Using Social Media and Scholarly Text to Predict Public Understanding of Science

Siravuri, Harish Varma; Akella, Akhil Pandey; Bailey, Christian; Alhoori, Hamed

doi:10.1145/3197026.3203890

Cited by 5 publications

(14 citation statements)

References 8 publications

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“…We will investigate scholarly information behavior among researchers producing or dealing with non-English content. Additionally, we plan to investigate how social media can build and affect a research culture using various recommendations and text analytics approaches [138] [139].…”

Section: Discussionmentioning

confidence: 99%

Anatomy of scholarly information behavior patterns in the wake of academic social media platforms

et al. 2018

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As more scholarly content is born digital or converted to a digital format, digital libraries are becoming increasingly vital to researchers seeking to leverage scholarly big data for scientific discovery. Although scholarly products are available in abundance-especially in environments created by the advent of social networking services-little is known about international scholarly information needs, information-seeking behavior, or information use. The purpose of this paper is to address these gaps via an in-depth analysis of the information needs and information-seeking behavior of researchers, both students and faculty, at two universities, one in the U.S. and the other in Qatar. Based on this analysis, the study identifies and describes new behavior patterns on the part of researchers as they engage in the information-seeking process. The analysis reveals that the use of academic social networks has notable effects on various scholarly activities. Further, this study identifies differences between students and faculty members in regard to their use of academic social networks, and it identifies differences between researchers according to discipline.Although the researchers who participated in the present study represent a range of disciplinary and cultural backgrounds, the study reports a number of similarities in terms of the researchers' scholarly activities.

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Section: Discussionmentioning

confidence: 99%

Anatomy of scholarly information behavior patterns in the wake of academic social media platforms

et al. 2018

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“…The literature includes multiple studies going back decades in which researchers have considered the life and obsolescence of scholarly articles by analyzing factors relevant to measuring the impact of scholarly research that is of interest to the public (Siravuri et al, 2018). Larivière et al (2008) observed that the age of cited material has risen continuously since the mid-1960s.…”

Section: Obsolescence Of Research Articlesmentioning

confidence: 99%

Quantifying the online long-term interest in research

Shahzad

Alhoori

Freedman

et al. 2022

Journal of Informetrics

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“…There is a need to understand the impact of research beyond the traditional scholarly impact (i.e., citations) (Le et al, 2019;Kousha and Thelwall, 2019;Noyons, 2019) such as the impact on economics (Shaikh and Alhoori, 2019), public policy (Kale et al, 2017), social media (Alhoori et al, 2019), news outlets (Siravuri and Alhoori, 2017), and public understanding of science (Siravuri et al, 2018). In the present study, we subjected a collection of tweets to the process of sentiment analysis, which refers to the contextual mining of texts through which subjective information is identified and extracted (Liu, 2012).…”

Section: Introductionmentioning

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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Using Social Media and Scholarly Text to Predict Public Understanding of Science

Cited by 5 publications

References 8 publications

Anatomy of scholarly information behavior patterns in the wake of academic social media platforms

Anatomy of scholarly information behavior patterns in the wake of academic social media platforms

Quantifying the online long-term interest in research

Public Reaction to Scientific Research via Twitter Sentiment Prediction

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