Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d5f3c75ca69d1522cff0cff45fc363f49e66b6f3

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In General Health Professions

Emilio Ferrara

In Health

Emilio Ferrara

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Citations in the corpus

Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT (2023, background): “The popularity of ChatGPT, as evidenced by the rapidly growing number of registered users (Altman, 2022), suggests that this type of technology is poised to become increasingly integrated into society.”
Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks (2018, methodology): “We analyze three pages that belong to important Google web services (Google Docs [44], Gmail [42], Google Calendar [43]), two of the top 25 most-accessed web sites [7] (WordPress [149] and Twitter [142]), and one animation-heavy page [23].”
The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic (2020, background): “One of these inspiring examples is Dr. Yale Tung Chen, an EP who chronicled his COVID-19 infection, including ultrasound ﬁ ndings, 9 and simultaneously demystifying the illness while educating the world in a novel format.”
Large Language Model Unlearning (2023, background): “Local community compliance policy can iterate frequently [TikTok, 2023, Twitter, 2023, Facebook, 2023].”
Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (2020, background): “Verified users on Twitter have been identified by Twitter as accounts of public interest and are verified to be authentic accounts [36].”
UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception (2020, background): “The list also includes applications such as Facebook [9], Amazon [1], Twitter [25], Netﬂix [15], and Spotify [22].”
How and why patients made Long Covid (2020, neutral): “In March, patients started sharing experiences on social media, drawing attention to possible Covid-related sequelae (e.g. Pope, 2020).”
Learning Deep Mean Field Games for Modeling Large Population Behavior (2017, background): “…of a set of trending topics on social media, represented by the global population distribution over topics, while each user’s observation of this global state inﬂuences their choice of the next topic in which to participate, thereby contributing to future population behavior (Twitter, 2017).”