Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, the correlation between the bibliometric and altmetric indices is examined using Spearman correlation. Third, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying Area Under the Curve (AUC) - an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A zero-truncated negative binomial model is implemented to measure the extent to which different bibliometric and altmetric factors contribute to citation counts. Blog count appears to be the most important factor increasing the number of citations by 38.6% in the field of Health Professions and Nursing, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. Interestingly, both Blog count and Twitter count always show positive increase in the number of citations across all fields. While there was a positive weak correlation between bibliometric and altmetric indices, the results show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC= 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics could better distinguish highly cited publications.Comment: 34 Pages, 3 Figures, 15 Table

Analysis of Tweets Mentioning Scholarly Works from an Institutional Repository

Altmetrics derived from Twitter have potential benefits for institutional repository (IR) stakeholders (faculty, students, administrators, and academic libraries) when metrics aggregators (Altmetric, Plum Analytics) are integrated with IRs. There is limited research on tweets mentioning works in IRs and how the results impact IR stakeholders, specifically libraries. In order to address this gap in the literature, the author conducted a content analysis of tweets tracked by a metrics aggregator (Plum X Metrics) in a Digital Commons IR. The study found that the majority of tweets were neutral in attitude, intended for a general audience, included no hashtags, and were written by users unaffiliated with the works. The results are similar to findings from other studies, including low numbers of tweeted works, high numbers of tweets neutral in attitude, and evidence of self-tweets. The discussion addresses these results in relation to the value of tweets and suggested improvements to Twitter metrics based on IR stakeholders’ needs

Predicting literature’s early impact with sentiment analysis in Twitter

Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature’s early impact

Tweet coupling: a social media methodology for clustering scientific publications

This is an accepted manuscript of an article published by Springer in Scientometrics on 18/05/2020, available online: https://doi.org/10.1007/s11192-020-03499-1 The accepted version of the publication may differ from the final published version.© 2020, Akadémiai Kiadó, Budapest, Hungary. We argue that classic citation-based scientific document clustering approaches, like co-citation or Bibliographic Coupling, lack to leverage the social-usage of the scientific literature originate through online information dissemination platforms, such as Twitter. In this paper, we present the methodology Tweet Coupling, which measures the similarity between two or more scientific documents if one or more Twitter users mention them in the tweet(s). We evaluate our proposal on an altmetric dataset, which consists of 3081 scientific documents and 8299 unique Twitter users. By employing the clustering approaches of Bibliographic Coupling and Tweet Coupling, we find the relationship between the bibliographic and tweet coupled scientific documents. Further, using VOSviewer, we empirically show that Tweet Coupling appears to be a better clustering methodology to generate cohesive clusters since it groups similar documents from the subfields of the selected field, in contrast to the Bibliographic Coupling approach that groups cross-disciplinary documents in the same cluster.The authors (Saeed-Ul Hassan & Mudassir Shabbir) were funded by the CIPL (National Center in Big Data and Cloud Computing (NCBC) grant, received from the Planning Commission of Pakistan, through Higher Education Commission (HEC) of Pakistan. This work was partially supported by the Spanish Ministry of Science and Technology under the projects TIN2017-89517-P and TIN2017-83445-P. Eugenio Martínez Cámara was supported by the Spanish Government Programme Juan de la Cierva Incorporación (IJC2018-036092-I).Published versio

Exploiting tweet sentiments in altmetrics large-scale data

This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users’ sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialised lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarisation approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users’ expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business and Decision Sciences, tweet aspects are focused on the results section. In contrast, in Physics and Astronomy, Materials Sciences and Computer Science, these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact

UNDERSTANDING THE SCHOLARLY COMMUNICATION PROCESS THROUGH DIGITAL TRACES: A STUDY OF TWITTER

Through the lens of the exploratory framework of Digital Trace of Scholarly Acts (DTSA), this dissertation study explored researchers’ activities around scholarly articles on Twitter. Using a mixed-methods design, this study analyzed data collected from a large-scale survey and twenty interviews with researchers on Twitter. The Critical Incident Technique was used as part of the interview study to learn about the full stories behind researchers’ sharing of scholarly articles on Twitter. There were variations in the researcher’s sentiment of opinions on articles they tweeted, retweeted, replied, and liked, based on their demographics. Despite a general positive tendency, researchers’ Twitter activities were associated with different sentiment due to their different perceptions of these activities. Variations were also found in how sharing scholarly articles on Twitter fit into researchers’ scholarly acts workflow with no monolithic pattern. This study contributed to a better understanding of the digital traces left by researchers on Twitter by providing richer descriptions and narratives of their activities. Researchers shared scholarly articles on Twitter for a variety of motivations: networking, promoting, disseminating, commenting, communicating with intended users, acknowledgment, and saving for later reference. The findings particularly shed light on the role of Twitter in communicating research and network building. Investigating the impact of the articles on the researchers led to a better understanding of what types of articles had a higher premium of sharing by researchers on Twitter. Evidence was found to support both the normative theory and the constructivist theory – the categories of impact included connecting, informing, practice-changing, beyond research, and potential impact. However, more than half of the shared articles examined had no impact on the researchers’ own work, indicating that Twitter metrics, even solely based on researchers’ Twitter activities, should not be used as an evaluative metric of the articles shared.Doctor of Philosoph