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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. (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. 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. 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. 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.

IntroductionThe Cochrane Schizophrenia Group (CSzG) has produced and maintained systematic reviews of effects of interventions for schizophrenia and related illness. Each review has a Plain Language Summary (PLS), for those without specialised knowledge, and an abstract, which are freely available from The Cochrane Library (https://summaries.cochrane.org). Increasingly, evidence is being distributed using social media such as Twitter and Weibo (in China) alongside traditional publications.Methods and analysisIn a prospective two-arm, parallel, open randomised controlled trial with a 1:1 allocation ratio, we will allocate 170 published systematic reviews into the intervention group (tweeting arm/Weibo arm) versus the control group (non-tweeting arm). Reviews will be stratified by baseline access activity, defined as high (≥19 views per week, n=14), medium (4.3 to 18.99 views per week, n=72) or low (<4.3 views per week, n=84), based on Google Analytics, which will also be used for evaluating outcomes. The intervention group will have three tweets daily using Hootsuite with a slightly different accompanying text (written by CEA and AB) and a shortened Uniform Resource Locator (URL) to the PLS: a) The review title as it appears in summaries.cochrane.org, b) A pertinent extract from results or discussion sections of the abstract and c) An intriguing question or pithy statement related to the evidence in the abstract. The primary outcome will be: total number of visits to a PLS in 7 days following the tweet. Secondary outcomes will include % new visits, bounce rate, pages per visit, visit duration, page views, unique page views, time on page, entrances, exiting behaviour and country distribution.Ethics and disseminationThis study does not involve living participants, and uses information available in the public domain. Participants are published systematic reviews, hence, no ethical approval is required. Dissemination will be via Twitter, Weibo and traditional academic means.Trial registration numberISRCTN84658943.

In this paper we take advantage of recent developments in identifying the demographic characteristics of Twitter users to explore the demographic differences between those who do and do not enable location services and those who do and do not geotag their tweets. We discuss the collation and processing of two datasets-one focusing on enabling geoservices and the other on tweet geotagging. We then investigate how opting in to either of these behaviours is associated with gender, age, class, the language in which tweets are written and the language in which users interact with the Twitter user interface. We find statistically significant differences for both behaviours for all demographic characteristics, although the magnitude of association differs substantially by factor. We conclude that there are significant demographic variations between those who opt in to geoservices and those who geotag their tweets. Not withstanding the limitations of the data, we suggest that Twitter users who publish geographical information are not representative of the wider Twitter population.

How to manage the research-paper deluge? Blogs, colleagues and social media can all help.

Disruptive technologies are revolutionising continuing professional development in emergency medicine and critical care (EMCC). Data on EMCC blogs and podcasts were gathered prospectively from 2002 through November 2013. During this time there was a rapid expansion of EMCC websites, from two blogs and one podcast in 2002 to 141 blogs and 42 podcasts in 2013. This paper illustrates the explosive growth of EMCC websites and provides a foundation that will anchor future research in this burgeoning field.

We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads, and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.

With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 839 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory [corrected]. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.

Drawing on Bronfenbrenner’s ecological theory and prior empirical research, the current study examines the way that blogging and social networking may impact feelings of connection and social support, which in turn could impact maternal well-being (e.g., marital functioning, parenting stress, and depression). One hundred and fifty-seven new mothers reported on their media use and various well-being variables. On average, mothers were 27 years old (SD = 5.15) and infants were 7.90 months old (SD = 5.21). All mothers had access to the Internet in their home. New mothers spent approximately 3 hours on the computer each day, with most of this time spent on the Internet. Findings suggested that frequency of blogging predicted feelings of connection to extended family and friends which then predicted perceptions of social support. This in turn predicted maternal well-being, as measured by marital satisfaction, couple conflict, parenting stress, and depression. In sum, blogging may improve new mothers’ well-being, as they feel more connected to the world outside their home through the Internet.