Studying Scientific Discourse on the Web Using Bibliometrics: A Chemistry Blogging Case Study Paul Groth
Thomas Gurney
VU University Amsterdam De Boelelaan 1081a 1081 HV Amsterdam
Rathenau Institute Anna van Saksenlaan 51 2593 HW The Hague
The Netherlands
The Netherlands
[email protected]
[email protected]
ABSTRACT
implications of science.
Scientific discourse occurs both in the academic literature and, increasingly, on the Web. What is discussed in the literature influences what is discussed on the web, and the reverse. However, the study of this discourse has largely been isolated based on medium either using bibliometrics for academic literature or webometrics for Web-based communication. In this work, the science blog aggregator Researchblogging.org is used to enable the study of scientific discourse on the Web using bibliometric techniques, in particular, keyword and citation similarity maps. The study focuses on a set of 295 chemistry blog posts about peer-reviewed research. Based on bibliometric maps, we provide evidence that scientific discourse on the Web is more immediate, contextually relevant and has a larger non-technical focus than the academic literature.
The remainder of this work is organized as follows. First, in Section 2, a background to bibliometrics and scientific blog analysis is given. This is followed by a description of the method employed for this study, including, the maps of science produced in Section 3. Section 4 provides a four-part analysis of the results of the study followed by a brief conclusion.
Keywords bibliometrics, science blogs, researchblogging.org, scientific discourse
1. INTRODUCTION Scientific discourse is increasingly being conducted on the Web using a variety of technology platforms; including blogs, mailing lists and Twitter [8]. This discourse is not separate from traditional academic discourse in published papers, but is intertwined with it: blogs etc. are increasingly referring to, and commenting on, traditional publications. Indeed, these alternative platforms for scientific discourse are interesting to measure because they provide, practically, instant commentary on publications removing the lag that is associated with the traditional publication cycle. In this work, a first step towards studying scientific discourse on the Web through its connection to the academic literature is taken. This is in contrast to prior approaches that study the web and academic literature using separate techniques under the headings of Webometrics and Bibliometrics [29] To study blogs using bibliometrics, this work relies upon Researchblogging.org, which aggregates scientific blog posts about peer-reviewed research. Importantly, the site has quality criteria for the blogs that it includes as is later discussed. Using 295 blog posts about chemistry and their associated publications, a number of conclusions are drawn about scientific discourse on the web, namely, that it is immediate, contextually relevant, focused on quality, and concerned with the non-technical
Copyright is held by the authors. Web Science Conf. 2010, April 26-27, 2010, Raleigh, NC, USA.
2. BACKGROUND Here, a brief background to bibliometrics and the analysis of blogs, in particular, science blogs is provided.
2.1 Bibliometrics The history and use of bibliometric data for publication analysis is extensive, with roots in the creation of the Science Citation Index (SCI) by the Institute for Scientific information (ISI) [14]. The many uses and virtues of the SCI have been extolled by de Solla Price [13] as a source for development of indicators as guides to understand basic features of science. These indicators could focus on the state of the art, or the practitioners involved or combinations of each and features of these could include, for example, ‘how many researchers?’, ‘how much money is spent on science?’, or ‘how “good” are research groups?’ [9]. Various methods have been used to determine the knowledge landscape associated with the field under study - such as coword mapping, citation analysis and keyword similarity maps. Coword mapping is a commonly used tool that has been applied for some time previously [12], in which the cognitive foreground of the publication can be highlighted using the frequency of cooccurrence of the publication title words. The title words are selected by the authors themselves allowing them to position themselves both topically and temporally [21] and allows inspection of the field by an analyst in those terms. The use of citation analysis as a tool is widespread ([5,7,31,4]), with novel and innovative modifications to methods such as the combination of citation and coword analysis [11, 30]. Of course some problems are encountered using citation analysis methods such as publication lag which affects the citation gathering ability of the publication with citation rate peaking between 4 and 10 years after publication [18]. Keyword mapping is another meta-analysis tool widely used, where a map is created using the degree of similarity between publications based on their shared keyword usage [6]. Keyword mapping is increasingly being seen outside of academic publications with uses in website development and online news sites to visually advertise the most read or commented topics [20]. Publications and researchers themselves are subject to analysis in bibliometrics, with the ‘success’ of researchers and their publications depending on many metrics, such as what journals their articles are published in [32], and how many citations each of their publications have received [24].
2.2 Scientific Blog Analysis Blogs in general are examples of participatory journalism, with scientific blogs primarily addressing issues and topics that are published in academic journals but also extending to scientific issues of interest to the public (e.g. global warming or health policy). Scientific blogs (and all other types of blogs) have major strengths, in that they have the ability to provide instantaneous commentary on a subject with simultaneous feedback on their own content. Science blogs and bloggers may act as ‘honest’ voices, with discussions centering not only about the actual content of their focus but also on the style and methodology, without having to use ‘polite’ tones commonly found in scientific publications when addressing actual or perceived errors [15, 1, 2, 3]. Science blogs are also often used as a mechanism to engage non-scientists as a form of community outreach [8]. Scientific blogs also have the ability to reintroduce older publications to the reader environment, where the scientific content may be out of the scope of interest of current research spheres but may still hold interest amongst involved readers. The differences between the two mediums are apparent in terms of the complexity of the science discussed which makes blogs interesting analytical tools as they source and address characteristically different knowledge bases. The analysis of blogs and social media has recently enjoyed widespread attention [3, 20]. Furthermore, mirroring bibliometrics, the modeling of how ideas flow through blogs has begun to be addressed [16]. While there is extensive work on analyzing blogs, specific work studying science blogs, for example [29], is less extensive and has focused primarily on the relationships between blogs themselves and not on its relationship to the academic literature.
3. METHOD To study blogs using bibliometric techniques, the science blog aggregator Researchblogging.org was selected. It was chosen both for its focus on blogs about peer-reviewed science and its quality control mechanism. The individual bloggers and their posts are vetted, which maintains a degree of rigor and consistency as a data source. The site follows strict publishing protocols, in a similar manner to scientific journal publishing rules. These include having all blogs that register with the site checked by a moderator before inclusion using the following rules (quoted directly from [22]). • • • • • • • •
Contain at least one post meeting our guidelines and at least five total posts. Have been updated within the past six months. Be well-maintained by the blogger (e.g. relatively free from spam comments, bad code, etc.). Include original work by the blogger and link or cite materials taken from other sources. Meet community-established standards for decency (e.g. free from pornography, hate speech, etc.). Have some means of contacting the blogger (e.g. email address, “contact” form, comments, etc.). Be freely available to all readers. Be written in a supported language. (Currently English is the only supported language, but we plan on adding additional languages in the future.) (sic)
The guidelines for a post include rules such as that a post must discuss a peer-reviewed publication(s), it must contain a full and complete citation, the post should be original work, and it must be accurate and thoughtful [23].
The layout of the blog posts share some similarity to publications, which enables us to subject the blog posts to similar analyses as the publications. The topic section labeled Chemistry was chosen as an entry point. Each blog post between Sept. 1 2007 and January 20, 2010 was downloaded and parsed, and imported into a relational data program. The resulting corpus contains 295 posts from 52 bloggers. Every blogger and blog post was assigned a unique ID, with each post linking to the title of the post, the abstract of the post, publications referenced in the post, host journals of those publications, the journal impact factor, the Digital Object Identifier of the publications, the blog post date and the number of times the post had been viewed as reported by the Researchblogging.org aggregator. The journal impact factor was obtained from the SCImago website [26]. The impact factors available from SCImago are generated using the SJR indicator over the Scopus data set [17]. The Scopus data set provides additional coverage beyond what is included in ISI’s Web of Science. It is for this reason that we have chosen to use the Scopus journal impact factors. However, ISI’s Web of Science provides an arguably better interface and search output format, which is why we chose ISI’s output. All journals hosting the blog-referenced publications are present in both databases. Using the publication identifying data on each post, the individual publications were downloaded and parsed using SAINT [27] into a separate relational database from ISI’s Web of Science and these additional publication data were then linked to the blog postings and blogger IDs. Each publication downloaded was manually checked against the publications listed in the blog posts to ensure the correct publications were retained. Any extraneous publications were not included in any further analysis. The titles and abstracts of the blog posts and publications were parsed to individual word level with stopwords removed (again using SAINT).
3.1 Map construction Using the publication author-assigned keywords, at a token-based level, a similarity map of the articles referenced by all blog posts was created, with each node representing the publications and each edge indicating the degree of similarity based on the Jaccard index of similarity1. Two versions of the article similarity map were created where:
1
1.
The node size was adjusted to reflect the degree count of each publication. This signifies the relative degree centrality of each publication, where degree centrality for a node is the sum of links to other nodes in the network. A higher degree count means the article is similar to a higher number of different articles in the set, and a lower degree count means the article is similar to fewer or no other articles in the set.
2.
The node size was adjusted for the number of times the blog posting(s) referencing the publication was viewed.
The Jaccard Index of similarity between two objects A and B, can be written as follows: (c)/(a + b – c) where c is the count of shared tokens between A and B, a and b are the count of unique tokens of A and B respectively including shared tokens. The measure result is between 0 (no similarity at all) and 1 (identical) [11].
A large node would signify that the blog post(s) have been read often, and by proxy the content of the publication(s) disseminated to a wider audience.
the size of node corresponding to degree count and nodes have been colored to reflect the year in which the publication was published.
Following this, a map using keyword similarity was created in which each publication node in the map was then assigned satellite nodes consisting of the shared title words of the publication title and blog title, and the unique words found in each of the publication title and blog title. The complete map followed the scheme as shown below in Figure 1.
A
B
Figure 1: Two publications A and B share linked via the author-assigned keywords. The black satellite nodes indicate the shared publication and blog post title words, the grey nodes indicate the unique publication title words and the white nodes indicate the unique blog post title words. In the case of one article being referenced by more than one blogger, the words used by each blog post title were added together resulting in a larger ‘cloud’ of satellite nodes.
Figure 2a: Keyword similarity map of publications referenced in blog posts in Chemistry section on Researchblogging.org. Size of nodes corresponds to degree count (larger node indicates publication is more central in network). Edge width corresponds to similarity between nodes. (N=296 (full set count), n=235 (set count minus isolates)).
The map allows us to create a topic map of the subjects being discussed in the science blogosphere at present with the satellite nodes providing a view as to the differences between the commentary of the authors and of the bloggers when referencing the publications. The size of the nodes is the number of times each blog post has been viewed, indicating the interest in the blog post and by proxy the publications referenced. Due to space demands and readability, these maps have not been included but are available on request. In future work, a similar map will be created but using the analog to publications citations for blog posts, namely, inbound hyperlinks to determine the importance of each blog post. Additionally, title word co-occurrence maps were created to demonstrate the topics covered by a) the publications, and b) the blog postings. A comparison between these maps will highlight the topical differences and similarities discussed by the publications referenced as well as the blogs that reference them.
4. RESULTS AND ANALYSIS Below an analysis of the generated maps is given. The analysis focuses on the differences between scientific discourse on the Web and its counterpart, academic literature.
4.1 Scientific discourse on the Web is more immediate The maps created include keyword similarity maps displaying the publications referenced in the blog posts where in: Figure 2a each node is a publication with size of node corresponding to degree count (larger node indicates publication is more central in the network) and in Figure 2b – each node is a publication with
Figure 2b: Keyword similarity map of publications referenced in blog posts in Chemistry section on Researchblogging.org. Size of node corresponds to degree count (larger node indicates publication is more central in network). Edge width corresponds to similarity between nodes. (N=296 (full set count), n=235 (set count minus isolates)). Node colour has been selected to display the age of the publication (Red=2010-2006, Orange=2005-2000, Yellow=1999-1990, Green=1989-earliest start date (1952)) In Figure 3 – each node is a publication with size of node corresponding to number of page views of the blog post(s) referencing the publication. Comparing Figure 2b and 3, where
node size varies with degree count (as in 2b) and number of views (as in 3), there are marked differences.
blog posts focus primarily on recent publications. This is additionally supported by Table 1, which shows that a majority of the publications discussed were published in the same year as the blog post. However, looking at the page views map (Figure 3), the relative size of the older nodes is larger than recently published nodes in general, indicating that the number of page views related to the publications or content related to the publications is of interest to blog readers. This notion of interest is supported by studies that show that download statistics correlate to subsequent citations [25, 33]. Considering all the blog posts are younger than 3 years old, differences in size of node due to accumulation of page views is not significant.
4.2 Scientific discourse on the Web is more contextually relevant
Pajek
Figure 3: Keyword similarity map of publications referenced in blog posts in Chemistry section on Researchblogging.org. Size of node corresponds to number of page views of blog post(s) referencing the publication. Edge width corresponds to similarity between nodes. (N=296 (full set count), n=235 (set count minus isolates)). Node colour has been selected to display the age of the publication (Red=2010-2006, Orange=2005-2000, Yellow=1999-1990, Green=1989-earliest start date (1952)) Table 1: Comparison of blog post age to referenced publication date Difference in age (years) between blog post and publication discussed
Count of blog Difference posts (con) Count (con)
0
235
13
1
1
36
14
1
2
16
15
1
3
8
18
1
4
6
22
1
5
9
24
1
6
2
26
1
7
1
32
1
8
1
33
1
9
3
37
1
11
1
55
1
12
1
56
1
With publications that are much older, where the subject matter may not be as similar to any recent publications, almost all the green nodes, (signifying older publications) are isolates and only a few yellow and orange nodes are located in the primary subnetwork. One can also note that the maps are dominated by red nodes signifying publication dates younger than 2006. Thus, the
In comparing publication citation rates, most citations to publications occur after 3 years and diminish after 10 years. Putting aside the immediacy effect of blogs (as compared to publications), current blogs will often refer back to older publications whilst putting the knowledge contained within them into a more modern context. Considering the rate at which socially relevant topics appear, the publication lags affecting scientific publications rarely are able to maintain relevance, and are almost by definition always ex post (at least in experimental sciences). Blogs, however, are able to provide an ex nunc commentary utilizing the most recent of scientific publications.
4.3 Scientific discourse on the Web focuses on high quality science In order to determine the quality of science discussed in the blog posts, the available journal impact factors were used. Because most publications cited in the blogs are extremely recent, using citations as a quality indicator was not possible due to citation lag. Thus, based on the SJR indicator, it was found that 70.5% of the publications were in high-impact journals with rankings in the top 20 chemistry journals or higher. 21% of the papers appear in the top 60 publications over all fields such as Science, Nature, and the Annual Review of Biochemistry.
4.4 Scientific discourse on the Web includes the non-technical implications of science A table of the most common words used by blog posts only, publications only and shared words was created (Table 2). The table is designed to demonstrate the word usage for bloggers and scientists and considering the list of shared words versus the blog only words, one can see there is a large and complex shared vocabulary, with each knowledge base having dwindling counts of unique words. The unique words of the blog and publication titles are markedly different in that the more socially oriented words appear almost exclusively in the blog titles. Table 2: Most commonly used words that are shared and unique to bloggers and publications (partial table shown only). Blog only
Shared
Article only
Count
Word
Count
Word
Count
Word
8
month
24
synthesis
7
data
8
bio
17
graphene
6
organic
8
heat
15
chemical
6
virus
7
day
9
via
6
selective
7
climate
9
chemistry
5
identification
Blog only 7
worse
Shared 9
drug
4
Article only
4
flu
6
approach
3
controlled
mechanism
4
wikis
6
structural
3
acidification
3
calcification
3
allenes
7
medical
9
structure
4
ocean
3
blog
6
characteriz ation
6
living
7
gold
4
system
3
man-made
6
stable
6
nettab
7
properties
4
channel
5
journal
7
dna
4
evolution
3
mimetics
6
nanoparticl es 3
besides
6
hydrogen
3
coral
workflows
7
spectrosco py
3
5
4
fluorescent
3
bet
6
quantum
3
networks
5
technologie 7 s
raman
4
nmr
3
csi
6
species
3
receptor
5
think
7
single
3
accurate
3
rugged
6
cells
3
possible
5
your
7
carbon
3
proton
3
presents
5
dots
3
membrane
5
entity
7
molecular
3
capacity 3
some
5
rna
3
electrochem ical
4
adamantan e
7
based
materials
3
development
3
inspirations
5
simple
3
emission
3
neuramidas e
5
biological
3
bioactive
4
cheap
7
films
3
surfaceenhanced
4
swine
6
water
3
conversion
4
don
6
protein
3
environments
4
latest
6
formation
3
proteins
Figure 4: Blog post title coword map of publications. A threshold of a minimum of 2 co-occurrences of words has been set. Size of node corresponds to frequency of occurrence; edge thickness corresponds to frequency of co-occurrence (Total number of nodes for all subnetworks N=143). Nodes are coloured according to degree centrality- like coloured nodes have same values.
Figure 5 is a title word co-occurrence map in which the nodes are words linked to other words that they occur with in the titles of the publications referenced by the blog posts. Coword mapping, as mentioned previously, is useful in determining the topical landscape of the publications. Significant word groupings in Figure 5 address issues of the origins of chemistry, the future of different chemical fields, the origin of life, water research through atmospheric chemistry and anionic chemistry. Comparing this to Figure 4, the title word co-occurrence map in which the nodes are the title words used by the blog posts, this is a much more scattered system of subnetworks, with close to 20 different subnetworks, with the common word groupings. Many of the title word groupings describe scientific processes or phenomena, but
most of the groupings describe the implications of science, such as swine flu resistance, grand life schemes (“Life, the universe and everything else”), climate change, instances of plagiarism and references to online crowd-sourced knowledge bases (‘Wikis’). The ability of blogs to present scientific research to a potential audience of non-academic but interested people is apparent, with blog post word choice more oriented to current social, political and medicinal topics, whilst explaining the underlying sciences behind each topic in more detail in the blog. The coherence of the coword maps, especially in the publication title map, is interesting in the publication title map there is a large subnetwork indicating that the topics under discussion appear to be coherent in terms of the word usage. However with the blog title word map, even
Figure 5: Publication title coword map of publications referenced by blog posts. A threshold of a minimum of 2 co-occurrences of words has been set. (The largest subnetwork is shown here where n=182, total number of nodes for all subnetworks N=214). Nodes are coloured according to degree centrality- like coloured nodes have same values.
though the blogs are discussing the same publications and science behind it, the higher number of subnetworks and dispersed topics indicates that the extrapolation of ideas from the same knowledge set is more diverse (albeit with much of the diversity addressing non-scientific issues). This is an important result in of itself as it adds to the view that scientific blogging serves an important knowledge dissemination service.
5. CONCLUSION Researchblogging.org provides a unique resource for studying scientific discourse on the Web through classic bibliometric techniques. Using a combination of webometrics and biblometrics, an initial basis for the following conclusions about the scientific discourse on the Web was given: • • • •
It is more immediate that traditional academic literature. It is more contextually relevant than academic literature. It focuses on high-quality science. It focuses on non-technical implications of science.
The study presented here focuses on chemistry blogs. Future work will expand this study to consider other disciplines. Furthermore, additional work will be done to include blogs outside of the Researchblogging.org while still maintaining the link to bibliometrics and ensuring the same high quality of content.
6. ACKNOWLEDGMENTS The authors are supported by the Semantically Mapping Science project (http://www.sms-project.org/). Additionally, the authors thank Seed Media Group LLC. for hosting Researchblogging.org.
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