As part of the 2016 ESRC Festival of Social Science, SAGE Publishing and the Campaign for Social Science held a panel debate exploring big data and social media research. The panel sought to address: 1) What opportunities does the availability of big social data provide for social research? What new questions can we answer? 2) Characteristics of big data include volume, variety and velocity; what new skills and methods are needed for data collection and data analysis? 3) What ethical considerations are there in the use of social media data and other forms of big data? 4)What are the challenges in negotiating access to big data for social research? 5) What impact are these challenges having on the pace of change in the social sciences? How fast do social science courses need to evolve to equip researchers with the skills they need to engage in data intensive research? The full video of the panel debate can be found here: https://www.youtube.com/embed/XWUK_UMsz9M

1. Big Data, Social
Media Research
& Innovations in
Research
Methods
A panel debate as part of the 2016
In partnership with
&

2. Introducing our Panel
@sfwitherspoon @DrLukeSloan @mrkthmsknndy

3. Don’t forget to connect with us
throughout!
@SAGE_News
@CfSocialScience
#esrcfestival

4. 3057 respondents are
definitely planning on doing
big data research in the
future or might do so in the
future
SAGE’s Big Data Survey completed by over 9,400 social
scientists

5. What kinds of big data are social scientists using?

6. What challenges face big data researchers?
• Funding
• Access to data
• Finding collaborators with
the right skills
• Learning new
software/programming
skills
• Learning new methods

7. What challenges face researchers looking to engage in big data?

8. What challenges face those trying to teach big data?

9. Emerging Hurdles
• Data Access
• Ethical use
• Skills gap
• Software
• Interdisciplinary collaborations & research LABs
• How and where to publish

10. Resources to check out
• White paper: Who is Doing Computational Social Science Research? https://goo.gl/6cIga7
• Big Data Newsletter – sign up bigdataresearch@sagepub.com
• Methodspace group with Big Data resources https://goo.gl/d0Q3pW
• Twitter feed - @SAGE_Methods
• Big Data & Society Open Access Journal - http://bds.sagepub.com/
• Handbook of Social Media Research Methods

11. Don’t forget to connect with us
throughout!
@SAGE_News
@CfSocialScience
#esrcfestival

12. What Can Social Media Tell us About
the
Social World?
Luke Sloan (@DrLukeSloan)
Social Data Science Lab
Cardiff School of Social Sciences
Cardiff University

13. My research focuses on Twitter and how social media data can be used to understand social
phenomenon…
• Who Uses Twitter? (Sloan et al. 2015. Who tweets? Deriving the demographic
characteristics of age, occupation and social class from Twitter user meta-data.
Plos One 10(3), article number: e0115545. (10.1371/journal.pone.0115545)
• Who geotags? (Sloan and Morgan 2015. Who tweets with their location?
Understanding the relationship between demographic characteristics and the use
of geoservices and geotagging on Twitter. PLoS ONE 10(11), article number:
e0142209. (10.1371/journal.pone.0142209)
• Predicting the UK General Election 2015 (Burnap et al. 2016. 140 characters
to victory?: Using Twitter to predict the UK 2015 General Election. Electoral
Studies (10.1016/j.electstud.2015.11.017)
• Crime-Sensing Through Twitter (Williams, Burnap & Sloan 2016. Crime
sensing with big data: the affordances and limitations of using open source
communications to estimate crime patterns. British Journal of Criminology
(10.1093/bjc/azw031)
About Me
Out later this year: Sloan & Quan-Haase (Dec 2016)
SAGE Handbook of Social Media Research Methods

14. • Naturally occurring data
• Current and timely
• Temporal and geographical granularity
• Up to 1% of global traffic available for free
• Anyone with a Twitter account can collect this data
• Collect a random 1%, tweets containing keywords or tweets
from individual accounts
Context

15. • Boston Marathon (anomaly detection)
• US Presidential Election Reaction (gender &
sentiment)
• Ebola Crisis (geography, gender & sentiment)
• Worry Questions…
Three Case Studies

16. The Boston Marathon

17. The Boston Marathon

18. US Presidential
Election Reaction

19. US Presidential
Election Reaction

21. • Twitter data collected via COSMOS Desktop
• Tuesday 20th to Sat 24th Jan 2015
• Condition: contains “ebola”
• 182,517 tweets of which:
• 39,037 made by male users
• 31,244 made by female users
• 1,715 with geo-tagging enabled (0.94%)
The Ebola Crisis

22. Gender Differences
The Ebola Crisis

24. Male
Network
Female
Network

26. Geographical Differences
The Ebola Crisis

30. Intersectionality:
Gender, Geography & Sentiment
(+ive)
The Ebola Crisis

31. Pink = female tweeters Blue = male tweeters

32. Pink = female tweeters Blue = male tweeters +ive sentiment = larger

33. • Twitter tells us something about the public response to Ebola…
• … that is different to what we would normally find out through traditional social
research
• Demographic characteristics seem to impact upon online behaviour (words &
networks)
• All of this analysis can be done on COSMOS Desktop
• Scoping the issues, focus on more in-depth analysis
• Potential for social media analytics to provide real-time information on ebola
• Understanding demographic difference in networks and information flows enables
intelligent interventions (see Sloan et al. 2014 for a food industry example)
The Ebola Crisis

34. • Who is represented?
• What is sentiment?
• What are we missing?
• How do people use Twitter?
Worry Questions

35. Burnap, P. and Williams, M. (2015) ‘Cyber Hate Speech on Twitter:
An Application of Machine Classification and Statistical Modeling for
Policy and Decision Making’, Policy & Internet (7:2)
Burnap, P., Williams, M.L. et al. (2014), ‘Tweeting the Terror:
Modelling the Social Media Reaction to the Woolwich Terrorist
Attack’, Social Network Analysis and Mining (4:2 )
Edwards et al. (2013) Computational social science and
methodological innovation: surrogacy, augmentation or
reorientation?, International Journal of Social Research Methods,
16:3
Gayo-Avello (2012) I wanted to Predict Elections with Twitter and all I
got was this Lousy Paper: A Balanced Survey on Election Prediction
using Twitter Data, Department of Computer Science, University of
Oviedo Spain
Mislove et al. (2011) Understanding the demographics of Twitter
users, Proceedings of the Fifth International AAAI Conference on
Weblogs and Social Media
Schwartz et al. (201) Personality, Gender, and Age in the Language
of Social Media: The Open-Vocabulary Approach, PLOS ONE, 8:9
(DOI: 10.1371/journal.pone.0073791)
Sloan et al. (2013) Knowing the Tweeters: Deriving Sociologically
Relevant Demographics from Twitter, Sociological Research Online,
18:3 (http://www.socresonline.org.uk/18/3/7.html)
Sloan et al. (2014) Going Viral in Social Media – Networks and
Intercepted Misinformation, Software Sustainability Institute, Cardiff
University
Sloan et al. (2015) Who Tweets? Deriving the Demographic
Characteristics of Age, Occupation and Social Class from Twitter
User Meta-Data. PLOS ONE 10(3): e0115545.
doi:10.1371/journal.pone.0115545
Sloan, L. & Morgan, J. (2015) Who Tweets with Their Location?:
Understanding the relationship between demographic characteristics
and the use of geoservices and geotagging on Twitter. PLOS ONE
10(11): e0142209. doi:10.1371/journal.pone.0142209
Williams, M. L. and Burnap, P. (2015) ‘Cyberhate on social media in
the aftermath of Woolwich: A case study in computational
criminology and big data. British Journal of Criminology
References & Key Readings
web: socialdatalab.net

36. Don’t forget to connect with us
throughout!
@SAGE_News
@CfSocialScience
#esrcfestival