Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters

Yu, Houqiang; Xiao, Tingting; Xu, Shenmeng; Wang, Yuefen

doi:10.1016/j.joi.2019.08.001

Cited by 25 publications

(26 citation statements)

References 26 publications

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“…At the user level, the presence of scholarly tweets makes it possible to recognize and characterize users discussing science on Twitter. Scholarly tweets, therefore, were drawn upon for identifying and classifying Twitter users participating in scholarly communication (Costas et al, 2020;Díaz-Faes et al, 2019;Vainio & Holmberg, 2017;Yu et al, 2019), and for further exploring how users by type performed differently while utilizing Twitter for scholarly communication (Didegah et al, 2018;Holmberg & Thelwall, 2014;Mohammadi et al, 2018;Yu, 2017). Besides the research on the characterization of individual users who posted scholarly tweets, the Twitter uptake and usage of various scholarly entities, such as scholarly book publishers (Wang & Zuccala, 2021), scholarly journals (Zheng et al, 2019), and universities (Yolcu, 2013), is also an important topic in the direction of secondary social media metrics.…”

Section: Scholarly Tweets As the Objects Of Studymentioning

confidence: 99%

User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis

2022

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This study investigates the extent to which scholarly tweets of scientific papers are engaged with by Twitter users through four types of user engagement behaviors, i.e., liking, retweeting, quoting, and replying. Based on a sample consisting of 7 million scholarly tweets of Web of Science papers, our results show that likes is the most prevalent engagement metric, covering 44% of scholarly tweets, followed by retweets (36%), whereas quotes and replies are only present for 9% and 7% of all scholarly tweets, respectively. From a disciplinary point of view, scholarly tweets in the field of Social Sciences and Humanities are more likely to trigger user engagement over other subject fields. The presence of user engagement is more associated with other Twitter-based factors (e.g., number of mentioned users in tweets and number of followers of users) than with science-based factors (e.g., citations and Mendeley readers of tweeted papers). Building on these findings, this study sheds light on the possibility to apply user engagement metrics in measuring deeper levels of Twitter reception of scholarly information.

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Section: Scholarly Tweets As the Objects Of Studymentioning

confidence: 99%

User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis

2022

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“…Secondly, the large amount of retweets produced by simply copying the original tweets (boyd et al 2010) increase the repetition rate of hashtags used on Twitter. Besides, there is a large group of scholars with publications included in the WoS database who are also active on Twitter (Costas et al 2017;Yu et al 2019), which means that these scholars may use the same academic terms in their Twitter use of hashtags.…”

Section: Discussionmentioning

confidence: 99%

How do academic topics shift across altmetric sources? A case study of the research area of Big Data

Lyu¹,

Costas²

2020

Scientometrics

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Taking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science (WoS) and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia, and Twitter. Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Different measures are applied to determine the (dis)similarities between the topics put forward by the publication authors and those by the online audiences. Results show that overall there are substantial differences between the two sets of topics around Big Data scientific research. The main exception is Twitter, where high-frequency hashtags in tweets have a stronger concordance with the author keywords in publications. Among the online communities, Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research. Specifically, the audiences not only focus on more easyto-understand academic topics related to social or general issues, but also extend them to a broader range of topics in their online discussions. This study lays the foundations for further investigations about the role of online audiences in the transformation of academic topics across altmetric sources, and the degree of concern and reception of scholarly contents by online communities.

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“…Semantic-based supervised classification of tweets based on lexical knowledge resources and WordNet domain for classification is possible [ 14 ]. Another study explored the detection of scientific tweets by analysing 2.63 million scientific tweets and investigated user account types and their geographical location using a feature-based approach [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Malik

Aslam

et al. 2022

Computational Intelligence and Neuroscience

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People’s lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people’s interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

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Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters

Cited by 25 publications

References 26 publications

User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis

User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis

How do academic topics shift across altmetric sources? A case study of the research area of Big Data

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

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