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19 janvier 2016

Tweets Do Measure Non – Citational Intellectual Impact

Purpose

The aim of the paper is to identify the motive behind the social media indicators in focus to tweets and attempts to identify what is measured or indicated by tweets, based on these motives.

Design/methodology/approach

Documents with non zero tweets were manually collected from a source of 5 journals – Nature Biotechnology, Nature Nanotechnology, Nature Physics, Nature Chemistry and Nature Communications for the period January 2014 – October 2014 so as to depict the contemporary trend, as tweets tends to have L shaped curve in time-wise distribution.

Findings

Investigations suggest that the motives behind the tweets are research reach, research acceptance and research usage. Further analysis revealed that the motive behind self – tweets are research visibility, which is one of the attributes of social media and therefore self tweets may not be a complex problem as expected seeing that documents are self tweeted not more than once in most cases.

Furthermore, identifying and classifying tweets based on users – Publishers, Frequent tweeters who apparently tweet all documents of an issue and Authors will increase the effectiveness of altmetrics in research evaluation. It was also found that association between subjects can be identified by the analysis of tweets pattern among subjects.

Originality/value

Study proposes an overall hierarchical structure of impact based on the change/advancement instigated. The study confirms that tweets do measure non – academic intellectual impact that is not captured by traditional metrics.

URL : http://www.itlit.net/v2n2art2.pdf

19 décembre 201323 janvier 2016

Transformation of Science Communication in the Age of Social Media

The aim of the present article is to discuss several consequences of the Open Science from a perspective of science communication and philosophy of communication. Apart from the purely communicative and philosophical issues, the paper deals with the questions that concern the science populariza- tion process through social media (especially Twitter and blogs).

The article consists of three sections: the first one suggests a definition of science communication and social media, the second examines the transformation of science in the Age of the Internet and considers the influence of social media on science communication, the third and final one presents some case studies and philosophical observations.

The most important conclusion to be reached here is that the social media have changed science and science communication. Twitter and blogs as novelty tools of science communication can be useful and meaningful for both science and society. Furthermore, social media can be used to facilitate broader involvement of citizens in the discussion about science.

URL : http://teorievedy.flu.cas.cz/index.php/tv/article/view/172

3 mai 2013

The role of twitter in the life cycle…

The role of twitter in the life cycle of a scientific publication :

“Twitter is a micro-blogging social media platform for short messages that can have a long-term impact on how scientists create and publish ideas. We investigate the usefulness of twitter in the development and distribution of scientific knowledge. At the start of the life cycle of a scientific publication, twitter provides a large virtual department of colleagues that can help to rapidly generate, share and refine new ideas. As ideas become manuscripts, twitter can be used as an informal arena for the pre-review of works in progress. Finally, tweeting published findings can communicate research to a broad audience of other researchers, decision makers, journalists and the general public that can amplify the scientific and social impact of publications. However, there are limitations, largely surrounding issues of intellectual property and ownership, inclusiveness and misrepresentations of science sound bites. Nevertheless, we believe twitter is a useful social media tool that can provide a valuable contribution to scientific publishing in the 21st century.”

URL : http://arxiv.org/abs/1305.0435

19 décembre 201117 août 2015

Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

Background

Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known.

Objective:

(1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles.

Methods

Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated.

Results

A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity.

Conclusions

Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.

URL : http://www.jmir.org/2011/4/e123/