Twitter Users in Science Tweets Linking to Articles:

The Case of Web of Science Articles with Iranian Authors

Ashraf Maleki

Department of Library and Information Science, University of Tehran, Tehran, Iran

Although there is some evidence that tweets linking to articles reflect earlier formal

citations, it is not clear whether this potential advantage of tweets can be traced in different

geographical regions. To test this on a different country with relatively fast scientific growth

during recent years, tweets to Web of Science indexed articles by Iranian authors (2011-

2012) are investigated. About 5% of articles had tweeted links, mostly in life sciences and

biomedicine (10%). Further analysis of 1,236 tweets showed that only 11% of 589 sampled

articles were tweeted by academics, whereas this was 41% by non-academics, suggesting

the social impact of tweets rather than their academic influence. Finding moderate

correlations between very limited articles with academic tweets and their citation counts,

only in life sciences and biomedicine (excluding other fields), suggests that academic value

of tweets linking to articles should be cautiously used for research evaluation, although it

may reflect social influence of research.

Keywords: Altmetric, tweet metric, scholarly impact, citation, social impact, authoritative tweeters

Introduction Although traditional citation analysis allows to access academic aspect of research impact, wider

influence of research that seems untraceable through citation have so far been mainly ignored. Along

with discovering capabilities in web, new dimensions of research assessment such as teaching, cultural,

usage, and social impact of publications have become measureable through various web-based sources,

like online syllabus and reading lists, power points files, Mendeley bookmarks, ResearchGate and

Academia.edu usage statistics, science blogs, and Twitter mention (see more in Kousha, 2014). Among

metrics harnessed to online social networking sites tweets including URLs linking to articles are

considered to be appropriately prevalent both in publication coverage (Thelwall et al., 2013) and variety

of Twitter users involved in publication mention. What makes these metrics important is that they

appear faster than traditional citations and can be earlier predictor for well ahead formal citations (Shuai,

Pepe, & Bollen, 2012; Thelwall et al., 2013). This is more dominant about tweet metric values as there

are evidence that tweets can help to recognize highly cited papers just after publishing research

(Eysenbach, 2009). However, it is unclear whether these potential advantages of tweets are available to

assess publication in different geographical regions.

There are evidence that geography vitally impacts aspects of Twitter social network, for example

national information exchange in Twitter intensely exceeds international ones (Kulshrestha, Kooti,

Nikravesh, Gummadi, 2012), although its effect has not yet been examined on science communication

in tweets. Therefore, this research examines tweet uptakes in the context of scholarly articles of Iran, as

a completely different case amongst developing countries which had a substantial scientific publication

growth, in recent years (e.g. Moin, Mahmoudi, & Rezaei, 2005; Anonymous, 2005; McKenzie, 2010;

Brown, 2011), and had the capability to appear amongst limited number of countries with the most cited

1% papers (King, 2004). Nevertheless, how this might affect publication uptakes through tweets, can

be generally important for non-Western countries facing new metrics in research impact assessment.

Hence, the results in this research might show how tweet-metric values can be useful in research

assessment in Iran.

A fundamental problem with understanding tweets is lack of clear scholarly content in 140-

character limited space in tweets. Attempts to classify scientific tweets and evaluate extent of scholarly

communication were laborious and limited to initiatives like journals receiving tweets to articles (e.g.

Thelwall, Tsou, Weingart, Holmberg, & Haustein, 2013), researchers within given disciplines who are

twitter users through interviews (Priem & Costello, 2010), their hashtag usage (e.g. Weller &

Puschmann, 2011), or classifying scholarly content provision (Holmberg & Thelwall, 2014). Moreover,

some research attempt to understand scientific tweets dissemination in conference through tweeter

network analysis (e.g. Letierce, Passant, Breslin & Decker, 2010), however it is not known how articles

might be disseminated in twitter. This may help us understand how tweets help crossing the gap between

researchers and general audience by showing whether general audience in Twitter does significantly

share similar interest as academics in research articles. Thus, this research is a quantitative approach to

understand impact in tweets represented by tweeters’ contribution in article dissemination.

Scholarly Impact Evaluation through Tweets Although Twitter is intended for general social communications it is used for in-the-moment, quick

messaging in education, and information sharing academic conferences (e.g., Bonetta, 2009; Letierce

et al., 2010; Priem & Costello, 2010; Ross, et al., 2011). Veletsianos (2011) conducted a contextual

analysis of scholars in Twitter and identified different seven activities for using twitter such as sharing

information and resources for professional, teaching and social networking purposes. Also, there are

estimates of scholarly communication in Twitter among researchers. Priem et al. (2010) assessed 2.5%

of scholars active in Twitter, whereas one third of their tweets contain scholarly content. A new

evidence discloses 34%, 23%, 22%, 7.5%, and 6.5% of tweets from researchers in biochemistry,

astrophysics, digital humanities, history of science, and economics, respectively, with the purpose of

scholarly communication (Holmberg & Thelwall, 2014). This might be the reason for increased care on

part of information providers to the new type of information resources; as an instance, consider the

project of archiving and making retrievable tweets of scholars in the Library of Congress (Costello &

Priem, 2011).

Whilst there is an increasing interest on part of authors to assess public views and discussions

about their papers in online social media tools (Wouters & Costas, 2012), scholars approve that they

cite on Twitter and tweet mentions might reflect scholarly impact (Priem & Costello, 2010). Other study

showed that tweets correlate moderately with citations of Scopus and closely with citations of Google

Scholar (Eysenbach, 2011). Twitter followers are also analyzed for estimation of across-the-board

impact of tweets from researchers. Darling et al. (2013) conducted a content analysis on twitter profiles

of active tweeting marine scientists and found that their followers mainly (55%) are science students,

scientists, and science organizations, while the rest include non-scientists, media and general audience.

Besides, there are estimates of extent and quality of article uptake in life science and biomedicine,

representing that less than 10% of 1.4 million PubMed articles are spread through tweeting, while there

are differences in journal and specialty levels (Haustein, et al., 2013). Moreover, in a content analysis,

Thelwall et al. (2013) examined sample of 270 tweets linking to articles of four journals, four digital

libraries and two DOI URLs, finding no critical emotion in them and 42% of tweets with article title

mention and 41% with brief summary of article.

Content Analysis and URL-sharing Tweets There are diverse motivations that drive URL-tweeting. The prior idea about URL-sharing tweets

classifies them as information sharing (Java et al, 2007) that seem to be more valuable to users to read

(André, Bernstein & Luther, 2012). URLs in scholarly tweets have been evaluated in various context.

Weller, Dröge, and Puschmann (2011) reported 40% and 27% of tweets including URLs from two

conference. Holmberg and Thelwall (2014) also found links with the purpose of scholarly

communication in about 10%, 7%, 5%, 4%, and 3% of tweets from researchers in astrophysics,

biochemistry, economics, history of science and, digital humanities, respectively, whereas in fact they

created more links - 23%, 21%, 38%, 27%, 15%.

Also, various characteristics of tweeting conventions are evaluated. For instance, there is

evidence that URLs are prevalent in retweets, as Boyd et al. (2010) reported 52% of retweeting URL-

containing tweets and Weller, Dröge, and Puschmann (2011) also found in tweets of two conferences

almost 50% of retweets that included URLs linking to targets like blogs, slides, projects, press, media,

conference, twitter, publications etc. However, regarding tweets including URLs to scientific article,

there are evidence that retweets, which create about 19% of total tweets, seem neutral in research impact

assessment of Nature articles, as they do not create significant difference among them (Holmberg,

2014).

Various studies concerning aspects of content analysis in tweets embedded URL-linking within

diverse categories. Dann (2010) summed up five prior categorizations over content analysis of tweets

(Java et al, 2007; Jansen et al., 2009; Pear Analytics, 2009; Honeycutt and Herring, 2009; and, Naaman,

et al., 2010), presuming two type of conversational and pass-along tweets capable to include URLs in

categories involving referral, query, response, retweets, user generated content, and endorsement.

However practically Dann’s categorization, makes overlapping groups of tweets (for example a query

tweet can also be a referral statement or a retweet). Therefore for the purposes in this research it is

useful to emphasize article URL tweeting practices based on usage of @user statement in posts and

regenerate two categories of tweets with @user statement and without it.

Other studies found twitter content analysis useful for examining disseminating factors in mass

communication studies, social sciences, sports, politics and even medicine to find about the networks’

marketing capabilities, communication patterns of celebrities like successful athletes with their fans,

sentiments in trendy social events and epidemic medical situations, and also to predict political

elections. Table 1 gives an overview on examples of those studies in different fields. Thus, in this study

it is examined how various twitter users support article dissemination.

Table 1. A number of content analysis researches on tweets

Article bibliography Dataset Broad topic Research purpose

Understanding professional athletes’ use

of Twitter: A content analysis of athlete

tweets (Hambrick, Simmons, Greenhalgh

& Greenwell, 2010).

1,962 tweets Sports To examine types of

Twitter use among

professional athletes

What Are Libraries Doing on Twitter?

(Stuart, 2010)

Data from 443

tweeting

libraries

Information

Science

To study how libraries are

using Twitter

Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1

outbreak (Chew & Eysenbach, 2010)

Sample of 5,395 tweets about

swine flu

Medicine To examine public perceptions using Twitter

during the 2009 H1N1

pandemic

Twittering to the top: A content analysis

of corporate tweets to measure

organization-public relationships (Edman,

2010)

1,577 tweets of

47 corporation

on 94 Twitter

homepages

Mass

Communication

Exploring through the

ways of corporations

social communication on

twitter

Predicting Elections with Twitter: What

140 Characters Reveal about Political

Sentiment (Tumasjan et al., 2010)

104,003 tweet Politics,

Elections

Examined usage of

Twitter for political

deliberation in German

federal election

Bad News Travel Fast: A Content-based

Analysis of Interestingness on Twitter

(Kunegis & Alhadi, 2011).

Three dataset

with overall

more that 60 million tweets

Social sciences To examine likelihood of

a retweet and discover

interestingness patterns of tweets

Tweet, tweet, tweet: A content analysis of

nonprofit organizations’ Twitter updates

(Waters & Jamal, 2011)

773 tweets Social sciences Examined ways of

nonprofit organizations

usage of Twitter

Shoveling tweets: An analysis of the

microblogging engagement of traditional

Tweets of 180

accounts on

2009 and 198

Mass

Communication

Using Twitter by US

newspapers and television

station

news organizations (Messner, Linke &

Eford, 2011, April)

accounts on

2010

Sentiment in Twitter events (Thelwall,

Buckley & Paltoglou, 2011)

Almost 35

million English-

language tweets

Social sciences Assessing association of

interests in Twitter with

heightened emotions

Trending Twitter topics in English: An

international comparison (Wilkinson &

Thelwall, 2012).

0.5 million

tweets

Social sciences Trending topics on

Twitter for various

countries

Twitter as a source of vaccination

information: Content drivers and what they are saying (Love et al., 2013).

6,827 tweets Medicine Studying tweets about

vaccinations, documenting sources, the

tone, and medical

accuracy of the

conversations

Research Questions 1. To what extent research articles by Iranian authors are tweeted and how this may correlate with

formal citations across fields?

2. Is there significantly similar research interest shared by tweeters from within various careers?

3. How scientific articles are disseminated amongst tweeter groups?

Method To address research issues, DOIs of 102,168 documents of Iran published during 1997-2012 which

were indexed in Thomson Reuters Web of Science (WoS) were checked through Altmetric.com for

articles hyperlinks in tweets. The reason for selecting WoS collection is that it yet makes the most

promising resource for researcher excellence evaluation in Iranian universities and institutes. Hence,

studying tweet mentions to these articles might reveal their other aspects of impact, particularly when

citation counts is not available about them. Thus, broader research areas of WoS are used for general

subject categorization of articles.

Altmetric.com is used to gather data because it uses a custom-built system which tracks articles’

out-links in various web resources like publishers’ page, abstract databases like PubMed, and

repositories like arXiv and SSRN either by article’s URLs or disambiguated identifiers (Adie, 2013).

CrossRef’s digital object identifier (DOI), which helps unique and permanent online identification of

articles, is an established and advantageous handle for linking an article’s altmetric results targeting its

various directories in web (Costas, Zahedi & Wouters, 2014). Therefore, for gathering data, feature of

“With identifier” in the page of Explore the data in member users’ interface of Altmetric.com was used

for manual submitting 50 DOIs in each search, and altmetric statistics results were saved to text files

with button of “Export articles” in Articles tab (data gathering took twenty days until July 21, 2013).

However, this research associates with limitations caused by using DOIs such as non-comprehensive

coverage of articles and disciplinary biases in favour of science, technology, engineering and

mathematics rather that social science and humanities (Alperin, 2013).

Concerning last two questions in research a random sample of 589 articles with a confidence

level of 95% in each broader field were selected for tweeter categorization through their profile

information (see Table 3 for proportion of sampled articles, and number of total and unique tweets

linking to them across fields). In this regard repeated tweets from the same tweeters were removed and

1,236 unique tweets out of 1,500 tweets were analyzed by twitter users. In order to analyze Twitter

users, highly tweeted (220 tweets) article of How to construct the perfect sandcastle was omitted, to

avoid unreal increase in proportion of public individuals with diverting count in one article.

Subsequently, tweeters’ profile descriptions and when needed links to online web CVs were read and

careers were classified based on content analysis of words in profiles, resulting categories in Table 2.

To answer second question of research a factor analysis approach is conducted to classify

tweeters with similar interest. Therefore, a common factor analysis (CFA) method is used as data

reduction method to find latent communities of tweeters with similar articles of interest. As addressed

in the third question of research, to find who more influential tweeters in article dissemination are,

tweeter-tweetee network is analyzed. In words of Letierce et al. (2010), in Twitter, hubs are ‘users who

address lots of @user messages’ and authorities ‘receive tweets from others, using the same @user

pattern.’ To learn authority and hub tweeters in tweeting network of articles, Webometric Analyst

(lexiurl.wlv.ac.uk) was used to extract and map tweeter-tweetee connections and Sci2Tool

(sci2.cns.iu.edu) was used to run HITS algorithm (Kleinberg, 1999) for authority and hub scores of

tweeters.

Table 2. Classification scheme used for user analysis of article tweeters

Students (including undergraduate and graduate students, and postdocs) Official researchers (with an academic affiliation and career stage)

Professionals and graduates out of academia (including independent researchers, and anyone introducing a

profession) Public Individuals (including non-professional profile information, pseudonyms that gave no profile

information, unclearly described account, and discarded accounts)

Science organizations (including universities, libraries, conferences, institutes, and online scientific

associations) Media and media activists Product or service providers (including NGOs, companies, online health service centres, etc.)

Topical article updater (subject specific tweeters)

Journals and publishers (including official or unofficial publication or news and comment accounts of publishers)

Results

Tweets to Iranian Scholars’ Articles Main finding of study in Table 3 shows that in 96,653 of total checked articles, 2.3% (2,256) received

5,169 tweets linking to them. However, note that proportions are normalized in different time period

regarding papers with positive tweet mentions, as mentioned in Table 3 for each broader research area.

Of tweeted articles recognized through Altmetric.com 98% were published in 2010 to 2012 and only

2% were published before this period. This is while Altmetric.com claims on its website that “any

transient mentions of publications before 2011 might have been missed about tweets.” Hence results in

Table 3 are normalized separately for publications before 2011 and after it.

Proportion of total articles (5.2%) tweeted in 2011-2012 is comparable to that of all years (2.3%).

The most tweeted articles (10%) were in life sciences and biomedicine and the least in technology (2%).

About 70%, 14%, 9% and 7% of tweets link to articles in life sciences and biomedicine, physical

sciences, social sciences and humanities, and technology, respectively. Although gradual increase in

proportion of tweeted articles in all fields, further results show that there are considerable number of

articles which are tweeted, while they get no WoS citation yet. So, among tweeted life sciences and

biomedicine articles published in 2012 (15%) only 5% had WoS citations, although this is different

across subject areas. For being very recent, formal citations are extremely low (63% of articles got no

formal citation at the time of gathering data). Nonetheless, medians of WoS citations are twice (2) as

much as tweets (1) in all fields, excluding life science and biomedicine (both 1) and none of their

correlations were significant.

Table 3. Total checked Web of Science indexed articles with Iranian authors, tweeted

proportion of papers and sample sizes in terms of broader research areas

Research Areas

(Publication

Years)

No. (%) of

articles

with DOI

No. (%) of tweeted

articles in altmetric.com No.

(%) of

tweets

Sample

Up to 2010 2011-2012 No. (%) of articles

No. of tweets

No. (%) of unique tweets

Life Sciences &

Biomedicine 30,347

(31%) 162 (0.9%)

1,259

(10%)

3,658

(71%) 232 (16%) 560 534 (43%)

(2001-2012)

Physical Sciences

(2001-2012) 34,803

(36%) 25 (0.1%) 542 (4%) 708

(14%) 183 (32%) 256 247 (20%)

Technology

(2005-2012) 29,080

(30%) 27 (0.1%) 198 (2%) 352

(7%) 128 (57%) 233 204 (17%)

Social Sciences &

Humanities

(2002-2012)

2,423 (3%) 8 (0.04%) 36 (2%) 451

(9%) 44 (100%) 451 251 (20%)

Total 96,653

(100%) 222 (0.4%) 2,035

(5.2%)

5,169

(100%) 589 (26%) 1,500 1,236

(100%) 2,256 (2.3%)

Twitter Users Categorizing tweeters of sampled articles, 656 unique accounts were generally recognized as 417 (64%)

individual accounts with a personal identity, 174 (27%) specialized accounts that mostly tweet in

specific subjects, 60 (9%) publishers and journals, whereas only 5 (1%) libraries. Figure 1 and Appendix

1 compare share of tweeted articles by various types of Twitter users in four broader research areas.

Main results suggest that proportionally official researchers in social sciences (33%), topical article

updaters both in life sciences and biomedicine and physical sciences (respectively 36% and 39%), and

public individuals in technology (21%) are tweeting higher proportion of the articles. Among other

results, science organizations are dominantly tweeting in physical sciences, whereas professionals and

graduates are relatively prevailing in two broader disciplines of life science and biomedicine, and social

science and humanities. Further results demonstrate that frequency of tweets from public individuals

and professionals and graduates out of academia mostly exceeds tweets from other tweeter classes

(Appendix 1).

Figure 2. Contribution of various Twitter users in terms of tweeted articles in four broader

research areas

Tweeter Communities: A Factor Analysis Approach A common factor analysis (CFA) is conducted to explore tweeters with common tweeting practice, and

an oblimin rotation is applied as a solution to consider the existing correlation among variables

(tweeters). KMO measure of sample adequacy (KMO = 0.679) being near to 0.7 demonstrates that

sample is just adequate for factor analysis. Three resulting factors are shown in Table 4 that distinguish

only 36.5% cumulative variance in this examination. The reason for low variances is that CFA method

considers an error for each variable, and in contrary to methods like principle component analysis, it

differentiates between common parts of variables with their unique parts. Thus, it is believed that

recognized factors in this method are much near to reality than the later method for considering errors

and communalities.

As shown in Table 4, although science organization and professionals and graduates out of

academia share highest communalities (0.903 and 0.727, respectively) in the extracted factors official

researchers and science organizations are part of the main underlying pattern of tweets in the first factor

by highest eigenvalue (2.127) and distinguishing almost 24% of variance through the factor. Media and

online service and product providers make up the second factor by about 9% of variance, whereas

professionals and graduates out of academia, public individuals and students appear in the third factor,

distinguishing 4% of variance. Publishers and journals and topical article updaters share the least

communalities and seem to be appearing in none of the factors (for loadings < 0.32). Among the

variables the highest and the most persuasive loading belongs to science organizations (0.778) in the

first factor, while loading from rest of the variables in all three factors are moderate or weak.

Further analysis disclosed that only in life sciences and biomedicine, by contribution of official

researchers or science organizations (tweeters in first factor) total tweet values would have

appropriately moderate correlation with formal citations (significant in r=0.459, p<0.01), whereas

correlations were not significant in other disciplines, and as well not surprisingly when considering

tweeters in other two factors.

Table 4. Loadings of a common factor analysis (CFA) after an oblimin rotation, with factor

eigenvalues and communalities of tweeters

Factor Communalities

1 2 3 Extraction

Official Researcher .571 -.187 .189 .425

Science Organizations .778 .415 -.177 .903

Media & Media Activists -.039 .682 .103 .451

Online Service and Product Providers .025 .443 -.045 .207

Students .110 .009 .330 .153

Professionals & Graduates Out of Academia .380 .125 .588 .727

Public Individuals -.043 .131 .587 .333

Publishers or Journals .007 .070 -.111 .017

Topical Article Updaters .112 .164 .119 .076

Total Eigenvalues 2.127 .842 .322

% of Variance 23.636 9.358 3.576

Cumulative % 23.636 32.994 36.570

a. Rotation converged in 20 iterations.

Authority and Hub Tweeters of Scientific Articles: Retweets and Conversations Initial results demonstrate that tweets including a @user statement address about 20% of tweeted

articles in the sample, in which 117 tweeters target 71 other twitter users in their posts. Figure 2 shows

interconnections between tweeter categories as formed in a tweeter-tweetee network. Direction of

arrows are from sources (hubs) to targets (authorities). It is apparent from this figure that official

researchers are receiving most tweets from other tweeters, and earn the higher authority scores in

various disciplines (see examples in Table 6). Further results showed that official researchers are often

mentioned by other peers (66%) and science organizations (about 30%). This also supports strong

association of science organization and academic tweeters in factor analysis. Topical article updaters,

however, appear to be well-known resources for various tweeters as they are retweeted or referred by

many users.

Figure 3. Directed tweeter-tweetee network in term of tweeter categories (mapped in

Webometric Analyst)

Table 5 suggests the highest authority and hub score tweeters in various fields. The notable results

in table shows that Official researchers appear as high score authorities in all disciplines except in

physical sciences where journal and publisher are dominant. For example among official researches

@Peterasinger in life science and biomedicine, @Benvanveen in social sciences, and both @Clhw1

and @Adamgdunn in technology were the most preferred tweeters in posts, who gained highest

authority scores, whereas journal and publisher tweeters like @Njphysics, @Aip_publishing in physical

sciences and @Wbpsychology in social sciences and humanities are referred the most in tweets. Science

organizations are remarkable tweeters that are in the middle of science dissemination both in physical

sciences and life sciences and biomedicine. As @blackphysics and @Africanphysics in physical

sciences and @Whadvocacy in life science and biomedicine were with the highest hub scores. Main

hubs in social sciences and technology are distinctive professionals in which one is a researcher,

@Adamgdunn, with both high hub and authority scores and it suggests that he is highly tweeting as

well as being tweeted.

Table 5. Higher hub and authority score tweeters within each discipline

Tweeter Hub

score Tweeter Category

Tweetee

Authority

score Tweeter Category

Physical Sciences

Blackphysicists 0.613581 Science Organization Njphysics 0.613234 Journal

Africanphysics 0.379214 Science Organization Aip_publishing 0.378999 Publisher

Life sciences and Biomedicine

Sandrodemaio 0.120762 Official Researcher Peterasinger 0.144854 Official Researcher

Whadvocacy 0.098388 Science Organization Ncdchild 0.119678 Science Organization

Social sciences

Emxily_ 0.249962 Professional Wbpsychology 0.499850 Publisher

Guidobruinsma 0.249962 Service Provider Benvanveen 0.166617 Official researcher

Technology

Adamgdunn 0.407089 Official researcher Clhw1 0.290203 Official researcher

David_colquhoun 0.407089

Professional and online

writer

Adamgdunn 0.145102 Official researcher

Discussion and Conclusion Due to practical constraints, feasibility of examining national level dataset could only be guaranteed

with DOI-submission into Altmetric.com. Therefore opportunity to check tweet counts to almost 40%

of Web of Science indexed articles of Iran that exclude DOIs were lost. Another potential problem is

that the examined sample was roughly adequate for factor analysis, while factor analysis does more

reliable data reduction solutions when data is really big. Therefore it is probable that the results are not

the same for other datasets.

There were 2.3% uptake of articles from Iranian scholars during different time periods, the most

in life sciences and biomedicine (5%) and the least in technology (2%). However the proportion of

tweeted articles in 2011-2012 were 5.2% with also proportionately higher disciplinary uptake,

especially in life sciences and biomedicine (10%). Tweet uptakes was studied in different time periods

in previous studies. Thus, tweeting patterns reported in previous studies are compared in equivalent

time periods to scholarly publications of Iran. For example, proportion of tweeted articles during 2005-

2012 in this study (2.5%) is just over another estimation (1.6%) in a general random examination of

20,000 WoS-indexed article through ImpactStory.org (Zahedi, Costas, & Wouters, 2014). Furthermore,

equating the results with that of Haustein et al. (2013) in PubMed (9%) in 2010 to 2012 for Iranian life

sciences and biomedicine publications suggests fairly similar uptake of articles (8%). Besides, although

this study takes all articles with Iranian affiliation either as corresponding or subsidiary authors, Iranians

contribute a major role in tweeted articles' authorship, for 1,854 (82%) articles with Iranian-affiliated

corresponding authors, of which 116 articles (78%) were in the top quartile of tweets. Hence, results

are not significantly influenced by major international co-authorships. This result is important, since

findings suggest that WoS-indexed publications with Iranian authors are tweeted in relatively similar

pattern as findings showed in other general datasets.

More noteworthy results suggest that there is a remarkable difference between academics and

non-academics’ article tweeting interest. In other words, 27% of articles tweeted by official researchers

(only 11%) and science organization significantly differ from 41% of articles that are tweeted by other

tweeter groups. Moreover, 24% of article are simply tweeted by topical article updaters for

classificatory reasons and could not be taken for research assessment purposes. Hence, it seems that

although tweets are broadly representative of social impact which is poorly reflected by formal citation,

scientific impact of article is available in a very smaller degree among a very limited proportion of

publications. So that tweets don’t generally seem promising in research assessment, but reflecting social

influence.

Analyzing retweets and conversational tweets is also in some ways peculiar, because they are

only about 20% of tweeted articles and there are already evidence that retweets are neutral in research

tweets impact assessment (Holmberg, 2014). However there are interesting results in networking

analysis of these tweets. An analysis on retweets and conversational references displays higher authority

scores for official researchers in most disciplines, whilst higher hub scores for science organization.

This is while results from factor analysis also find more communalities between these two tweeter

groups and their contribution in tweets in life science and biomedicine was better predictive of future

formal citation. These findings highlight significant scientific impact about tweeted articles with

contribution of official researchers and science organizations that might also appear either in retweets

or conversations. Additionally, professionals out of academia with higher hub scores are in the key

position of popular science reach to public individuals, and look as indicative of social impact of

research. They contribute in more tweets than official researchers and appear in the same factor as

general audience. Furthermore, topical article updaters are more known to most tweeters and appear

valuable for article reach, whereas journal and publishers are not similarly well communicated. This

suggests that tweeter audience tends to disseminate update information about a field rather than a

publishers’ newer publications.

In the studied sample, only 10 unique tweeters (1.5%) were recognized that did self-tweets (i.e.

tweet posts that are made by users who are the authors of the tweeted articles), of which none had

Iranian affiliation. Results show that in internationally collaborated works one of the none-Iranian

researchers might have self-tweeted, who is usually the corresponding author, while only subsidiary

authors did repetitive self-tweeting. It is observed that specifically in the context of a conference

(recognized by conference hashtag) main author tend to self-tweet the article. Iranian self-tweeters

appeared as a researcher at universities abroad. Also Persian pseudonym with no profile information is

observed for which there was not enough self-tweeting evidence. Due to no incentives for Iranian

researchers to care about tweets to articles, there is less engagement of Iranian tweeter users, however

this might not be the case for authors from within other nations. Hence, in terms of a practical

implications of tweets for research assessment process in Iran it seem that tweets might be

demonstrative of social impact of English science publications, however for inadequate coverage of

publication (5%) and moderate correlation of tweets with formal citations only in limited number of life

science and biomedicine article with academic tweets they might be cautiously used in research

assessment.

Ultimately, altmetrics are not fully understood and yet there are less indications to estimate value

of social networking based metrics. Hence, for purpose of practical application they are currently

indefinite and the least promising resources. This research tried to investigate aspects of twitter users,

either by examining their shared interest or by evaluating authoritative and disseminating sources inside

communication patterns in twitter around scientific articles, finding that tweeted articles by researchers

and science organizations appear significantly different from that of general audience. While tweeters

appeared in three different communities as known academic, business and media benefactors, and

general audience, they reflect different aspects of articles’ impact. Therefore, to take a successful

application of tweets in scientific evaluation further analysis on perhaps tweeter users, besides scholarly

context of tweets (Holmberg, Thelwall, 2014) might be needed. However, altmetric providers may ease

it by reporting tweets to articles from researcher twitter members separately, if possible, so that it may

be a further step to help tracking influential and authoritative tweets in science.

Acknowledgment The author would like to thank Dr. Kayvan Kousha, Statistical Cybermetrics Research Group of

University of Wolverhampton, for his very useful comments.

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Appendix 1. Frequency of tweets and tweeted articles posted by various Twitter authors in terms of communities more in communication

(normalization in each broader research area)

Twitter User in

terms of Impact

Theme

Social Sciences &

Humanities

Life sciences &

biomedicine Physical Sciences Technology Total No. of Unique

Twitter

Authors (No.

of Tweets)

No. (%) of

Tweets

No. (%) of

Articles

No. (%) of

Tweets

No. (%) of

Articles

No. (%) of

Tweets

No. (%) of

Articles

No. (%) of

Tweets

No. (%) of

Articles

No. (%) of

Tweets

No. (%) of

Articles

Official Researchers 16 (15%) 13 (33%) 35 (7%) 14 (6%) 18 (7%) 15 (8%) 40 (20%) 25 (20%) 109 (10%) 67 (11%) 72 (109)

Students 6 (6%) 5 (13%) 11 (2%) 9 (4%) 2 (1%) 2 (1%) 5 (2%) 4 (3%) 24 (2%) 20 (3%) 21 (24)

Professional &

Graduates Out of

Academia 20 (19%) 11 (28%) 125 (23%) 69 (29%) 23 (9%) 20 (11%) 21 (10%) 14 (11%) 189 (17%) 114 (19%) 148 (189)

Product and Service

Providers 4 (4%) 2 (5%) 15 (3%) 12 (5%) 2 (1%) 2 (1%) 8 (4%) 8 (6%) 29 (3%) 24 (4%) 25 (29)

Science

Organizations 16 (15%) 4 (10%) 57 (11%) 34 (14%) 48 (20%) 44 (24%) 18 (9%) 16 (13%) 137 (11%) 98 (17%) 75 (137)

Media & Media

Activists 5 (5%) 3 (8%) 5 (1%) 5 (2%) 1 (0%) 1 (1%) 1 (0%) 1 (1%) 12 (1%) 10 (2%) 11 (12)

Public Individuals 28 (26%) 12 (30%) 105 (20%) 58 (24%) 36 (15%) 26 (14%) 55 (27%) 27 (21%) 224 (20%) 123 (21%) 172 (224)

Journals &

Publishers 6 (6%) 5 (13%) 52 (10%) 39 (16%) 28 (11%) 25 (14%) 28 (14%) 25 (20%) 114 (10%) 94 (16%) 61 (114)

Topical RSS Feeds 13 (12%) 7 (18%) 129 (24%) 85 (36%) 88 (36%) 71 (39%) 26 (13%) 25 (20%) 256 (23%) 188 (32%) 90 (256)

Total Unique

Tweets 114 (100%) 40 (100%)

534

(100%)

238

(100%) 246 (100%) 183 (100%) 202 (100%) 128 (100%)

1,096

(100%)

589

(100%) 798 (1,096)