(1) The Cardiff Online Social Media Observartory (COSMOS) aims to evaluate the challenges of using social media data in social research through developing tools to harvest, index, and analyze social media data. (2) One objective is to analyze social media data to monitor social cohesion and tension around events. This will examine measures of connectivity and sentiment related to social cohesion. (3) The implications of COSMOS include that it could be a surrogate for traditional methods, an augmentation through "mashing" social media with other data, or lead to a reorientation of methods through networked research and new populations. However, issues around statistical robustness, ethics, and surveillance need consideration.

1. NSMNSS Network Event
Cardiff Online Social Media ObServatory (COSMOS)
Background and Implications for Social Research
Methods
William Housley, Matthew Williams, Adam Edwards, Pete Burnap, Malcolm Williams,
Luke Sloan, Omer Rana & Nic Avis
SOCSI/COMSC Research Network
School of Social Sciences & School of Computer Science and Informatics
Cardiff University

2. Background and Implications of COSMOS
• ‘Coming crisis of empirical sociology’ (Savage and Burrows, 2007)
• Social media and the interactive web (Web2.0) provide potential for systematic
data mining and analysis of naturally occurring data thereby rendering new
populations visible and thinkable
• Development of a ‘social science digital tool kit’

3. Methodological Aim and Objectives of COSMOS
Aim:
• To evaluate the technical, methodological, and ethical challenges
presented by social media data in the context of social sciences.
Objectives:
• To demonstrate how APIs can be utilised within the COSMOS platform
to crawl, harvest, index and visualise qualitative and quantitative
social media data;
• To interrogate social media data using social science criteria for
quality and robustness;
• To examine the ethical and legal issues with harvesting data from
social media sources.

4. Aim and Objectives of COSMOS Demonstrator
Project: Monitoring Social Cohesion and Tension
Aim:
• To analyse social media data for the purposes of monitoring social
cohesion and tension before, during and after major events (e.g.
urban riots; industrial action; political protests/elections; major
sporting events etc.).
Demonstrator Objectives:
• To examine measures of connectivity in the analysis of social media
data in relation to indices of social cohesion and tension;
• To examine sentiment about social cohesion and tension in social
media data and their correlation with events;
• To examine the feasibility of ‘mashing’ official curated data sources
with social media data.

5. Cardiff Online Social Media ObServatory
Consi der pr ocess
shar i ng – si m l ar t o
i

6. Implications of COSMOS for Social Research
1. Is it a surrogate for conventional, terrestrial, social research methods?
E.g. as an alternative to household surveys, ethnographies, focus groups,
interviews, content analysis
2. Is it an augmentation of conventional, terrestrial, social research methods?
E.g. a platform for distributed, digital, ethnography; ‘mashing’ social media
communications with curated datasets (such as neighbourhood crime statistics)
3. Is it a re-orientation of social research methods?
E.g. the emergence of the networked researcher integrating multiple digital data
strands in different locations; recognition of new populations (‘object-oriented
sociality’, Knorr-Cetina, Latour)
4. Ethical implications?
E.g. surveillance and public sociology (panoptic and synoptic power)

7. Implications of COSMOS for Social Research
(1.) Is it a surrogate for conventional, terrestrial, social research
methods? E.g. as an alternative to household surveys,
ethnographies, focus groups, interviews, content analysis
– Low fidelity – relatively impoverished, for example demographic data
– Inert – passive data, not elicited though face to face interaction
– Noise – problem of identifying relevant information when treating social
media traffic as data
– Is the analysis of social media data statistically robust ? What does
inference mean in relation to research questions and sample in the
context of social media traffic

8. Implications of COSMOS for Social Research
(2.) Is it an augmentation of conventional, terrestrial, social research methods?
E.g. a platform for distributed, digital, ethnography, networked team coding of
large data sets, crowd sourcing
‘Mashing’ social media communications with curated datasets (such as
neighbourhood crime statistics)
Web 2.0 can provide a means of ‘reaching hard to reach groups’ – e.g. social media
can augment poor survey responses and reach marginalised communities

9. Implications of COSMOS for Social Research
(3.) Is it a re-orientation of social research methods? E.g. the emergence of the
networked researcher integrating multiple digital data strands in different locations;
recognition of new populations (‘object-oriented sociality’, Knorr-Cetina, Latour)
– New ways of understanding agency and social organisation using social media as a
case example (e.g. Marc Hughes, NodeXL) – investigation and visualisation of
classic questions in real time. Possibility of identifying new social categories,
connections and relations
– Modelling and visualization – re-orientates interpretation of data
– Naturally occurring mediated data – ‘pulse of the world’
– Real/Useful Time Analysis – Archiving – Event Analysis – Anomaly Detection - from
punctuated to locomotive data collection and analysis
– Convergence between computing and social science – networked researcher
– New digital tools – platforms and observatories – signature science or predictive
science – collaborative and open source

10. Ethical Considerations
(4.) Ethics and the politics of data
– Panoptic surveillance
– Feedback consequences, auto-poesis and self-referentiality, closed systems
– (Double hermeneutic)
BUT
Synoptic surveillance and public sociology; need to develop digital tools and social
analysis within contemporary contours of emerging digital society for public good not
private gain; open source.

11. Sentiment and Signature Science ?

12. Sentiment and Signature Science ?

13. Mashing Social Media and Neighbourhood
Crime Data?

14. SOCSI/COMSC Research Network
School of Social Sciences & School of
Computer Science and Informatics
Cardiff University
http://www.cardiff.ac.uk/socsi/researc
h/researchgroups/comsc-
socsi/index.html