Web services research paper

If a correlation is found, this might suggest that altmetrics and citations measure, at least to a certain extent, the same phenomenon and that altmetrics are merely early indicators of this underlying quality. The absence of such a relationship, however, would demonstrate that altmetrics probably measure something different. Given this scenario, the quality that is measured by altmetrics should be examined in order to understand the validity of using such metrics in an evaluative manner or for information retrieval.

This paper contributes to this discussion by comparing eleven different altmetric sources with citation data for to , depending on the metric PubMed documents published between and Specifically, this study seeks to answer the following research question: To what extent do the altmetric indicators associate with citation counts? Employing non-citation-based metrics in the evaluation of research is not novel. Previous research has looked for correlations between traditional citations and their younger counterparts: online presentations [10] , online syllabi [11] , Google Scholar citations [12] — [14] , Google Book citations [15] , and article downloads [16] — [18].

Although webometric and electronic readership studies have tried to reflect scholarly impact in a broader sense, they have often been restricted by scalability of and access to data. As altmetrics focus on social media platforms that often provide free access to usage data through Web APIs, data collection is less problematic [19].

Several sources have been proposed as alternatives for measuring the impact of scholarly publications, such as mentions and citations in blogs, Wikipedia, Twitter or Facebook or reader counts on social reference managers and bookmarking platforms [1] , [19] — [22]. Evaluations of these sources have focused on single genres or sources, such as Twitter [23] — [25] , blogs [26] — 27 , bookmarks [28] , and Wikipedia [29].

Web Services

Some research has focused on a variety of indicators for a single source, such as analyses of PLoS article-level metrics ALM , which include counts of comments, ratings, social bookmarks and blog citations to articles published in the PLoS journals [4] , [30]. Reader counts from social bookmarking services and social reference managers such as Mendeley, CiteULike, BibSonomy and Connotea have also been analyzed [28] , [31] — [35].

A few studies have investigated altmetrics and their relationship with traditional citation indicators. Tweets of arXiv articles i. Although these results suggest that there is a positive relationship between tweets and citations, these correlation studies have mainly covered individual elite journals and those that favour internet research. The exception, for arXiv preprints also covers a somewhat special area of scholarship: articles from quantitative research areas promoted by their authors through self-archiving.

Arguments against the value of altmetrics include the ease with which they can be manipulated and their susceptibility to skew in favour of comical or sexual titles e. In order to obtain more robust evidence, larger scale studies are needed.

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Moreover, the various altmetrics have different characteristics when examined diachronically. Priem, Piwowar, and Hemminger [36] examined the distribution of social media events over time for PLoS articles, noting differences in behaviour.

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For example, citations, page views and Wikipedia citations tended to increase over time while CiteULike, Mendeley, Delicious bookmarks, and F ratings were relatively unaffected by article ages. Other metrics contained serious flaws as changes in service and limitations of data hindered analysis—this highlights concerns over the stability of some of these indicators and the use of these indicators in longitudinal studies.

It seems that altmetrics probably capture a broad, or at least a different, aspect of research visibility and impact in comparison to citation counts. Publications also influence the development of new technologies, the daily work of professionals, teaching, and also have other societal effects [37] , [38] , which may also be tweeted about or discussed in the social web. Kurtz and Bollen [39] classified readers of scholarly publications into four groups: researchers, practitioners, undergraduates and the interested public.

Whilst all of these might use the social web, the first group is the most likely to publish scholarly papers. It is owned by the U. National Library of Medicine. National Institutes of Health [40]. The goal of the research design was to devise a fair test of whether higher altmetrics values associate with higher citation counts for articles. Previous altmetric and webometric studies have tended to correlate citations with the web metric on the assumption that since citation counts are a recognised indicator of academic impact, any other measure that correlates positively with them is also likely to associate with academic impact.

Correlation tests are not ideal for altmetrics, however, because many are based upon services with a rapidly increasing uptake. In consequence, newer articles can expect, on average, to receive higher altmetric scores than older articles. Since citations also take time to accrue the opposite is true for citation counts and so without adjusting for these differences a correlation test is always biased towards negative correlations.

Journal of Web Semantics

Adjusting citation and usage windows to eliminate these biases, as done with download statistics e. To avoid these issues a simple sign test was devised. For this test, each article is compared only against the two articles published immediately before and after it within the data set used and for the same journal. Thus only articles of approximately the same age, which are similarly exposed to the same citation delay and usage uptake biases, are compared to each other.

Moreover any slight advantage or disadvantage of the article published after the one tested should be cancelled out by its averaging with the equivalent advantage or disadvantage of the article published before. The test gives three possible outcomes:. Success : the altmetric score is higher than the average altmetric score of the two adjacent articles and its citation score is higher than the average of the two adjacent articles OR the altmetric score is lower than the average altmetric score of the two adjacent articles and its citation score is lower than the average of the two adjacent articles.

Failure : the altmetric score is higher than the average altmetric score of the two adjacent articles and its citation score is lower than the average of the two adjacent articles OR the altmetric score is lower than the average altmetric score of the two adjacent articles and its citation score is higher than the average of the two adjacent articles. Null : All other cases.


Note that this includes cases where all three articles are uncited, which is likely to occur when the articles are relatively new. To illustrate the above, suppose that articles A, B, and C are ranked in publication order and attracted 2, 3, and 6 tweets respectively. Using the above scores, the more strongly an altmetric associates with citations, the higher the ratio of success to failure should be.

Conversely, if an altmetric has no association with citations then the number of successes should not be statistically significantly different from the number of failures.

The altmetric data used originates from altmetric. This data was delivered on January 1, and includes altmetric scores gathered since July Although the system was undergoing development at the time and there may be periods of lost data, this should not cause false positive results due to the testing method used, as described above.

The 11 metrics are the following. FbWalls : A licensed Facebook firehose is used for Wall posts to check for citations. Blogs : The blog feed citations are from a manually-curated list of about 2, science blogs, derived from the indexes at Nature. MSM : The mainstream media citation count is based on a manually curated list of about 60 newspapers and magazines using links in their science coverage.

LinkedIn : LinkedIn. The altmetric data is not a complete list of all articles with PubMed IDs. Instead it is a list of all articles with a PubMed ID and a non-zero altmetric. Citations for these articles, if any, were obtained from WoS by matching the bibliographic characteristics authors, titles, journals, and pages of PubMed records with WoS records. First author self-citations were excluded from the results on the basis that authors would rarely hear about their work from social media.

Citations and self-citations had a Spearman correlation of 0. Mentions of articles by their authors in the altmetric data were not removed because this is impractical e. There were 3,, citations altogether to the articles in the data set, excluding self-citations. Although the citation scores for the articles are not reliable due to the short citation windows, this should not cause systematic biases in the results because publication time is taken into account in the method used to compare citations with altmetric scores.

For each journal and each altmetric, a list was created of all articles with a score of at least 1 on the altmetric, discarding articles with a zero score. The reason for the discarding policy was that the data set did not include a complete list of articles in each journal and it was impractical to obtain such a list. Moreover, since the authors did not have control over the data collection process, altmetric data for articles may be missing due to problems in the data collection process e. As a consequence of this, it is not possible to be sure that articles with zero values for an altmetric should not have positive scores unlike [42] for example.

It is more certain that articles with a positive score on an altmetric had their data effectively collected with that altmetric and so data for articles with non-zero altmetric scores is the most reliable and is the only data used in this article. Since the data collection process varies between altmetrics, it is not possible to assume that a positive score for an article on one altmetric implies that it will also have been effectively monitored for all the other altmetrics. Preliminary testing showed that this was not the case resulting in a preliminary analysis of the data with additional implied zeros for articles with a non-zero score on one altmetric but a positive score at least one other altmetric being rejected as unreliable and not reported here.

The results, therefore, only relate to articles attracting a positive altmetric score. To obtain the chronological order needed for the sign test, for each journal and altmetric, the document lists were ordered by PubMed ID. Although imperfect, this was the most reliable general source of chronological information available. DOIs sometimes contain chronological information, such as a year, but even when a year is present it can refer to the submission year, acceptance year or publication year.

Although the publication year and issue number are included in the bibliographic metadata, they are not detailed enough and in many cases do not reflect the actual date of online availability. In contrast, the PubMed ID is more fine-grained and universal. It seems likely to be reasonably chronologically consistent for each individual journal, if not between journals. Conducting the main analysis for journals separately ensures that predominantly articles from the same subject area are compared, except in the case of multidisciplinary journals.

Marcelo Eler. Testing Service Oriented Architecture applications is a challenging task due to the high dynamism, the low coupling and the low testability of services. Web services, a popular implementation of services, are usually provided as black box Web services, a popular implementation of services, are usually provided as black box and using testing techniques based on implementation is limited.


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This paper presents an approach to support the use of the structural testing technique on. The growth of the World-Wide Web today is simply phenomenal. It continues to grow rapidly and new technologies, applications are being developed to support end users modern life.