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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
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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
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• Learning new
software/programming skills
• Learning new methods
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
• How and where to publish
Resources to check out• White paper: Who is Doing Computational Social Science Research? https://goo.gl/6cIga7
• Big Data Newsletter – sign up [email protected]
• 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
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 Social SciencesCardiff University
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
• 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
• Boston Marathon (anomaly detection)• US Presidential Election Reaction
(gender & sentiment)• Ebola Crisis (geography, gender &
sentiment)• Worry Questions…
Three Case Studies
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 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
Gender Differences
The Ebola Crisis
Male Networ
k
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 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
• 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 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
Don’t forget to connect with us throughout!
@SAGE_News
@CfSocialScience
#esrcfestival
