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      If You Are Not on Social Media, Here's What You're Missing! #DoTheThing

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      Archives of Pathology & Laboratory Medicine
      Archives of Pathology and Laboratory Medicine

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          Is Open Access

          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.
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            Prognostic effect of different PD-L1 expression patterns in squamous cell carcinoma and adenocarcinoma of the cervix

            Programmed death-ligand 1 (PD-L1) is expressed in various immune cells and tumor cells, and is able to bind to PD-1 on T lymphocytes, thereby inhibiting their function. At present, the PD-1/PD-L1 axis is a major immunotherapeutic target for checkpoint inhibition in various cancer types, but information on the clinical significance of PD-L1 expression in cervical cancer is largely lacking. Here, we studied PD-L1 expression in paraffin-embedded samples from two cohorts of patients with cervical cancer: primary tumor samples from cohort I (squamous cell carcinoma, n=156 and adenocarcinoma, n=49) and primary and paired metastatic tumor samples from cohort II (squamous cell carcinoma, n=96 and adenocarcinoma, n=31). Squamous cell carcinomas were more frequently positive for PD-L1 and also contained more PD-L1-positive tumor-associated macrophages as compared with adenocarcinomas (both P<0.001). PD-L1-positive tumor-associated macrophages were found to express CD163 and/or CD14 by triple fluorescent immunohistochemistry, demonstrating an M2-like phenotype. Interestingly, disease-free survival (P=0.022) and disease-specific survival (P=0.046) were significantly poorer in squamous cell carcinoma patients with diffuse PD-L1 expression as compared with patients with marginal PD-L1 expression (i.e., on the interface between tumor and stroma) in primary tumors. Disease-specific survival was significantly worse in adenocarcinoma patients with PD-L1-positive tumor-associated macrophages compared with adenocarcinoma patients without PD-L1-positive tumor-associated macrophages (P=0.014). No differences in PD-L1 expression between primary tumors and paired metastatic lymph nodes were detected. However, PD-L1-positive immune cells were found in greater abundance around the metastatic tumors as compared with the paired primary tumors (P=0.001 for squamous cell carcinoma and P=0.041 for adenocarcinoma). These findings point to a key role of PD-L1 in immune escape of cervical cancer, and provide a rationale for therapeutic targeting of the PD-1/PD-L1 pathway.
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              How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations

              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.
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                Author and article information

                Journal
                Archives of Pathology & Laboratory Medicine
                Archives of Pathology & Laboratory Medicine
                Archives of Pathology and Laboratory Medicine
                0003-9985
                1543-2165
                November 2017
                November 2017
                : 141
                : 11
                : 1567-1576
                Article
                10.5858/arpa.2016-0612-SA
                be81d008-f537-4894-8bb3-b8f3de14ca06
                © 2017
                History

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