Exploring the Structure of Government on the Web

Transcription

1 Exploring the Structure of Government on the Web Presentation at Smart Sensing & Big Data Analytics: Governing through Information Symposium 3-4 March 2014, Australian National University Robert Ackland Australian Demographic & Social Research Institute, Australian National University Homepage: Project:

2 VOSON Project at the ANU ( Teaching, research and tool development in areas of computational social science, network science, web science 2

3 VOSON project formally commenced in 2005, but research dates back to 2002 Australian Research Council grants: DP "New Methods for Researching the Existence and Impact of Political Networks on the WWW" Ackland and Gibson SR "Virtual Observatory for the Study of Online Networks (VOSON)" - Ackland, Gibson, O'Neil, Buchhorn, Bimber, Ward 2005 LP "The role of online social networks in successful ageing: benefitting from 'who you know' at older ages" - Booth, Ackland, Windsor DP "The institutional structure of e-government: a cross-policy, cross-country comparison" Henman, Ackland, Margetts DP Understanding online attention and user-generated content creation: An information consumption and production perspective - Ackland

4 Background Government use of the Internet has rapidly evolved. While this evolution has been examined in terms of the content, usability and interactivity of sites, the institutional structure of government on the web is less explored. Australian Research Council-funded project titled "The institutional structure of e-government: a cross-policy, cross-country comparison": CIs: Paul Henman (UQ), Robert Ackland (ANU), Helen Margetts (Oxford) Research associates: Tim Graham (UQ), Lin Chen (ANU) 4

5 Overall aims of project Aim 1: Assess whether government hyperlink networks reflect offline institutional structures Is e-government facilitating joined-up government or are jurisdictional boundaries still a significant barrier? Whalen (2011) studied the hyperlink structure of the US.gov domain, assessing correspondence between online structure of US government and its offline hierarchy. Major difference is our project compares the UK and Australia, identifying both similarities and contrasts in the relationship between institutional structure and online presence. 5

6 Aim 2: Use hyperlink data to assess nodality of government (Hood & Margetts 2007) is government at centre of informational networks? Nodality affects whether government messages received by the population. Web might increase government nodality, but can also decrease nodality, through increased competition from other information providers (who may destabilise/confuse/subvert the messages and actions of government). Example: anti-vaccination lobby groups. We ask: is government using the web to enhance its visibility? Are there differences in nodality across policy domains, countries (AU and UK)? Our approach is different to that used by Escher et al. (2006) Escher et al. focused only on the UK Foreign Office (and US and Australian counterparts), our analysis includes other sectors of government, allowing cross-country and cross-sector comparisons We collect more hyperlink data, allowing us to identify the connection between sites that link to (or are linked to by) government sites. We can construction of nodality measures that are different to those used by Escher et al. (e.g. those requiring complete network data). 6

7 Webometrics (link count analysis) focus on egonetworks, rather than complete networks typically only know attributes of ego, not alters 7

8 Today some methodological aspects Hyperlink network data collection (VOSON) Network reduction techniques Community structure in government hyperlink networks Coding websites (machine learning) 8

9 Hyperlink network data collection (VOSON) 9

10 Manually identified AU and UK government seed pages (typically, entry pages to government websites): AU 88 pages UK 92 pages Used the VOSON software ( to construct hyperlink network data using two stage approach: Stage 1: Stage 2: VOSON in-built crawler crawled the seed sites finding internal pages linked to from the entry page. Collected outbound links from each of the internal pages and also text content Bing API was used to find all inbound links to each of the internal pages (including seed page) Every new page discovered above (i.e. pages that either link to or are linked to by government web page) was then crawled by VOSON in-built crawler to find connections among these pages Data collected in

11 11

12 VOSON 2.0 web interface works with Firefox, Chrome, Safari, ipad VOSON+NodeXL allows construction and import of hyperlink networks from within NodeXL 12

13 Network reduction techniques 13

14 Network size (pages): AU: 1,517,020 nodes (pages) UK: 1,588,757 nodes (pages) First major network reduction technique: construct network of websites rather than pages VOSON has approach for automatically grouping pages into pagegroups e.g for AU, 6694 pages from Australian Taxation office all included in a single node ato.gov.au Full network size (pagegroups/sites): AU: nodes (pages), edges UK: nodes (pages), edges 14

15 Gephi map UK network only showing 30K+ nodes with indegree+outdegree>1...not much analytical potential from this visualisation... 15

16 In future work we will be investigating approaches for removing edges to reveal the backbone of UK and AU government hyperlink networks e.g. Serrano, M., Boguñá, M. and A. Vespignani (2009): Extracting the multiscale backbone of complex weighted networks, PNAS, 106(16),

17 Community structure in government hyperlink networks 17

18 Some approaches for 'community' detection in networks Modularity maximisation (Lancichinetti & Fortunato, 2012) Edge-Betweenness (Girvan & Newman, 2001) Fast-Greedy (Clauset et al, 2004) Multi-Level (Blondel et al, 2008) Walktrap (Pons & Latapy, 2005) Infomap (Rosvall, Axelsson & Bergstrom, 2009) 18

19 The hyperlink networks we have collected are both directed and weighted (weight on edge from node i to j are number of pages with links from site i to j) Of the above, only Edge-Betweenness and Infomap support directed and weighted graphs 19

20 Edge-Betweenness We found the Edge-Betweenness algorithm (as implemented in igraph/r) does not scale well. In a test run with UK hyperlink network, algorithm did not converge after 24 hours running... 20

21 Infomap See: Scales well for large, dense networks information theoretic approach - appropriate to this network, where there is flow of information and attention If site i links to site j can think of a flow of information from j to i and a flow of attention from i to j. We do not have data on flow of web users from site i to site j i.e. 'clickstream data' We therefore make assumption that the number of pages on site i that contain hyperlinks to site j (these are our edge weights) is proportional to the flow of attention/information 21

22 First attempt... Tried Infomap implemented in R/iGraph (v ) Results: Not good! Algorithm consistently generated a single massive community (approx. 95% of nodes) and thousands of tiny communities (1 or 2 nodes per community) Results do not pass sanity test (i.e. face validity) The problem: Many nodes in the UK network have no outlinks Therefore, effect of teleportation in the Infomap algorithm is significant (it randomly connects nodes) This problem was solved in Lambiotte and Rosvall (2012) 22

23 Second attempt... Results from Lambiotte and Rosvall (2012) were recently developed into Infomap algorithm This latest code is not yet integrated in R/iGraph So, next steps: Download and compile C++ source code for Infomap (v ) Run the standalone Infomap algorithm Using Infomap Map Generator, can examine the community structure of UK network at different scales (varying the number of communities displayed and number of links between communities) 23

24 17 out of 4571 communities (44% of all flow) 24

25 45 out of 4571 communities (70% of all flow) 25

26 Each community is named after the website that has the highest flow and PageRank in that particular community (i.e. the top dog website) Distribution of flow across network follows a power law There are many communities, but a very small percentage hog all the flow across the network Top 5% of communities (229 nodes out of 4571) account for about 86% of all flow in the network Infomap uses an implementation of the PageRank algorithm to calculate importance of each community (aggregate PageRank of all websites in that community) 26

27 Preliminary findings Extremely influential communities form around social media and blogging platforms A massive amount of flow is directed through the Twitter community (e.g. from Twitter to Many UK seed sites form influential communities (i.e. Top 20), but not all. Somewhat unexpectedly, two UK Gov business websites each form highly influential communities (community rank #4, 0.048% of all flow throughout network) (community rank #8, 0.025% of all flow throughout network) 27

28 Coding websites 28

29 To understand the structure of government hyperlink networks, we need to know something about the websites in these networks Generic top-level domains (.edu,.com, org etc.) will only give very coarsegrained information on who these sites are What policy domain are they in? (health, education, social security?) This is social science research so we need more information on nodes Options: 1. Manually code every site (not feasible, as we have >100K sites) 2. Manually code a subset of sites e.g. the most important sites based on centrality measure (scientifically valid?) 3. Manually code a sample of sites (e.g. adaptive sampling). To be explored in future Manually code training dataset and then use machine learning to predict website type The following is summary of preliminary work on approach

30 Data collection Subset of 'important' websites in the UK network were coded into discrete policy domains by a human coder Subset chosen as seed sites plus sites connected to two or more seed sites e.g. coding: Community services, Health, Foreign Affairs Need to collect and clean the HTML data from websites in the network While the original VOSON crawl collected text content for all websites crawled, for this proof of concept, we re-collected the text content (in future we will use the VOSON-collected text data)

31 Text processing R XML package used to clean the HTML (strip HTML tags, remove white spaces, remove strange ASCII characters, convert to lowercase, extract key word frequencies) 2157 websites were usable (i.e. with clean web text and a known policy domain) Machine Learning using the RTextTools package in R (supervised learning for text classification)

32 Support Vector Machine (SVM) Websites with known policy codes = 2157 SVM training sample = 2000 SVM test sample = 157 Some example results of classification: PRECISION RECALL F-SCORE Education Employment Environment Foreign Affairs Health Housing

33 SVM Conclusion Surprising level of accuracy Future work will involve: More data (will use HTML collected via VOSON) Investigate different machine learning algorithms