How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations
Applied Network Science volume 7, Article number: 36 (2022)
Social network analysis (SNA) tools and concepts are essential for addressing many environmental management and sustainability issues. One method to gather SNA data is to scrape them from environmental organizations’ websites. Web-based research can provide important opportunities to understand environmental governance and policy networks while potentially reducing costs and time when compared to traditional survey and interview methods. A key parameter is ‘search depth,’ i.e., how many connected pages within a website to search for information. Existing research uses a variety of depths and no best practices exist, undermining research quality and case study comparability. We therefore analyze how search depth affects SNA data collection among environmental organizations, if results vary when organizations have different objectives, and how search depth affects social network structure. We find that scraping to a depth of three captures the majority of relevant network data regardless of an organization’s focus. Stakeholder identification (i.e., who is in the network) may require less scraping, but this might under-represent network structure (i.e., who is connected). We also discuss how scraping web-pages of local programs of larger organizations may lead to uncertain results and how our work can combine with mixed methods approaches.
Understanding how and why different groups are connected is critical for addressing many of society’s most challenging sustainability problems, which often involve coordination and cooperation among different places and management sectors (Bodin 2017; Clark and Harley 2020; DeFries and Nagendra 2017; Sayles et al. 2019). Examples include non-point source pollution and emission reductions (DeFries and Nagendra 2017) and coordinating among jurisdictions that fragment interconnected land-and-sea-scapes (Crowder et al. 2006; Pittman and Armitage 2017). In response, academics and practitioners increasingly look at environmental governance, stakeholder, and management networks, often using social network analysis (SNA) tools and conceptsFootnote 1 (Bodin and Crona 2009; Bodin and Prell 2011; Bodin 2017; Bodin et al. 2019; Sayles et al. 2019; Groce et al. 2019; Kluger et al. 2020).
Most SNA research focusing on the environment relies on traditional social science methods, such as surveys and interviews, to collect primary data about network relationships (Bodin and Prell 2011). Recent scholarship, however, has used online information to understand these networks and is motivated by both an interest in understanding online interactions as a phenomenon of study and for their methodological potential to provide faster and cheaper approaches to collect data (Park 2003; Park and Thelwall 2006; Kreakie et al. 2016; Hayes and Scott 2018).Footnote 2 Organizations can have an online presence through social media and websites, two different online sources that likely represent different kinds of network relationships (Hayes and Scott 2018). When deriving network relationships from organizations’ websites, information is usually extracted from descriptions in the body of the text, and can include information about roles and functions, or from hyperlinks to another organization’s page.Footnote 3
Previous work on hyperlink networks has established some important observations about how hyperlink data might be used. Several SNA studies have compared results from data collected using online approaches to those using traditional survey approaches. They found that the two approaches resulted in similar results when analyzing small, core networks of environmental organizations, e.g., 25–60 groups (Morgans et al. 2017; Yi and Scholz, 2016). With larger networks (e.g., > 100), however, there tends to be much less similarity (Hayes and Scott 2018; Morgans et al. 2017; Yi and Scholz, 2016). Online data are likely not replacements for survey data, though this may be context specific; however, online data can be a valuable supplement or scoping tool. For example, Hayes and Scott (2018) found that structural patterns from online networks could be used to calibrate simulations to “fill in the gaps” of missing survey data, a classic research problem that can significantly undermine network analysis because it is very vulnerable to missing data (Costenbader and Valente 2003). Additionally, while online and survey derived data can result in different patterns of connection, several studies shows that there can be less variability about who simply is in the network (Kreakie et al. 2016; Morgans et al. 2017). This led Kreakie et al. (2016) to propose using online data as a tool to help identify stakeholders for collaborative environmental governance.
A key question when gathering data from websites is how deep to search. A website consists of any number of internally linked web-pages, collated under a unique Uniform Resource Locator (URL). The term ‘search depth’ refers to the minimum number of clicks that a user would navigate through to go from a website’s homepage to a sub-page within that website. Intuitively, searching more pages has the potential to uncover more network information, with a possible tradeoff of time and energy spent searching (whether it be computer automated or manually) versus the value of information returned for the purpose at hand. Among studies using automated web-crawlers and scrapers (terminology used synonymously hereFootnote 4), approaches have included searching an entire website (Ackland and O’Neil 2011), searching to depths two or three (Hayes and Scott 2018; Yi and Scholz 2016), searching a site’s home page (Elgin 2015), or the home page plus purposefully selected sub-pages titled “partners” and “links” found on the home page (Kreakie et al. 2016). Depth parameters for research using manual approaches to derive network data from website are rarely reported, though often cited as being systematic (Hileman and Lubell 2018; Hileman et al. 2018; Morgans et al. 2017; Berardo et al. 2019). In general, there has been surprisingly little discussion in the literature about best practices and tradeoffs associated with search depth, with a variety of depths used, often without explanation. A detailed assessment about how search depth affects SNA data collection and analysis would thus be useful for several reasons.
First, it is necessary to understanding how search depth affects network data collection and analysis to ensure that research results are robust and valid. Second, understanding the effects of search depth can enhance case study synthesis by specifying how different depth studies should be compared. It also highlights the need to have transparent and justified search depths reported in research papers. Third, understanding search depth has practical implications for stakeholders and practitioners looking to use web-based network tools. While it is true that gathering data online is often faster and cheaper than traditional survey or interview methods and can facilitate multiple time series of data collection (Kreakie et al. 2016; Hayes and Scott 2018); even with computer assistance, web-scraping can still take hours to days depending on the data set (Issuecrawler 2021). Given that many environmental practitioners find their time and resources stretched thin (Sayles and Baggio 2017a; Sayles 2018), improved guidance on how deep to search and potential tradeoffs is essential information for using web-scraping tools.
In this paper, we address the issue of search depth by analyzing hyperlink data scraped from the web-pages of 78 environmental stewardship organizations. We focus on hyperlink network data collection using an automated computer web-scraping tool, though our work is generalizable to manual search approaches as well. Specifically, we quantitatively assess how network structure changes with increased search depth by considering several metrics commonly used in SNA to describe network structure and function (Table 1; Bodin et al. 2006; Carlsson and Sandström 2008). We also analyze the depth at which specific sub-pages describing likely partnership or other inter-organizational relationships occur, to provide guidance on gathering online network data based on web-page names. Finally, we assess how the quantity of hyperlink returns varies by search depth and if there is any difference among stewardship groups with different primary foci to understand if our results are contextual to a specific kind of stewardship group or activity.
Organizational website selection
We used data from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a relatively recent, publicly available data set about environmental stewardship organizations working in New York City, USA (n = 719). Since hyperlink web-scraping can be computationally slow (Issuecrawler 2021), we took a geographically bounded subset of the data for analysis selecting organizations that worked entirely within or overlapped the NYC borough of Staten Island (n = 111). The STEW-MAP data included organizations’ websites, which we verified resulted in 86 working websites; however, eight sites could not be scraped, which we removed from our final sample (n = 78, Fig. 1). See Additional file 1 for details.
We used the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020) to collect hyperlink network data. The snaWeb package is a web-scraper with a set of functions to retrieve URLs from specified websites and build hyperlink networks. snaWeb scrapes sites to any specified depth, checks the status of site URLs (e.g., URL status code 200 vs. 404 or other errors), and returns a redirected URL if one exists. The ability to find redirects is an important behavior for network studies, as two sites with hyperlinks to a common third site will be connected to this third site even if one site uses an outdated URL, which is a frequent issue on the web (Dellavalle et al. 2003; Duda and Camp 2008; Hennessey and Ge 2013; Jones et al. 2016; Hondula 2020).
We scraped the 78 websites between 09 and 17 June 2020 to a maximum search depth of ten, expecting most, if not all sites, to have a maximum depth below ten (see Additional file 1 for additional specification). Scraping returned 46,366 URLs; one third (34.56%) were external links to other sites; two-thirds (65.41%) were internal (i.e., they had the same root as the searched site). Most URLs (91.33%) were classified as valid returns, meaning they responded successfully when accessed (URL status code 200). While the number of URL returns differed among these categories, there was no difference in qualitative patterns or statistical comparisons (see Additional file 1). We therefore focus on valid external returns in the main text when analyzing network structure and group comparisons, as valid external returns are most likely to be potential network relationships. We then use valid internal sub-page returns for keyword analysis because these sub-pages would list an organization’s collaborators (Fig. 1).
To fully understand what information is returned from the web-scraper, it is important to understand how it responds to long URLs. Many large environmental organizations, such as government agencies or large non-profits, consist of sub-programs that in many ways, function more like independent programs than a single entity (Sayles and Baggio 2017a; Sayles 2018; Newig et al. 2010). For the purpose of understanding environmental governance systems, it often makes sense to treat these sub-programs as different groups. For example, when looking at stakeholders in the Northeastern United States, it is logical to include the U.S. Environmental Protection Agency (EPA) Region One, which works in the region, but not EPA Region Ten, which operates on the other side of the continent. Both regions, however, have the same root URL (www.epa.gov). snaWeb uses the full URL that is entered for the search (e.g., www.epa.gov/aboutepa/epa-region-1-new-england) as the search base. Sub-pages of this base are classified as internal sub-pages and scraped. Pages at the same level or higher (e.g., www.epa.gov/aboutepa/epa-region-10-pacific-northwest, or simply www.epa.gov) are classified as family pages having the same root, so technically internal, but not sub-pages, and are not scraped. This search behavior attempts to more accurately represent the structure and reality of networked environmental governance. Eight organizations in our Staten Island data self-identified by sub-pages (I.e., they listed sub-pages when replying to the STEW-MAP survey).
Accuracy and stability assessments
We ran several stability and accuracy assessments to ensure our data’s validity (Fig. 1). To test accuracy, we compared hyperlink returns at depth one from the snaWeb package to manual inspections of the HTML source code for 19 sites (ten randomly selected and nine purposefully selected; 24.36% of our sample). The data generated with snaWeb had near 100% accuracy (see Additional file 1 for details).
To test for stability, we repeatedly scraped 26 sites (20 randomly and six purposefully selected; 33.33% of our sample) three times to see if there were fluctuations in the search depth and number of URL returns. The maximum search depth achieved per site was consistent, with zero percent variability across all three test runs. The total number of returned URLs was also stable (mean and median variability of 1.48% and 0.00%, respectively), with some variance attributed to slow-loading or unresponsive internal sub-pages that would be scraped when they did respond to the HTTP call of snaWeb, but not when they were unresponsive. Overall, snaWeb produced accurate and stable results in what is itself a highly dynamic and variable environment of the world wide web. (See discussion of potential limitations in the Additional file 1.)
Hyperlink data preparation and analysis
The analysis of hyperlink network data almost always involves a significant level of data cleaning, re-coding, and consolidation (Ackland 2010; Elgin 2015). We reduced the total hyperlink data (n = 46,366 URLs, Fig. 1) to root URLs, as has been done elsewhere (Elgin 2015). It would not make sense, for example, for one of our search links to have multiple network connections to an external site’s “home,” “about,” and “partners” pages. We further removed links to social media, which show different but complementary information from hyperlink networks (Hayes and Scott 2018), and removed links to images, file storage, web-services, audio files, and any other content that did not represent an organization, as well as news media, which illustrates information distribution, but not necessarily inter-organizational connections.
Following Kreakie et al. (2016), we manually checked all URLs and consolidated them when two different root URLs represented the same organization (Fig. 1). For example, an organization might have a dedicated website, with a unique URL, to communicate its environmental management plan. We did not further modify root URLs from potentially related units, such as two academic units within a university (e.g., www.gc.cuny.edu and www.guttman.cuny.edu). Such consolidations should be guided by case specific information and the research questions at hand (Elgin 2015). Using the root URLs as reported (with the aforementioned cleaning) is suitable for testing network structure against search depth since our questions and analyses are largely methodological; however, other tests might warrant further refinement of URLs (Elgin 2015).
Once cleaned, all edges were assigned a value corresponding to their search depth. We removed all duplicate edges, retaining only the first instance and depth value. This yielded a network of 2233 nodes (i.e., URLs), which we further reduced, by removing all nodes with a total degree of one, to form a core network of 267 nodes (which included five isolates). We then unweighted the network and calculated metrics (Table 1) for each cumulative search depth, where returns for a given depth include those before it (Fig. 1), using the R packages intergraph, sna, network and igraph (Bojanowski 2015; Butts 2008, 2020a, b; Csardi and Nepusz 2006).
Key word analysis and group comparisons
To identify the depths of specific sub-pages describing likely partnership or other inter-organizational relationships, we performed key word searches (listed in the results) for the first occurrence by depth in valid internal URLs (Fig. 1). To test for differences in total returns and maximum search depth based on stewardship foci we separated the STEW-MAP sample into two groups based on organizations’ stated primary stewardship objectives in the STEW-MAP database: first, organizations focused on communication-based activities, i.e., education and advocacy (EA, n = 40); second, groups doing ‘on-the-ground’ or management activities, i.e., conservation, management, monitoring, participating and partnering in stewardship activities, and transforming the environment (CMMPPT, n = 35, Fig. 1). Groups were compared using Mann–Whitney U tests in the R computation language (R Core Team 2020). Three groups were coded as unknown (two did not provide information, a third listed “none of the above”) and were not statistically compared due to the small sample.
The reduced root URL network grew rapidly from depth one to two and largely stabilized by depth three. There was little to no variability in calculated network metrics beyond depth three (Figs. 2, 3). Interestingly, while there was only a 2.70% increase in the number of nodes from depths two to three, there was a 30.07% increase in the number of edges (Fig. 3A, Additional file 1: Table S3). While network size (i.e., the nodes or URLs in the network) changed very little from depths two to three, the structure of who was connected changed dramatically. It is worth noting, however, that the non-reduced network (n = 2,333) did not show this pattern at depths two to three; nodes and edges were added at equivalent rates (22.77% and 27.12% respectively; Additional file 1: Table S3).
The first occurrences of within-URL key words meant to signifying inter-organizational relationship also occurred within the first three depths (98.4% of returns) and were concentrated around depths one and two (Table 2). Three key words, however, were only found at depths two or higher: “funding,” “donors,” and “team-members.”
The raw scrape of the data also returned the majority of URLs by depth three or four (Fig. 4A), by which point, most sites reached their maximum depth (Fig. 4B). Even for 12 sites (15%) that reached the maximum search depth of ten (Fig. 4B), these higher search depths accounted for a very small percentage of their total URL returns (Fig. 4A) and there was little reward for the extra time needed to searching deeper.
Finally, the average number of valid external returns and maximum search depth were not statistically different between the AM and CMMPT groups (Mann–Whitney U test, p = 0.375 and 0.908, respectively, Fig. 4C, D); and all other comparisons were similar across the two groups (Fig. 4A, B).
A social network perspective is fundamental for addressing many environmental management and sustainability problems (Bodin 2017; Clark and Harley 2020; Sayles et al. 2019). Studying online presences of environmental organizations holds great potential to better understand environmental governance and policy (Hayes and Scott 2018; Yi and Scholz, 2016), as well as build tools to help environmental stakeholders and managers (Kreakie et al. 2016). Working with online network data is relatively new; we are still learning about best practices (Park and Thelwall 2006; Hayes and Scott 2018). This paper provides insight on a key variable for online research: how deep to search websites and whether this varies among organizations with different foci.
Based on our results, searching sites to depth three seems to capture all relevant network data. This does not vary among environmental stewardship organizations focused on communication versus on the ground management. Several of our tests suggest that simply searching to depth two could be appropriate in some cases. The majority of internal pages with possible relationships, based on key word search, occur within the first two depths; and the number of nodes (i.e., who was in the network) changes very little beyond depth two for the reduced focal network (n = 267). If simply scraping websites to identify major stakeholder groups, little information would be lost, in our case, by only searching to depth two. This could save environmental practitioners, some of whom lack time and resources (Sayles 2018; Sayles and Baggio 2017a), considerable computation time, data cleaning, and interpretation (Ackland 2010; Elgin 2015). However, scraping only to depth two would miss critical information about network structure (i.e., who is connected), as the number of edges did not stabilize until depth three, illustrating the importance of searching at this higher depth if one wants to analyze network patterns. Furthermore, for the full, unreduced network, many nodes were still added at depth three. While these are peripheral nodes within our network, they could be informative for certain investigations, such as identifying potentially marginalized groups. For structural analysis, searching to depth three, or maybe even four to be extra conservative, is likely best, unless other data justify something else. In the least, all search depth decisions should be clearly documented and reported in publications to improve cross-study comparisons and interpretations as search depth influences network structure.
While our case study clearly shows a sweet spot around depth three, regardless of an organization’s focus, several limitations are worth noting. First, our results need to be replicated for other locations and environmental issues beyond stewardship in order to build a stronger evidence base and set of guiding principles for online network data collections. Second, the sites in our sample predominantly represent non-profit and citizen organizations. Only two organizations represented other sectors: a sub-program within the NYC Department of Parks and Recreation and an academic unit within the City College of New York. It is possible that different organization types, such as state or federal government, may have different hyperlink patterns on their websites. While further testing is needed, we suspect that any differences among websites are more likely to be case specific as opposed to categorical. Nonetheless, our empirical results may be limited to non-profit and citizen groups. Third, results from the key word search reflect sites that use a description of the page in the URL, e.g., “our partners.” Analyzing cases where the page URL is not descriptive text, but rather a numeric ID or something else, could lead to different results; however, we are unaware of any theoretical reason why websites organized using numerical IDs in the URL would be different than those using text descriptions. These possible limitations noted, our results provide initial and important guidance on scraping websites to study online environmental organization networks.
In addition to our empirical results, working with the snaWeb tool revealed several interesting methodological issues relevant to the use and development of hyperlink network web scraping tools. For example, navigating the fuzzy boundaries that some organizations and sub-programs can exhibit is a known challenge when defining what a node represents in SNA studies about the environment (Sayles and Baggio 2017a; Sayles 2018; Newig et al. 2010). This problem may become more complex, however, when working with hyperlink networks as the existing fuzziness around defining proper units of analysis (i.e., what a node should represent) is compounded with website structure and all the choices that went into designing it. Several organizations in our sample self-identified by a sub-page of the root URL. When conducting research at a local scale, such as environmental stewardship activities in the Staten Island Borough of NYC, reducing the scrape of large Federal, State, and NGO websites to local programs or chapters makes a lot of sense (and indeed has been done elsewhere, e.g., Ackland and O’Neil (2011)). Yet, our own observations suggest, albeit anecdotally, that these sub-pages may not always match the actual organizational units engaged in collaborative environmental actions, which the network is meant to model. For example, some sub-pages had very few sub-pages of their own (“sub-sub-pages” if you will). These pages sometimes linked to what seemed like relevant internal organizational content stored elsewhere in the parent organization’s website. Any such content would not be scraped by the snaWeb package, or other similarly programmed web-scraper, because it is designed to only scrape sub-pages of the input search URL, including when that search URL is itself a sub-page of a larger website. These observations suggest that we need to better understand the relationships between sub-page scrape results and how these relate to the environmental management activities of sub-programs and chapters of larger organizations. Practitioners identifying stakeholders through web-scraping may want to be extra cautious when using sub-pages and integrate data from multiple sources and methods to ensure that they have not missed or over-represented specific stakeholders or sectors.
In general, mixed methods approaches may be fruitful for online network data gathering. Our research used an automated web scraping approach to gather hyperlinks, which is particularly attractive for its potential to gather data quickly, at low costs, and at multiple time series (Kreakie et al. 2016; Hayes and Scott 2018). This speed and potential to automate large datasets likely comes at the cost of more nuanced understandings about why relationships exist.Footnote 5 In response, some researchers are employing qualitative manual coding of websites to be able to better discern what constitutes a network edge. For example, Hileman et al. (2018: 5) argue that for their study, “simply having a hyperlink or being mentioned on [a] website does not constitute a [network relationship]; partners [must] be clearly designated as collaborators on shared projects or other work activities.” Manual coding could make use of automated scraping to generate website attribute data, including the number of internal pages, external links to other sites, page names (which are extracted by the snaWeb package), URL key words, and other relevant information to guide more structured manual coding. Benefits might include more reproducible methods and enhanced case study comparisons. Such an approach is just one illustration about how mixed methods could be used. Future work should continue to build upon these methodological ideas.
Finally, organizations without a website cannot be documented by hyperlink web-scraping as there is no site to link. While not necessarily a limitation of our research, as our work focuses solely on the distribution of the hyperlinks that are present in a website, web-presence is a limitation of hyperlink web-scraping in general if the goal is to document and analyze environmental governance networks. Here again, the aforementioned mixed methods approaches can help. Manual coding, text mining, and machine learning might record organization names within website text that lack hyperlinks. We also observed in our work, however, that some organizations listed partners, funder, or other relations using images of logos without any hyperlinks. Manual coding and analysis may be needed in such cases.
Network science tools and concepts are essential for addressing many environmental management and sustainability issues. Online network data provide important opportunities to understand environmental governance and policy networks, with potential cost and time savings compared to traditional research methods. Understanding how deep to search websites is important for building a scientific evidence base through comparable case studies and for developing efficient tools that can help stakeholders. Results from our analysis of the Staten Island, NYC data show that searching to depth three captures the majority of relevant network data and that organizations’ foci do not affect this. While searching to depth two may be sufficient for identifying key actors, it misses important structural information about who is connected. Future work should replicate our study for different places, environmental issues, and group types. We also need to better understand how to work with sub-programs of State and Federal agencies and NGOs. Researchers should also continue exploring creative methodological approaches such as combining automated methods to gather website metadata to inform systematic manual coding to better understand what links mean.
Availability of data and materials
All analysis was done in the open source R computational language and relevant packages are cited in the main text. The data are publicly available at the U.S. Environmental Protection Agency’s (EPA) Science Hub repository (catalog.data.gov/dataset/epa-sciencehub) https://doi.org/10.23719/1522542. The datasets used and/or analyzed during the current study are also available from the corresponding author on reasonable request.
Analyzing social network patterns can provide key diagnostics about how to enhance collaborative governance (e.g., Sayles and Baggio 2017a; Vance-Borland and Holley 2011), analyze issues such as power asymmetries or information flow (e.g., Bixler et al. 2016; Cohen et al. 2012), or test theories about building trust (e.g., Berardo and Scholz 2010; Lubell et al. 2014) and effective collaboration (e.g., Bodin et al. 2014; McAllister et al. 2017) to solve environmental problems. Network approaches to studying environmental management and sustainability are not limited to relationships among social actors. Network analysis can be used to understand more complex sets of relationships among organizations and policy forums (Lubell et al. 2014), organizations and environmental issues (Bergsten et al. 2019), and various kinds of social actors and the environmental resources that they interact with (Sayles et al. 2019; Kluger et al. 2020). These latter two examples are types of social-ecological networks (SENs; Sayles et al. 2019). There is, however, a core and foundational body of scholarship focusing on relationship among individuals or organizations involved in environmental governance, policy, and resource use (Bodin and Prell 2011).
The majority of scholarship using online data collection for environmental research focuses on social networks, where the network consists of organizations represented by their websites (Park 2003; Hayes and Scott 2018). Websites and social media, however, likely contain ample information about the places, issues, resources, and other policy and environmental information that could be used to analyze the more complex policy forum networks and SENs described in footnote one. (Indeed, such information has been gathered from websites to complement surveys and interviews for the analysis of a SEN (e.g., Sayles and Baggio 2017b)). Furthering our understanding about how best to use online information to study policy forum networks and SENs is likely a promising area for future research as traditional approaches to data collection remain a key hurdle to amassing empirical studies to support a larger evidence base (Sayles et al. 2019).
Websites might also be used to represent individuals, such as the home or bio-pages for prominent environmental thinkers, activists, or decision makers, but work to date has focused on organizations.
Web-crawlers are programs that automatically browse the internet and access pages by following hyperlinks. Scrapers are programs that extract specified content from web-pages. These processes can be done separately or simultaneously. When a web-crawler also extracts data during the crawl, it can be called a web-scraper (Khalil and Fakir 2017).
Machine learning and text mining may alleviate tradeoffs in depth vs. breadth. Further research on the effects of search depth on computation time and computer memory might also help understand and address technological limitations.
Conservation, management, monitoring, participating and partnering in stewardship activities, and transforming the environment grouping
Education and advocacy grouping
United States Environmental Protection Agency
New York City
Social network analysis
Stewardship Mapping and Assessment Project
Uniform Resource Locator
Ackland R (2010) WWW hyperlink networks. In: Hansen D, Shneiderman B, Smith M (eds) Analyzing social media networks with NodeXL: insights from a connected world. Morgan-Kaufmann
Ackland R, O’Neil M (2011) Online collective identity: the case of the environmental movement. Soc Netw 33(3):177–190. https://doi.org/10.1016/j.socnet.2011.03.001
Berardo R, Scholz JT (2010) Self-organizing policy networks: risk, partner selection, and cooperation in estuaries. Am J Polit Sci 54(3):632–649. https://doi.org/10.1111/j.1540-5907.2010.00451.x
Berardo R, Turner VK, Rice S (2019) Systemic coordination and the problem of seasonal harmful algal blooms in Lake Erie. Ecol Soc. https://doi.org/10.5751/ES-11046-240324
Bergsten A, Jiren TS, Leventon J, Dorresteijn I, Schultner J, Fischer J (2019) Identifying governance gaps among interlinked sustainability challenges. Environ Sci Policy 91:27–38. https://doi.org/10.1016/j.envsci.2018.10.007
Bixler RP, Wald DM, Ogden LA, Leong KM, Johnston EW, Romolini M (2016) Network governance for large-scale natural resource conservation and the challenge of capture. Front Ecol Environ 14(3):165–171. https://doi.org/10.1002/fee.1252
Bodin Ö (2017) Collaborative environmental governance: achieving collective action in social-ecological systems. Science. https://doi.org/10.1126/science.aan1114
Bodin Ö, Crona BI (2009) The role of social networks in natural resource governance: what relational patterns make a difference? Glob Environ Change 19(3):366–374. https://doi.org/10.1016/j.gloenvcha.2009.05.002
Bodin Ö, Prell C (2011) Social networks and natural resource management: uncovering the social fabric of environmental governance. Cambridge University Press
Bodin Ö, Crona B, Ernstson H (2006) Social networks in natural resource management: what is there to learn from a structural perspective? Ecol Soc 11(2):r2
Bodin Ö, Crona B, Thyresson M, Golz A-L, Tengö M (2014) Conservation success as a function of good alignment of social and ecological structures and processes. Conserv Biol 28(5):1371–1379. https://doi.org/10.1111/cobi.12306
Bodin Ö, Alexander S, Baggio J, Barnes M, Berardo R, Cumming G, Dee L, Fischer M, Fischer AP, Mancilla Garcia M, Guerrero A, Hileman J, Ingold K, Matous P, Morrison T, Nohrstedt D, Pittman J, Robins G, Sayles JS (2019) Improving network approaches to the study of complex social-ecological interdependencies. Nat Sustain 2:551–559
Bojanowski M (2015) intergraph: coercion routines for network data objects. R package version 2.0-2. http://mbojan.github.io/intergraph
Butts CT (2008) network: a package for managing relational data in R. J Stat Softw 24(2):1–36
Butts CT (2020a) sna: tools for social network analysis. R package version 2.6. https://CRAN.R-project.org/package=sna
Carlsson L, Sandström A (2008) Network governance of the commons. Int J Commons 2(1):33–54
Clark WC, Harley AG (2020) Sustainability science: toward a synthesis. Annu Rev Environ Resour 45:331–386. https://doi.org/10.1146/annurev-environ-012420-043621
Cohen PJ, Evans LS, Mills M (2012) Social networks supporting governance of coastal ecosystems in Solomon Islands. Conserv Lett 5(5):376–386. https://doi.org/10.1111/j.1755-263X.2012.00255.x
Costenbader E, Valente TW (2003) The stability of centrality measures when networks are sampled. Soc Netw 25(4):283–307. https://doi.org/10.1016/S0378-8733(03)00012-1
Crowder LB, Osherenko G, Young OR, Airame S, Norse EA, Baron N, Day JC, Douvere F, Ehler CN, Halpern BS, Langdon SJ, McLeod KL, Ogden JC, Peach RE, Rosenberg AA, Wilson JA (2006) Resolving mismatches in U.S. ocean governance. Science 313(5787):617–618. https://doi.org/10.1126/science.1129706
Csardi G, Nepusz T (2006) The igraph software package for complex network research, version 1.2.6. InterJournal Complex Systems, 1695
DeFries R, Nagendra H (2017) Ecosystem management as a wicked problem. Science. https://doi.org/10.1126/science.aal1950
Dellavalle RP, Hester EJ, Heilig LF, Drake AL, Kuntzman JW, Graber M, Schilling LM (2003) Going, going, gone: lost internet references. Science 302(5646):787–788
Duda JJ, Camp RJ (2008) Ecology in the information age: patterns of use and attrition rates of internet-based citations in ESA journals, 1997–2005. Front Ecol Environ 6(3):145–151
Elgin DJ (2015) Utilizing hyperlink network analysis to examine climate change supporters and opponents. Rev Policy Res 32(2):226–245. https://doi.org/10.1111/ropr.12118
Ernstson H, Sörlin S, Elmqvist T (2008) Social movements and ecosystem services—the role of social network structure in protecting and managing urban green areas in Stockholm. Ecol Soc 13(2):39
Groce JE, Farrelly MA, Jorgensen BS, Cook CN (2019) Using social-network research to improve outcomes in natural resource management. Conserv Biol 33:53–65. https://doi.org/10.1111/cobi.13127
Hayes AL, Scott TA (2018) Multiplex network analysis for complex governance systems using surveys and online behavior. Policy Stud J 46(2):327–353. https://doi.org/10.1111/psj.12210
Hennessey J, Ge SX (2013) A cross disciplinary study of link decay and the effectiveness of mitigation techniques. BMC Bioinform 14(SUPPL.14):S5. https://doi.org/10.1186/1471-2105-14-S14-S5
Hileman J, Lubell M (2018) The network structure of multilevel water resources governance in Central America. Ecol Soc. https://doi.org/10.5751/ES-10282-230248
Hileman J, Bastos MTA, Lubell M (2018) Robustness and the paradox of bridging organizations: the exit problem in regional water governance networks in Central America. Soc Nat Resour 31(6):683–697
Hondula KL (2020) Shiny App Accessibility, part 1: only you can prevent link rot. SESYNC Cyberhelp for Researchers & Teams Blog. https://cyberhelp.sesync.org/blog/shiny-in-pubs.html#fn:2
Issuecrawler (2021) Issuecrawler instructions for use. www.govcom.org/Issuecrawler_instructions.htm
Janssen MA, Bodin Ö, Anderies JM, Elmqvist T, Ernstson H, Mcallister RRJ, Olsson P, Ryan P (2006) Toward a network perspective of the study of resilience in social-ecological systems. Ecol Soc 11(1):15
Jones SM, Van De Sompel H, Shankar H, Klein M, Tobin R, Grover C (2016) Scholarly context adrift: three out of four URI references lead to changed content. PLoS ONE. https://doi.org/10.1371/journal.pone.0167475
Khalil S, Fakir M (2017) RCrawler: an R package for parallel web crawling and scraping. SoftwareX 6:98–106. https://doi.org/10.1016/j.softx.2017.04.004
Kluger LC, Gorris P, Kochalski S, Mueller MS, Romagnoni G (2020) Studying human–nature relationships through a network lens: a systematic review. People Nat 4:1100–1116. https://doi.org/10.1002/pan3.10136
Kreakie BJ, Hychka KC, Belaire JA, Minor E, Walker HA (2016) Internet-based approaches to building stakeholder networks for conservation and natural resource management. Environ Manag 57(2):345–354. https://doi.org/10.1007/s00267-015-0624-8
Lubell M, Robins G, Wang P (2014) Network structure and institutional complexity in an ecology of water management games. Ecol Soc 19(4):23. https://doi.org/10.5751/ES-06880-190423
McAllister RRJ, Robinson CJ, Brown A, Maclean K, Perry S, Liu S (2017) Balancing collaboration with coordination: contesting eradication in the Australian plant pest and disease biosecurity system. Int J Commons 11(1):330–354. https://doi.org/10.18352/ijc.701
Morgans CL, Guerrero AM, Ancrenaz M, Meijaard E, Wilson KA (2017) Not more, but strategic collaboration needed to conserve Borneo’s orangutan. Glob Ecol Conserv 11:236–246. https://doi.org/10.1016/j.gecco.2017.07.004
Newig J, Günther D, Pahl-wostl C (2010) Synapses in the network: learning in governance networks in the context of environmental management. Ecol Soc 15(4):24
Ostrom E (2009) A general framework for analyzing sustainability of social-ecological systems. Science (new York, N.y.) 325(5939):419–422. https://doi.org/10.1126/science.1172133
Park HW (2003) Hyperlink network analysis: a new method for the study of social structure on the web. Connections 25(1):49–61
Park HW, Thelwall M (2006) Hyperlink analyses of the World Wide Web: a review. J Comput Mediat Commun. https://doi.org/10.1111/j.1083-6101.2003.tb00223.x
Pittman J, Armitage D (2017) How does network governance affect social-ecological fit across the land–sea interface? An empirical assessment from the Lesser Antilles. Ecol Soc 22(4):5. https://doi.org/10.5751/ES-09593-220405
R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/. Version 4.0.3
Sayles JS (2018) Effects of social-ecological scale mismatches on estuary restoration at the project and landscape level in puget sound, USA. Ecol Restor 36(1):62–75. https://doi.org/10.3368/er.36.1.62c
Sayles JS, Baggio JA (2017a) Who collaborates and why: assessment and diagnostic of governance network integration for salmon restoration in Puget Sound, USA. J Environ Manag 186:64–78. https://doi.org/10.1016/j.jenvman.2016.09.085
Sayles JS, Baggio JA (2017b) Social–ecological network analysis of scale mismatches in estuary watershed restoration. Proc Natl Acad Sci 114(10):E1776–E1785. https://doi.org/10.1073/PNAS.1604405114
Sayles JS, Mancilla Garcia M, Hamilton M, Alexander SM, Baggio JA, Fischer AP, Ingold K, Meredith GR, Pittman J (2019) Social-ecological network analysis for sustainability sciences: a systematic review and innovative research agenda for the future. Environ Res Lett 14:093003
Stockton T (2020) snaWeb package: an R package for finding and building social networks for a website, version 1.0.1
USDA Forest Service (2017) Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. https://www.nrs.fs.fed.us/STEW-MAP/data/
Vance-Borland K, Holley J (2011) Conservation stakeholder network mapping, analysis, and weaving. Conserv Lett 4(4):278–288. https://doi.org/10.1111/j.1755-263X.2011.00176.x
Vargas L, Emami P, Traynor P (2020) On the detection of disinformation campaign activity with network analysis. In: 2020 Cloud Computing Security Workshop (CCSW’20), 14. https://doi.org/10.1145/3411495.34213631
Yi H, Scholz JT (2016) Policy networks in complex governance subsystems: observing and comparing hyperlink, media, and partnership networks. Policy Stud J 44(3):248–279. https://doi.org/10.1111/psj.12141
We thank Blair Crossman, Jonathan Essoka, and Marisa Mazzotta for reviewing and commenting on manuscript drafts. This manuscript’s EPA contribution number is ORD-042444.
This work was supported by EPA’s Sustainable and Healthy Communities Research Program. Author JSS was supported by an appointment to the U.S. Environmental Protection Agency (EPA) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the EPA. ORISE is managed by ORAU under DOE contract number DE-SC0014664. Author RF was supported under EPA contract 68HERH20D0003 to Oak Ridge Associated Universities, Inc. (ORAU). The views expressed in this paper are the authors' and do not necessarily reflect the views or policies of US EPA, DOE, or ORAU/ORISE.
The authors declare that they have no competing interests. This work has been reviewed in accordance with EPA policy and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
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Supplementary materials including: 1) supplemental data, 2) details of organizational website selection, 3) snaWeb package and search parameters, 4) data scraping accuracy, stability, and data integrity assessments, and 4) the complete list of scraped URLs.
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Sayles, J.S., Furey, R.P. & ten Brink, M.R. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Appl Netw Sci 7, 36 (2022). https://doi.org/10.1007/s41109-022-00472-0
- Social network analysis
- Hyperlink networks
- Environmental governance
- Decision support tools
- Environmental stewardship