Scholarly Twitter Metrics

The Marketing and Perceptions of Non-Tobacco Blunt Wraps on Twitter

OBJECTIVE

Non-tobacco blunt wraps (N-TBWs), which entered the marketplace in 2017, are being promoted as an alternative to traditional TBWs (e.g., cigarillos) for blunt smoking. The lack of studies on these novel products warrants an investigation. This study was the first to explore blunt smokers' perceptions about N-TBWs and the extent of product marketing on Twitter.

METHODS

A corpus of tweets from Twitter, posted between January 2017 and November 2021, were identified by a Boolean search string (N = 149,343), where 48,695 tweets were classified as relevant by a machine learning algorithm. These relevant tweets were further screened and labeled as promotional or organic based on product URLs, usernames, keywords, or hashtags. Topic modeling using Dirichlet Allocation was then employed for identifying latent patterns of words among relevant tweets. The Social Networking Potential (SNP) score was employed for identifying influential accounts.

RESULTS

Most relevant tweets (89%) were organic, non-promotional expressions about N-TBWs. Account users who only posted non-promotional tweets had a significantly higher SNP than those who only posted promotional tweets. Yet, neither of the two groups of account users consisted of known celebrities. Topic modeling revealed three broad groups of topics (7 in total) denoting the attributes of hemp N-TBWs, interest in non-hemp N-TBWs, and product marketing.

CONCLUSIONS

The large proportion of organic tweets is indicative of the nascency of N-TBWs, which will need to be marketed more extensively if they are to replace cigar products used by blunt smokers.

Measuring altmetric events: the need for longer observation period and article level computations

Purpose

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

Design/methodology/approach

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

Findings

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

Research limitations/implications

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

Practical implications

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

Social implications

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Originality/value

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

Whose research benefits more from Twitter? On Twitter-worthiness of communication research and its role in reinforcing disparities of the field

Twitter has become an important promotional tool for scholarly work, but individual academic publications have varied degrees of visibility on the platform. We explain this variation through the concept of Twitter-worthiness: factors making certain academic publications more likely to be visible on Twitter. Using publications from communication studies as our analytical case, we conduct statistical analyses of 32187 articles spanning 82 journals. Findings show that publications from G12 countries, covering social media topics and published open access tend to be mentioned more on Twitter. Similar to prior studies, this study demonstrates that Twitter mentions are associated with peer citations. Nevertheless, Twitter also has the potential to reinforce pre-existing disparities between communication research communities, especially between researchers from developed and less-developed regions. Open access, however, does not reinforce such disparities.

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.

Are patents linked on Twitter? A case study of Google patents

This study attempts to analyze patents as cited/mentioned documents to better understand the interest, dissemination and engagement of these documents in social environments, laying the foundations for social media studies of patents (social Patentometrics).Particularly, this study aims to determine how patents are disseminated on Twitter by analyzing three elements: tweets linking to patents, users linking to patents, and patents linked from Twitter. To do this, all the tweets containing at least one link to a full-text patent available on Google Patents were collected and analyzed, yielding a total of 126,815 tweets (and 129,001 links) to 86,417 patents. The results evidence an increase of the number of linking tweets over the years, presumably due to the creation of a standardized patent URL ID and the integration of Google Patents and Google Scholar, which took place in 2015. The engagement achieved by these tweets is limited (80.2% of tweets did not attract likes) but increasing notably since 2018. Two super-publisher twitter bot accounts (dailypatent and uspatentbot) are responsible of 53.3% of all the linking tweets, while most accounts are sporadic users linking to patent as part of a conversation. The patents most tweeted are, by far, from United States (87.5% of all links to Google Patents), mainly due to the effect of the two super-publishers. The impact of patents in terms of the number of tweets linking to them is unrelated to their year of publication, status or number of patent citations received, while controversial and media topics might be more determinant factors. However, further research is needed to better understand the topics discussed around patents on Twitter, the users involved, and the metrics attained. Given the increasing number of linking users and linked patents, this study finds Twitter as a relevant source to measure patent-level metrics, shedding light on the impact and interest of patents by the broad public.

User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis

This study investigates the extent to which scholarly tweets of scientific papers are engaged with by Twitter users through four types of user engagement behaviors, i.e., liking, retweeting, quoting, and replying. Based on a sample consisting of 7 million scholarly tweets of Web of Science papers, our results show that likes is the most prevalent engagement metric, covering 44% of scholarly tweets, followed by retweets (36%), whereas quotes and replies are only present for 9% and 7% of all scholarly tweets, respectively. From a disciplinary point of view, scholarly tweets in the field of Social Sciences and Humanities are more likely to trigger user engagement over other subject fields. The presence of user engagement is more associated with other Twitter-based factors (e.g., number of mentioned users in tweets and number of followers of users) than with science-based factors (e.g., citations and Mendeley readers of tweeted papers). Building on these findings, this study sheds light on the possibility to apply user engagement metrics in measuring deeper levels of Twitter reception of scholarly information.

Who tweets climate change papers? investigating publics of research through users’ descriptions

As social issues like climate change become increasingly salient, digital traces left by scholarly documents can be used to assess their reach outside of academia. Our research examine who shared climate change research papers on Twitter by looking at the expressions used in profile descriptions. We categorized users in eight categories (academia, communication, political, professional, personal, organization, bots and publishers) associated to specific expressions. Results indicate how diverse publics may be represented in the communication of scholarly documents on Twitter. Supplementing our word detection analysis with qualitative assessments of the results, we highlight how the presence of unique or multiple categorizations in textual Twitter descriptions provides evidence of the publics of research in specific contexts. Our results show a more substantial communication by academics and organizations for papers published in 2016, whereas the general public comparatively participated more in 2015. Overall, there is significant participation of publics outside of academia in the communication of climate change research articles on Twitter, although the extent to which these publics participate varies between individual papers. This means that papers circulate in specific communities which need to be assessed to understand the reach of research on social media. Furthermore, the flexibility of our method provide means for research assessment that consider the contextuality and plurality of publics involved on Twitter.

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.

Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics

Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize communities based on the research topics in which they are interested as social and geographic proximity also influence interactions. This paper proposes a new method which profiles social media users based on their interest on research topics using altmetric data. Social media users are clustered based on the topics related to the research publications they share in social media. This allows removing linkages which respond to social or personal proximity and identifying disconnected users who may have similar research interests. We test this method for users tweeting publications from the fields of Information Science & Library Science, and Microbiology. We conclude by discussing the potential application of this method and how it can assist information professionals, policy managers and academics to understand and identify the main actors discussing research literature in social media.

Can tweets be used to detect problems early with scientific papers? A case study of three retracted COVID-19/SARS-CoV-2 papers

Methodological mistakes, data errors, and scientific misconduct are considered prevalent problems in science that are often difficult to detect. In this study, we explore the potential of using data from Twitter for discovering problems with publications. In this case study, we analyzed tweet texts of three retracted publications about COVID-19 (Coronavirus disease 2019)/SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and their retraction notices. We did not find early warning signs in tweet texts regarding one publication, but we did find tweets that casted doubt on the validity of the two other publications shortly after their publication date. An extension of our current work might lead to an early warning system that makes the scientific community aware of problems with certain publications. Other sources, such as blogs or post-publication peer-review sites, could be included in such an early warning system. The methodology proposed in this case study should be validated using larger publication sets that also include a control group, i.e., publications that were not retracted.

Tweeting Bias in Diagnostic Test Accuracy Research: Does Title or Conclusion Positivity Influence Dissemination?

PURPOSE

To examine if tweeting bias exists within imaging literature by determining if diagnostic test accuracy (DTA) studies with positive titles or conclusions are tweeted more than non-positive studies.

METHODS

DTA studies published between October 2011 to April 2016 were included. Positivity of titles and conclusions were assessed independently and in duplicate, with disagreements resolved by consensus. A negative binomial regression analysis controlling for confounding variables was performed to assess the relationship between title or conclusion positivity and tweets an article received in the 100 days post-publication.

RESULTS

354 DTA studies were included. Twenty-four (7%) titles and 300 (85%) conclusions were positive (or positive with qualifier); 1 (0.3%) title and 23 (7%) conclusions were negative; and 329 (93%) titles and 26 (7%) conclusions were neutral. Studies with positive, negative, and neutral titles received a mean of 0.38, 0.00, and 0.45 tweets per study; while those with positive, negative, and neutral conclusions received a mean of 0.44, 0.61, and 0.38 tweets per study. Regression coefficients were -0.05 (SE 0.46) for positive relative to non-positive titles, and -0.09 (SE 0.31) for positive relative to non-positive conclusions. The positivity of the title (P = 0.91) or conclusion (P = 0.76) was not significantly associated with the number of tweets an article received.

CONCLUSIONS

The positivity of the title or conclusion for DTA studies does not influence the amount of tweets it receives suggesting that tweet bias is not present among imaging diagnostic accuracy studies. Study protocol available at https://osf.io/hdk2m/.

Leveraging Implementation Science to Understand Factors Influencing Sustained Use of Mental Health Apps: a Narrative Review

Mental health (MH) smartphone applications (apps), which can aid in self-management of conditions such as depression and anxiety, have demonstrated dramatic growth over the past decade. However, their effectiveness and potential for sustained use remain uncertain. This narrative review leverages implementation science theory to explore factors influencing MH app uptake. The review is guided by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework and discusses the role of the innovation, its recipients, context, and facilitation in influencing successful implementation of MH apps. The review highlights critical literature published between 2015 and 2020 with a focus on depression and anxiety apps. Sources were identified via PubMed, Google Scholar, and Twitter using a range of keywords pertaining to MH apps. Findings suggest that for apps to be successful, they must be advantageous over alternative tools, relatively easy to navigate, and aligned with users’ needs, skills, and resources. Significantly more attention must be paid to the complex contexts in which MH app implementation is occurring in order to refine facilitation strategies. The evidence base is still uncertain regarding the effectiveness and usability of MH apps, and much can be learned from the apps we use daily; namely, simpler is better and plans to integrate full behavioral treatments into smartphone form may be misguided. Non-traditional funding mechanisms that are nimble, responsive, and encouraging of industry partnerships will be necessary to move the course of MH app development in the right direction.