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

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 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.

Use of reference management software among postgraduate students in Greece

The use of reference management software in the context of academic work and research is the main subject of this study. The study focuses on the extent to which postgraduate students at the Aristotle University of Thessaloniki, one of the largest Greek universities, make use of – or avoid using – reference management software tools to organize their bibliographic databases and to automate the process of creating references and citations. The study also tries to find out which are the key factors for their choices and whether certain background characteristics affect their behavior. It should be mentioned that no previous studies have been conducted in Greece regarding the use of reference management software in the academic environment. An online questionnaire was sent to the postgraduate students at the University and a result set of 545 responses was collected and analyzed. The majority (almost two-thirds) of the respondents identified themselves as non-users and one-third identified themselves as reference management software users. Among the latter, Mendeley was found to be the software used by more than two-thirds of the users and was followed by EndNote and Zotero. It is worth mentioning that Mendeley is the software officially recommended by the University’s central library to its users but most of the students (more than 60%) were not aware of this fact. In terms of background characteristics, the analysis revealed, among other things, statistically significant relationships between degree level, student discipline and preferences, reference management software features, and potential future use of reference management software.

Measuring the Diversity of Facebook Reactions to Research

Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared---a conclusion that contradicts previous studies of other social media platforms.