This document summarizes a presentation on altmetrics and social media data. It discusses how social media is being used in scholarly communication, providing examples of academics using blogs, wikis and other sites. It also explores emerging altmetric data sources like Twitter, benefits of social media use, and who discusses research online. The document then examines altmetric data providers, aggregators and metrics, addressing challenges around coverage, normalization and social bots. It argues for more research on improving altmetrics methodology.

1. Altmetrics: Listening & Giving Voice
to Ideas with Social Media Data
Anatoliy Gruzd, PhD
Canada Research Chair and Associate
Professor
Director of Research, Social Media Lab
Ryerson University, Toronto, Canada
@Gruzd
Gruzd@Ryerson.ca

2. Should scholarly use of social media be considered
towards tenure and/or promotion?
Gruzd, A., Staves, K., and Wilk, A. (2011).
Tenure and Promotion in the Age of
Online Social Media.
Proceedings of the American Society for
Information Science and Technology
(ASIS&T) Conference.
Back in 2011 …

3. This is what academics
say about Altmetrics on
Twitter
6 years later…

4. This is what academics
say about Altmetrics on
Twitter
6 years later…

5. How did we
get here?

6. Evolution in Scholarly
Communication Channels
Letters
Emails
Mailing lists
Social Media

7. Scholarly Communication: Then and Now
Letters of Edwin Gilpin, a mining engineer,
government official & author (1850-1907)
Tweets of a contemporary scientist in the
domain of Earth Sciences (2014)
MacDonald, B., Duggan, L., Gruzd, A, & Collins, V., Scientific Communication: Testing Historical
& Present-Day Communication Networks with Social Network Analysis. Working paper.
9 months | 1300 letters | people=616 | ties=1277 1 month | 1302 tweets | people=756 | ties=1578

8. Popular Social Media Sites among Academics
Frequent
Use
Non-academic
soc.networks
Blogs
Online
document
management
Media
repositories
Wikis
Occasional
Use
Presentation
sharing sites
Video/tele
conference
Blog Wikis
Academic
soc.networks
Gruzd, A., & Goertzen, M. (2013). Wired Academia: Why Social Science Scholars Are Using Social Media. The
46th Hawaii International Conference on System Sciences (HICSS): 3332-3341, DOI: 10.1109/HICSS.2013.614

9. Benefits of Using Social Media
0% 10% 20% 30% 40% 50% 60%
Discovering new funding
Garnering mass media attention
Publishing findings
Maintaining professional image
Soliciting advice from peers
Collaborating with other researchers
Making new research contacts
Promoting current work/research
Discovering new ideas or publications
Following other researchers' work
Keeping up to date with topics
Gruzd, A., & Goertzen, M. (2013). Wired Academia: Why Social Science Scholars Are Using Social Media. The
46th Hawaii International Conference on System Sciences (HICSS): 3332-3341, DOI: 10.1109/HICSS.2013.614

10. Related benefits of social media use
based on the factor analysis
Social & Info
Dissemination
Information
Gathering
Collaboration explains
24%
of the total
variance
explains
16%
of the total
variance

11. Who talks about research
on social media?
• Not just academics! But also
• institutions
• journalists
• librarians
• policy makers
• other groups

12. Unexpected Receptor
Communities

13. As more people talk about research online, social
‘signals’ are becoming more valuable for …
• Academics – discover what peers are discussing
• Institutions & Funders –assess research impact
• Publishers - ↑readership, feature most-discussed
research, discover popular topics for future calls
• ATP Committees – evaluate scholarly output / service-
component

14. Example: Libraries & Museums
Making Biodiversity Heritage Library (BHL)
collections more “social”!

15. Google Trends for “Altmetrics” and “Altmetric”
Altmetrics is …
A set of “metrics proposed as an
alternative to the widely used journal
impact factor and personal citation
indices, like the h-index”
(Wikipedia)
“Study and use of scholarly impact
measures based on activity in online
tools and environments”
(Priem, 2014)
“The creation and study of new metrics
based on the Social Web for analyzing
and informing scholarship”
(Adie & Roe, 2013)

16. Research on Altmetrics is growing… but still very young
Top 10 most prolific scholars in this area
Source: Web of Science, Sep 2017

17. Altmetrics: Research Topics
Common research questions:
• To what extent articles published in a journal
are discussed on social media (coverage)?
• Is there a relationship between altmetrics
and more traditional impact factors (correlation
studies)?
Ex: among altmetrics, blog count is the
strongest predictor of increased citations:
• “One more blog post discussing a
publication increases the chance of more
citations by 4.7%” (Hassan et al., 2017)
• Very discipline specific
• Recent review paper: Sugimoto et al., 2017

18. Altmetrics: Data Providers, Aggregators and Metrics
Data
Providers
Aggregators Metrics

19. Altmetrics: Data Providers, Aggregators and Metrics
Data
Providers
Aggregators Metrics

20. Altmetrics: Data Providers
Twitter
68%
Facebook
17%
Blogs
7%
News
6%
Google+
2%
% COVERAGE OF PUBLICATIONS IN SOCIAL MEDIA
* Based on ~1M articles published between
2011-2015 (indexed by Scopus) and that have at
least one citation & one social media mention
(captured up until Feb 2017)(Hassan et al., 2017)

21. Altmetrics: Data Providers
Lack of APIs for some prominent SN platforms
Researchgate.net Academia.edu

22. Altmetrics: Data Providers
Lack of attention to some other SN platforms
Reddit
(Kumar et al., 2018)
Content Type n=1,227 posts
(100%)
Explanation 592 (48%)
Information Seeking 274 (22%)
Providing Resources 260 (21%)
Socializing with Positive Intent 204 (17%)
Explanation with Disagreement 71 (6%)
Subreddit Rules and Norms 66 (5%)
Explanation with Agreement 45 (4%)
Socializing with Negative Intent 4 (0%)

23. Altmetrics: Data Providers, Aggregators and Metrics
Data
Providers
Aggregators Metrics

24. Altmetrics: Data Aggregators
(Melero, 2015)

25. NISO Alternative Assessment Project
http://www.niso.org/apps/group_public/document.php?document_id=17090
transparency
replicability
accuracy
Altmetrics: Data Aggregators

26. NISO Alternative Assessment Project
http://www.niso.org/apps/group_public/document.php?document_id=17090
Altmetrics:
Data Aggregators

27. Altmetrics: Data Providers, Aggregators and Metrics
Data
Providers
Aggregators Metrics

28. Altmetrics: Metrics
Examples based on a case study of measuring impact of a drug safety article published by the
Canadian Network for Observational Drug Effect Studies (CNODES)
with Gamble, Traynor, Gruzd, Mai, Dormuth, Sketris
Basic
Indicators

29. Altmetrics: Metrics Example
Who tweeted about the CNODES paper?
Twitter user type # users % users
Members of the public 22 84%
Practitioners (doctors, other
healthcare professionals) 3 11%
Science communicators
(journalists, bloggers, editors) 1 3%
Account Type Twitter Account
Organization @bcdpic
Organization @action_designer
Organization @e24Business
Individual @social_club_
Individual @Srinjoy
Organization @connectcontacts
Organization @StartupPortal
Individual @kekesimot
Organization @youngentre
Source: Altrmetric.com

30. Altmetrics: Developing Metrics based on Social Network
Analysis (SNA)
Nodes = Social Media Users
Ties (lines) = Interactions

31. • ~10% of the 3,005 blogs
analyzed cite at least 1
article from the dataset of
2,246 articles.
• The most influential blogs,
as measured by in-links, are
written by diabetes patients
and tend not to cite
biomedical literature.
Gruzd, A., Black, F.A., Le, Y., Amos, K. (2012). Investigating Biomedical Research Literature in the Blogosphere: A Case Study
of Diabetes and HbA1c. Journal of the Medical Library Association 100(1): 34-42. DOI: 10.3163/1536-5050.100.1.007

32. The Rise of Social Bots
• Who are we studying:
Humans or Bots?
Social Bot – software designed
to act on the Internet with
some level of autonomy
Altmetrics: Metrics - Challenges

33. Different Types of Bots
Free music,
games, books,
downloads
Jewelery,
electronics,
vehicles
Contest,
gambling,
prizes
Finance, loans,
realty
Increase
Twitter
following
DietAdult
(Grier et al, 2010)

35. Detecting Bots…

36. Detecting Bots…
Photo
• Color & Edge
histograms
• Color & Edge
Directivity
Descriptor
(CEDD)
• Image
Similarity
Message
• Sensitive
words
• URL
• Duplicates
• #hashtags
• @replies Poster
• Username
• Engagement
level
• Creation
date
SocialNetwork
• # Friends
• # Following
• In/out degree
centrality
• Clustering
(Yardi et al, ‘09; Grier et al, ‘10; Wang, ‘10; Jin et al, ’11; Varol et al, ‘17)

37. How to introduce these emerging
techniques to altmetrics researchers
and developers who are relying on
social media as their go-to data source!
The challenge is ...
© Chris Allen licensed under Creative Commons

38. Altmetrics: Challenges &
Opportunities!
• Lack of access to some data providers
• Mostly tracking social mentions based on
DOIs/unique identifiers
• Reliance on different data providers
• Measuring different things
• Need for transparency, replicability &
accuracy
• Noisy data and social bots

39. Altmetrics: Listening & Giving Voice to
Ideas with Social Media Data
Anatoliy Gruzd, PhD
Canada Research Chair and Associate
Professor
Director of Research, Social Media Lab
Ryerson University, Toronto, Canada
@Gruzd
Gruzd@Ryerson.ca
Slides available at http://bit.ly/4amkey

40. References
• Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010). @spam: the underground on 140 characters or less (p. 27). ACM Press.
http://doi.org/10.1145/1866307.1866311
• Gruzd, A., Black, F.A., Le, Y., Amos, K. (2012). Investigating Biomedical Research Literature in the Blogosphere: A Case Study of Diabetes and
HbA1c. Journal of the Medical Library Association 100(1): 34-42. DOI: 10.3163/1536-5050.100.1.007
• Gruzd, A., Staves, K., and Wilk, A. (2011). Tenure and Promotion in the Age of Online Social Media. Proceedings of the American Society for
Information Science and Technology (ASIS&T) Conference.
• Gurajala, S., White, J. S., Hudson, B., Voter, B. R., & Matthews, J. N. (2016). Profile characteristics of fake Twitter accounts. Big Data &
Society, 3(2), 2053951716674236.
• Hassan, S. U., Imran, M., Gillani, U., Aljohani, N. R., Bowman, T. D., & Didegah, F. (2017). Measuring social media activity of scientific
literature: an exhaustive comparison of scopus and novel altmetrics big data. Scientometrics, 1-21.
• Kumar, P., Gruzd, A., Haythornthwaite, C., Gilbert, S., Esteve Del Valle, M., Paulin, D. (2018). Social Media in Educational Practice: Faculty
Present and Future Use of Social Media in Teaching. In Proceedings of the 51st Hawaii International Conference on System Sciences.
• MacDonald, B., Duggan, L., Gruzd, A, & Collins, V., Scientific Communication: Testing Historical & Present-Day Communication Networks
with Social Network Analysis. Working paper.
• Melero, R. (2015). Altmetrics–a complement to conventional metrics. Biochemia medica, 25(2), 152-160.
• Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: a review of the literature. Journal
of the Association for Information Science and Technology, 68(9), 2037-2062.
• Varol, O., Ferrara, E., Davis, C. A., Menczer, F., & Flammini, A. (2017). Online human-bot interactions: Detection, estimation, and
characterization. arXiv preprint arXiv:1703.03107.
• Wang, A. H. (2010). Don’t follow me: Spam detection in Twitter. In Proceedings of the 2010 International Conference on Security and
Cryptography (SECRYPT) (pp. 1–10). IEEE.