How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications

Fang, Zhichao; Costas, Rodrigo; Tian, Wencan; Wang, Xianwen; Wouters, Paul

doi:10.1002/asi.24458

Cited by 27 publications

(15 citation statements)

References 42 publications

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“…Mohammadi et al (2018) surveyed the motivations behind users' liking and retweeting behaviors in scientific contexts and reported that most survey respondents liked a tweet to "inform the authors that their tweets were interesting" and retweeted for the sake of disseminating the tweets. Based on the click metrics data provided by Bitly for its generated short links, Fang et al (2021) analyzed the click rates on Twitter of short links referring to scientific papers and concluded that nearly half of the studied scholarly short links were not clicked by Twitter users at all.…”

Section: User Engagement Behaviors Around Scholarly Tweetsmentioning

confidence: 99%

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

2022

Self Cite

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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: User Engagement Behaviors Around Scholarly Tweetsmentioning

confidence: 99%

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

2022

Self Cite

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“…Even more strikingly, this modest reward pales in terms of costs/benefits when compared to simply omitting descriptors from the title. This is hardly surprising given the current landscape of ever-increasing new articles (59), fast and short communication [e.g., advertising through tweets; (60)], and the decreasing attention span of readers (61).…”

Section: Discussionmentioning

confidence: 99%

How much biodiversity is concealed in the word “biodiversity”?

Mammola¹,

Fukushima²,

Biondo³

et al. 2022

Preprint

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Amidst a global biodiversity crisis, the word “biodiversity” has become indispensable for practical conservation, including as a normative term. Yet, biodiversity is often used as a buzzword in scientific literature. Resonant titles promoting to have studied “global biodiversity” may then be used to oversell research that is narrow-focused on a limited sample of taxonomic groups, regions, or habitats. We selected a random sample of ~900 papers with the word “biodiversity” in their title to take a long view of the use and misuse of this term. We analyzed the degree to which studies actually consider different taxonomic groups and biodiversity facets and how all of this translates to the impact of a paper. As many as 22% of the articles used the term biodiversity in the title but did not measure it at any level. Among the articles sampling biodiversity directly, the proportion of biodiversity investigated was systematically low. We documented a decrease in the taxonomic scope of articles in recent years, especially those relying on big data. This is in stark contrast with the parallel advances in analytical tools, monitoring technologies, and the availability of data. Importantly, studies with general titles (i.e., using the word “biodiversity” without mentioning any taxa, habitat, or region) attract more citations and online attention (Altmetric), but only when they also have a wider taxonomic scope. Our results have broad ramifications for understanding how the extrapolation from studies with narrow taxonomic scope shapes our view of global biodiversity patterns and poorly informs conservation practices.

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“…Chaining in this manner requires scientists to maintain the relevance and coherence between papers as their number grows exponentially. The second kind leverages a different type of network, which is social in nature, to support serendipitous sharing and finding of recommendations on social media platforms such as Twitter [37,55,63,81,114] or cold-emailing high-profile experts in an outside field to receive valuable bibliography [30,89,90,115].…”

Section: Related Work 21 Exploratory Search Needs In Scholarly Recomm...mentioning

confidence: 99%

From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

Kang

Kocielnik

Head

et al. 2022

CHI Conference on Human Factors in Computing Systems

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The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement-and future engagement when mediated by authors-without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.

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How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications

Cited by 27 publications

References 42 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 much biodiversity is concealed in the word “biodiversity”?

From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

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