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Big Data, Social
Media Research
& Innovations in
Research
Methods
A panel debate as part of the 2016
In partnership with
&
Introducing our Panel
@sfwitherspoon @DrLukeSloan @mrkthmsknndy
Don’t forget to connect with us
throughout!
@SAGE_News
@CfSocialScience
#esrcfestival
3057 respondents are
definitely planning on doing
big data research in the
future or might do so in the
future
SAGE’s Big ...
What kinds of big data are social scientists using?
What challenges face big data researchers?
• Funding
• Access to data
• Finding collaborators with
the right skills
• Lear...
What challenges face researchers looking to engage in big data?
What challenges face those trying to teach big data?
Emerging Hurdles
• Data Access
• Ethical use
• Skills gap
• Software
• Interdisciplinary collaborations & research LABs
• ...
Resources to check out
• White paper: Who is Doing Computational Social Science Research? https://goo.gl/6cIga7
• Big Data...
Don’t forget to connect with us
throughout!
@SAGE_News
@CfSocialScience
#esrcfestival
What Can Social Media Tell us About
the
Social World?
Luke Sloan (@DrLukeSloan)
Social Data Science Lab
Cardiff School of ...
My research focuses on Twitter and how social media data can be used to understand social
phenomenon…
• Who Uses Twitter? ...
• Naturally occurring data
• Current and timely
• Temporal and geographical granularity
• Up to 1% of global traffic avail...
• Boston Marathon (anomaly detection)
• US Presidential Election Reaction (gender &
sentiment)
• Ebola Crisis (geography, ...
The Boston Marathon
The Boston Marathon
US Presidential
Election Reaction
US Presidential
Election Reaction
• Twitter data collected via COSMOS Desktop
• Tuesday 20th to Sat 24th Jan 2015
• Condition: contains “ebola”
• 182,517 tw...
Gender Differences
The Ebola Crisis
Male
Network
Female
Network
Geographical Differences
The Ebola Crisis
Intersectionality:
Gender, Geography & Sentiment
(+ive)
The Ebola Crisis
Pink = female tweeters Blue = male tweeters
Pink = female tweeters Blue = male tweeters +ive sentiment = larger
• Twitter tells us something about the public response to Ebola…
• … that is different to what we would normally find out ...
• Who is represented?
• What is sentiment?
• What are we missing?
• How do people use Twitter?
Worry Questions
Burnap, P. and Williams, M. (2015) ‘Cyber Hate Speech on Twitter:
An Application of Machine Classification and Statistical...
Don’t forget to connect with us
throughout!
@SAGE_News
@CfSocialScience
#esrcfestival
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve...
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Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve to meet the challenges of big data?

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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

Published in: Data & Analytics
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Big Data, Social Media Research and Innovations in Research Methods – how will social science research and teaching evolve to meet the challenges of big data?

  1. 1. Big Data, Social Media Research & Innovations in Research Methods A panel debate as part of the 2016 In partnership with &
  2. 2. Introducing our Panel @sfwitherspoon @DrLukeSloan @mrkthmsknndy
  3. 3. Don’t forget to connect with us throughout! @SAGE_News @CfSocialScience #esrcfestival
  4. 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. 5. What kinds of big data are social scientists using?
  6. 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. 7. What challenges face researchers looking to engage in big data?
  8. 8. What challenges face those trying to teach big data?
  9. 9. Emerging Hurdles • Data Access • Ethical use • Skills gap • Software • Interdisciplinary collaborations & research LABs • How and where to publish
  10. 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. 11. Don’t forget to connect with us throughout! @SAGE_News @CfSocialScience #esrcfestival
  12. 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. 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. 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. 15. • Boston Marathon (anomaly detection) • US Presidential Election Reaction (gender & sentiment) • Ebola Crisis (geography, gender & sentiment) • Worry Questions… Three Case Studies
  16. 16. The Boston Marathon
  17. 17. The Boston Marathon
  18. 18. US Presidential Election Reaction
  19. 19. US Presidential Election Reaction
  20. 20. • 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
  21. 21. Gender Differences The Ebola Crisis
  22. 22. Male Network Female Network
  23. 23. Geographical Differences The Ebola Crisis
  24. 24. Intersectionality: Gender, Geography & Sentiment (+ive) The Ebola Crisis
  25. 25. Pink = female tweeters Blue = male tweeters
  26. 26. Pink = female tweeters Blue = male tweeters +ive sentiment = larger
  27. 27. • 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
  28. 28. • Who is represented? • What is sentiment? • What are we missing? • How do people use Twitter? Worry Questions
  29. 29. 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
  30. 30. Don’t forget to connect with us throughout! @SAGE_News @CfSocialScience #esrcfestival

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