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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.
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Exploring social contextual influences on healthy eating using big data analytics
An alarming proportion of the US population is overweight: 2/3 of US adults are overweight, and 1/3 of those overweight are obese. Obesity increases the risk of illnesses such as diabetes and cardiovascular diseases. This epidemic can be attributed to the combination of cheap, high-calorie food and lack of physical activity. In this paper, we propose a Big Data Analytics framework, called BiDAF, that aims to explore social contextual influences on healthy eating. For this purpose, we classified food tweets and social media images into as either healthy or unhealthy as well as food sentiments into either positive or negative, and further mapped them to an obesity prevalence map. The classification outcomes would be useful to reveal the social food trends and sentiments of the Centers for Disease and Control Prevention (CDC) USA obesity regions. The BiDAF framework has been implemented on Apache Spark and TensorFlow platforms. We have evaluated the BiDAF framework in terms of the accuracy on the food tweet classification and sentiment analysis. The experimental results indicated that the BiDAF framework is effective in classification and sentiment analysis of food tweet messages and also showed its potential in exploring social contextual influences that may contribute to healthy eating.
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- Award ID(s):
- 1650549
- PAR ID:
- 10051099
- Date Published:
- Journal Name:
- 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Page Range / eLocation ID:
- 1507 to 1514
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BIBM.2017.8217885
