Early indicators of scientific impact: Predicting citations with altmetrics

Akella, Akhil Pandey; Alhoori, Hamed; Kondamudi, Pavan Ravikanth; Freeman, Cole; Zhou, Huaiying

doi:10.1016/j.joi.2020.101128

Cited by 49 publications

(26 citation statements)

References 93 publications

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“…Digital Journalism has the highest impact factor among the journalism journals, and regarding Altmetric score's different sources, Digital Journalism has a higher twitter mention than the other journals-this may be because the journal itself has higher engagement with Twitter than other journals. Other studies have indicated that Twitter mentions can be a predictor of early citations of papers (Akella et al, 2021) and this may be the case for this journal.…”

Section: Discussionmentioning

confidence: 69%

An analysis of journalism articles achieving high Altmetric attention scores

Nabavi

2022

Learned Publishing

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New methods of judging the impact of academic articles now include alternative metrics, and the goal of this study was to provide an insight into the journals and papers with top Altmetric attention scores (AAS) in the field of journalism. Scopus and Dimensions were used as the primary data sources. Fifteen journalism journals were identified from Scopus, and papers from these journals with an Altmetric Attention Score of over 100 were collected from Dimensions as the study's sample, which comprised 87 papers. Most of the papers with high AAS were published after 2017, and five were published in 2022. The sample included a larger number of closed access articles (n = 50) than open access (n = 37), although analysis revealed that open access articles had higher median Tweets than closed access. Articles on journalism practice were more likely to receive attention from news outlets. None of the papers with high AAS are highly cited, which may be due to the limited time to accumulate citations. The journal with the highest impact factor (Digital Journalism) did not have the greatest number of papers with high AAS, but had far higher scores on Twitter engagement than the other journals. The results do not show any correlation between impact factors and citation metrics and social metrics.

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Section: Discussionmentioning

confidence: 69%

An analysis of journalism articles achieving high Altmetric attention scores

Nabavi

2022

Learned Publishing

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“…Recently, researchers were able to predict scholarly citations using altmetrics (Akella et al, 2021).…”

Section: Online Impact Of Scholarly Articlesmentioning

confidence: 99%

Quantifying the online long-term interest in research

Shahzad

Alhoori

Freedman

et al. 2022

Journal of Informetrics

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“…We used Altmetrics data released from Altmetric.com in July 2018. Altmetrics (Akella et al, 2021;Alhoori et al, , 2015, consist of mentions of scholarly articles in online social media, such as Facebook, Twitter, and Wikipedia, and in online reference managers, such as Mendeley. The 2018 release of the Altmetric dataset consists of the details about the online mentions of about 19 million publications.…”

Section: Datamentioning

confidence: 99%

Public Reaction to Scientific Research via Twitter Sentiment Prediction

Shahzad

Alhoori

2021

Journal of Data and Information Science

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Purpose Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles. Design/methodology/approach Several tools are used for sentiment analysis, so we applied five sentiment analysis tools to check which are suitable for capturing a tweet's sentiment value and decided to use NLTK VADER and TextBlob. We segregated the sentiment value into negative, positive, and neutral. We measure the mean and median of tweets’ sentiment value for research articles with more than one tweet. We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models. Findings We found that the most important feature in all the models was the sentiment of the research article title followed by the author count. We observed that the tree-based models performed better than other classification models, with Random Forest achieving 89% accuracy for binary classification and 73% accuracy for three-label classification. Research limitations In this research, we used state-of-the-art sentiment analysis libraries. However, these libraries might vary at times in their sentiment prediction behavior. Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper's details. In the future, we intend to broaden the scope of our research by employing word2vec models. Practical implications Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes. Research in this area has relied on fewer and more limited measures, such as citations and user studies with small datasets. There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research. This study will help scientists better comprehend the emotional impact of their work. Additionally, the value of understanding the public's interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public. Originality/value This study will extend work on public engagement with science, sociology of science, and computational social science. It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.

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Early indicators of scientific impact: Predicting citations with altmetrics

Cited by 49 publications

References 93 publications

An analysis of journalism articles achieving high Altmetric attention scores

An analysis of journalism articles achieving high Altmetric attention scores

Quantifying the online long-term interest in research

Public Reaction to Scientific Research via Twitter Sentiment Prediction

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