<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>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.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>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.</p>
</sec>
<sec><h3>Findings</h3>
<p>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.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>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.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>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.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>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.</p>
</sec>
</abstract>

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.

Public Reaction to Scientific Research via Twitter Sentiment Prediction

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Public Reaction to Scientific Research via Twitter Sentiment Prediction"}

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...

Public Reaction to Scientific Research via Twitter Sentiment Prediction

Article Category: Research Paper

Online veröffentlicht: 11. Dez. 2021

Seitenbereich: 97 - 124

Eingereicht: 05. Aug. 2021

Akzeptiert: 31. Okt. 2021

DOI: https://doi.org/10.2478/jdis-2022-0003

Schlüsselwörter
Sentiment analysis, Social media, Twitter, Emotional impact, Public understanding of science, Science and technology studies

© 2022 Murtuza Shahzad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Public Reaction to Scientific Research via Twitter Sentiment Prediction

Article Category: Research Paper

Online veröffentlicht: 11. Dez. 2021

Seitenbereich: 97 - 124

Eingereicht: 05. Aug. 2021

Akzeptiert: 31. Okt. 2021

DOI: https://doi.org/10.2478/jdis-2022-0003

SchlüsselwörterSentiment analysis, Social media, Twitter, Emotional impact, Public understanding of science, Science and technology studies

© 2022 Murtuza Shahzad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Sentiment analysis, Social media, Twitter, Emotional impact, Public understanding of science, Science and technology studies