steps towards mapping e-research and measuring impact
DESCRIPTION
Steps Towards Mapping e-Research and Measuring Impact. Alex Voss, Rob Procter, Peter Halfpenny, Meik Poschen, Marzieh Asgari-Targhi. AHM’08: Workshop on Profiling e-Research: Mapping Communities and Measuring Impacts Edinburgh, 10 th September 2008. Aims. - PowerPoint PPT PresentationTRANSCRIPT
Steps Towards Mapping e-Research and Measuring Impact
Alex Voss, Rob Procter, Peter Halfpenny, Meik Poschen,
Marzieh Asgari-Targhi
AHM’08: Workshop on Profiling e-Research: Mapping Communities and Measuring ImpactsEdinburgh, 10th September 2008
Aims
To compile a comprehensive* database of e-Social Science activities in the UK and elsewhere
To analyse the data in order to capture snapshot of e-Social Science
To provide a monitoring tool that flags up new content
To provide an infrastructure for further research
Problem
What I would call e-Social Science is not always labeled e-Social Science
Simply googling for the term will provide only a partial view
Need to establish a network of relevant nodes with context information on the web and expand search from there
Approach
Using lists of conference and workshop attendees
Search for relevant URLs Review resulting data Harvest web pages connected to these Extract key terms Visualise results Further steps…
Seed List
Data about attendees of events (Intl. Conference and Agenda Setting)
226 individuals Removal of duplicates and erroneous
entries Import into SQL database
Search
Using Yahoo Search API, generating list of URLs matching name, surname and affiliation
Restricted to .ac.uk, .edu and .nhs.uk and .gov.uk
Results in 30k hits for 226 people Extraction of hostnames from URL
Removing False Positives
Clustering of hostnames by frequency showed some systematic false positives through long lists of names on some sites
e.g., lists of alumni, sports teams etc. Manually removing these for the top 80
hostnames reduced number of URLs by 10k to 20k
Review
Clustering of hostnames by frequency (after cleaning):select count(host) as size, host from url group by host order by size desc;
+------+-------------------------------------+
| size | host |
+------+-------------------------------------+
| 211 | www.geog.leeds.ac.uk |
| 204 | www.nottingham.ac.uk |
| 140 | www.shef.ac.uk |
| 126 | www.ncess.ac.uk |
| 109 | www.manchester.ac.uk |
| 97 | www.lancs.ac.uk |
| 95 | www.psychology.nottingham.ac.uk |
| 93 | redress.lancs.ac.uk |
| 92 | www.cs.bris.ac.uk |
| 91 | www.comlab.ox.ac.uk |
Review (II) Clustering of URLs by number of persons mentioned (after
cleaning): +---------------------------------------------------------------------+ | size | url |
+---------------------------------------------------------------------+ | 24 | http://ess.si.umich.edu/papers.htm | 17 | http://www.ncess.ac.uk/events/ASW/visualisation/
| 17 | http://www.ncess.ac.uk/events/conference/2006/papers/
| 12 | http://ess.si.umich.edu/committee.htm
| 12 | http://redress.lancs.ac.uk/resources/
| 10 | http://www.kato.mvc.mcc.ac.uk/rss-wiki/VizNET
| 10 | http://www.informatics.manchester.ac.uk/aboutus/staff/| | 8 | http://www.ncess.ac.uk/about_us/people/?centre=
| 7 | http://www.geog.leeds.ac.uk/people/a.turner/personal/blog/
Checking Completeness select id from url where url = 'http://ess.si.umich.edu/committee.htm'; > 59765 select surname, name from delegate join delegate_url
on id = delegate_id where url_id = 59765;
This returns a list of 12 people but actual list of conference PC is much longer
Missing people who are in the database but also people missing in the database
Potential to expand list of people involved in e-Social Science
Harvesting Content
Harvesting 20k web pages takes time Using multithreaded code to mask latency Using 40 harvesters still takes about 4h All but 230 pages harvested 1.3GB of data
Amending Seed Data
Extracting email addresses Finding mailto: links actually works quite well Not much need to deal with obfuscation (such as alex.voss-at-
ncess.ac.uk) But doing this may improve results How to deal with multiple valid emails
Extracting affiliations Again, surprising how effective this was but ho Again, how to deal with multiple affiliations Affiliation does not map 1:1 to research area
Key Term Extraction
Using NaCTeM’s Termine (using website at the moment, web service soon)
Rank Term5 e-social science10 national centre11 rob procter12 social science13 marina jirotka14 international conference15 social sciences18 mark rouncefield19 computer science22 research centre27 science studies unit35 lancaster university40 computer supported cooperative work46 text mining48 paul luff
Key Term Extraction (II)
Next steps:Change code to use web services APIRepeat key term extraction for 226 individualsCreate unified key term list Review and create stop-listFactor this into tailored Termine serviceNamed entity recognition to extend seed list
Social Map
Co-occurrence of names on web pages
Further Next Steps
Add weights to social map – how strongly are people connected?
Drawing social network graphs for interactive analysis using information about link structure
Repeating Yahoo searches to flag up new data appearingRSS feed on what’s new in e-Social Science
Doing Yahoo searches on the top key terms emerging
Next Steps?
FOAF – type semantic data on e-Social Science projects
What incentives could we leverage to get people to provide the information we are interested in?
Combining with bibliometric work New kinds of entities:
PublicationsProjects, Organisations