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Cartography and Geographic Information Science

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Elements of viral cartography

Anthony C. Robinson

To cite this article: Anthony C. Robinson (2019) Elements of viral cartography, Cartography andGeographic Information Science, 46:4, 293-310, DOI: 10.1080/15230406.2018.1484304

To link to this article: https://doi.org/10.1080/15230406.2018.1484304

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Elements of viral cartographyAnthony C. Robinson

GeoVISTA Center, Department of Geography, The Pennsylvania State University, University Park, USA

ABSTRACTMaking and sharing maps is easier than ever, and social media platforms make it possible formaps to rapidly attain widespread visibility and engagement. Such maps can be consideredexamples of viral cartography – maps that reach rapid popularity via social media dissemination.In this research we propose a framework for evaluating the design and social disseminationcharacteristics of viral maps. We apply this framework in two case studies using maps thatreached wide audiences on Twitter. We then analyze collections of maps derived from andinspired by viral maps using image analysis and machine learning to characterize their designelements. Based on our initial work to conceptualize and analyze virality in cartography, wepropose a set of new research challenges to better understand viral mapmaking and leverage itssocial affordances.

ARTICLE HISTORYReceived 28 March 2018Accepted 29 May 2018

KEYWORDSSocial media; cartography;virality; machine learning;design framework

Introduction

The processes of mapmaking and map disseminationare more widely accessible than ever. Free mappingtools, data sets, and tutorials combined with socialmedia dissemination vectors make it possible for non-experts to quickly create and share maps with verylarge audiences. In some cases, maps made for andshared via social media generate significant attention,and can be considered viral.

It is important to recognize that social media is acritical vector through which people now communicateideas, opinions, and news about our world. Maps play aspecial role in this ecosystem by virtue of their powerto graphically represent and simplify complex phenom-ena and provide spatial context for current events.Maps continue to shape conversation and policywhile they simultaneously, “. . .appear respectable andaccurate (Monmonier, 1996, p. 2).” We argue thatmaps maintain their special gravity in social media,and that their power to lead (and mislead) readers(Crampton, 2001; Harley, 1988) can be amplified bythis new channel for discourse. Therefore, maps insocial media should be carefully examined to exploretheir design and dissemination, with particular atten-tion to those maps that have successfully gone viral.

This work proposes a framework for evaluating theelements of viral maps, including aspects of theirdesign as well as aspects of their dissemination and

social engagement. This framework provides the abilityto begin characterizing and comparing viral maps.

Through two case study analyses, we show how ourviral mapping design framework can be applied tostudy recent examples of viral cartography related tothe 2016 United States Presidential Election and the2017 Solar Eclipse. We then apply image analysis andmachine learning methods to characterize and visualizethe design elements of maps derived from and inspiredby those two viral maps. Based on our case studyanalyses, we highlight key research challenges thatemerge from our initial work on identifying elementsof viral cartography. We believe this work helps set thestage for identifying key attributes that support viralityin map design, and supports a longer-term goal to usethat information to be able to leverage those attributesfor the deliberate generation of viral maps in socialmedia.

Background

Virality in social media

Defining virality is a key concern for those interested inanalyzing the elements of viral cartography. There arenot yet widely agreed-upon definitions for what con-stitutes social media virality, but in broad terms viralityin social media typically has two key hallmarks; viralmedia achieves rapid popularity, and it achieves this

CONTACT Anthony C. Robinson acr181@psu.eduSupplemental data for this article can be accessed on the publisher’s website.

CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE2019, VOL. 46, NO. 4, 293–310https://doi.org/10.1080/15230406.2018.1484304

© 2018 Cartography and Geographic Information Society

popularity by virtue of people sharing media with oneanother (Goel, Anderson, Hofman, & Watts, 2016).The concept of virality in social media is intended toevoke the fundamental biological processes associatedwith the spread of disease from person to person. Itoperates in a distinct manner compared to traditionalforms of media that rely on top–down methods fordiffusion (e.g. a TV news story shared via cable televi-sion to its network of subscribers).

The mechanism of virality is a key area of concern,in order to characterize the process of informationdiffusion in addition to its overall reach. Centola(2010) suggests that encouragement or reinforcementfrom neighbors on a social network can lead to fasterand broader diffusion compared to networks thatemphasize raw connectivity to provide shortcuts pastclusters of users.

Other scholars have focused on the content of viralmedia itself, and have noted that emotions appear toplay a significant role in shaping what online contentmay become viral. Content that evokes a high degree ofarousal has a greater likelihood of being widely shared(Berger & Milkman, 2012). Additionally, Berger andMilkman (2013) found that when emotional valence isconsidered, positive content has a greater tendency ofbeing shared than negative content.

For the purposes of our research, we consider a mapto be viral when it has achieved rapid popularity viasocial media diffusion. An immediate challenge forapplying this definition is that we have to choose athreshold for what constitutes rapid popularity. Weargue that this threshold will vary considerablydepending on the type of viral content (e.g. text,image, video) and its maximum potential audience.For example, we hypothesize that the threshold forwhat constitutes a viral map would be a lower level ofoverall popularity compared to the threshold for whatconstitutes a viral music video. This assumes that thereare more music videos than maps, and a wider poten-tial audience for music videos than maps.

In an effort to characterize the diffusion of viralsocial media, Goel et al. (2016) focus on what theycall structural virality in social media. Specifically,they seek to distinguish between media that are broad-cast from one user to many followers and media thatare shared via many branches and cascades of users.Both processes may ultimately reach a large number ofusers, but the latter process would be more structurallyviral than the former. By focusing on structural char-acteristics, Goel et al. are also able to characterizevirality from less-to-more without relying on simplethresholds for the number of users who have engagedwith content.

We are also prompted to consider the stability of viralcontent in multiple ways. For example, the purest formof virality in social media is the diffusion of content inits original form (Blommaert & Varis, 2017) (e.g. shar-ing a link to an article with friends). A related process isthat of memetics. Richard Dawkins was among the firstto describe the diffusion of small aspects of culture as ameme (Dawkins, 1976), and in the past decade the riseof social media has led to new forms of memes spreadvia social networks. Memes in the social media era arecontent that is modified and shared, and the generationof derivatives is a common feature (Shifman, 2013).In the case study portion of this article, we draw atten-tion to the fact that viral maps (shared widely in theiroriginal form) are capable of spawning memetic reac-tions (as derived or inspired works).

Virality in cartography

Viral maps have to date received only a little attentionin academic cartography. Muehlenhaus (2014) offeredone of the earliest examples of published work to con-sider the notion of virality in cartography. In this work,Muehlenhaus proposes multiple persuasive map stylesand situates these among new mechanisms for rapiddevelopment and dissemination of maps. These mapstyles are then connected to real examples harvestedfrom a variety of web sources. Aside from the mentionin the title, however, no further exposition is offeredregarding the mechanism or character of virality incartography itself.

In Field (2014), we find an overview of the myriadissues surrounding new forms of digital cartography,particularly those that are developed by and sharedamong non-cartographers. Field’s essay critiques thequality and potential impact of widely-shared works,and briefly touches on the concept of virality whenexamining web maps that were showcased by Wiredmagazine as examples of viral cartography. Field’sfocus on a traditional media source such as a magazinealso suggests the need to consider how mapping in thenews media (Monmonier, 1989) may help us under-stand how maps have been interpreted through socialengagement well before the rise of social media.Furthermore, studies of cartography in news mediahave also highlighted how newspaper map design hasoften liberally interpreted the conventions of carto-graphic design in order to provoke and engage a publicaudience (Cosgrove & della Dora, 2005; Green, 1999;Vujakovic, 2002). This lens provides a valuable parallelto consider with respect to social media cartographywhere we also find unconventional maps that areintended to be provocative.

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We argue that this previous work highlights theopportunities associated with further investigatingviral cartography. We do not yet have good definitionsfor what constitutes a viral map. We do not have muchsubstantial knowledge about how viral maps aredesigned. And we do not yet know to what extentviral maps have an impact beyond their apparentephemerality.

The need to focus on elements of viral cartographyalso echoes to the imperatives of recent cartographicscholarship, which have included multiple calls for newefforts to explicitly connect map research to advancingsocietal and environmental problem solving(MacEachren & Kraak, 2001; Robinson et al., 2017;Virrantaus, Fairbairn, & Kraak, 2009). While viralmaps do not necessarily influence such problem dimen-sions, there is little doubt that understanding whatmakes maps go viral would help inform the design ofmaps that could reach a level of public influence that hasan impact on major global issues. As much as newanalytical methods may be necessary to solve problemslike food security and water shortages, we also mustdevelop a deeper understanding regarding how mapscan pique public curiosity and reach a significant levelof public currency. This investigation must also addressthe power that maps have to mislead, as well as informpublic discussion (Kent, 2017).

Elements of viral cartography

We propose the following design framework for char-acterizing dimensions of virality in cartography. Theconstruction of this framework is based upon afusion of core design considerations for makingmaps, key visual attributes of map layouts, the con-text in which the map was shared, and the degree towhich the map received social engagement. Here wedefine each proposed framework element and makeconnections to cartographic and social media con-cepts that ground our choices. In proposing theseelements, we draw here on core cartographic andvisual design texts (Bertin, 1967; Brewer, 2015;Dent, 1985; Krygier & Wood, 2011; MacEachren,1995; Robinson, Morrison, Muehrcke, Kimerling, &Guptill, 1995; Slocum, McMaster, Kessler, & Howard,2008). Although there are potentially dozens ofdesign dimensions that can be evaluated for a givenmap, we focus on the subset of map design elementsthat are most likely to have unique characteristicsand/or implications in a viral mapping context andwe explain those connections in each of the sectionsbelow.

Purpose

The purpose of a viral map refers to the user task that amap is intended to support. Whether it helps peoplenavigate from place to place or helps explain a socio-economic trend, every map has a purpose (Krygier &Wood, 2011). This dimension is particularly interestingin viral maps given the tendency for social media tofocus at least as often on entertainment value as it doeson the dissemination of news. In addition, the emer-gence of internet media intended to provoke user reac-tions, often called click bait (Blom & Hansen, 2015),represents another reason to carefully examine thepurpose of a given viral map. We highlight here theimportance of considering viral map purpose in termsof the designed intention of its cartographer, remainingdistinct from the potential re-interpretations andderived uses of a map once it has gone viral andreached a wide audience.

Audience

As is true with any map design, viral maps areassociated with an intended audience who are receiv-ing, reading, and interpreting their information. Wepropose distinguishing audience from social engage-ment by focusing on the reader that the cartographerhad in mind while designing a viral map. The formerconcerns the people for whom the map design wasintended to reach, while the latter focuses on themagnitude and character of who was actually engagedwith a viral map. An aspect of viral cartography thatmay be distinct from other cartographic design con-texts is that it is difficult to predict in advancewhether or not a map shared on social media willbecome viral. There can therefore be significant dif-ferences between the audience of a viral map as itwas initially designed versus the social engagementthat actually occurs.

Format

Format is the way in which a viral map is presented foronline consumption. We suggest that viral map formatinformation should include the type of online presen-tation as well as information about its interactive affor-dances. For example, a viral map may be a static mapimage provided as a JPEG file. Alternatively, a viralmap may be initially presented as an animated GIFimage, or as a static image that links to an interactivemap built with Javascript. Future viral maps maybe presented in other formats, perhaps taking advan-tage of new virtual or augmented reality paradigms.

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Location

The location for a viral map refers to the geographic region(s) for which it represents. Like other map formats, viralmaps may include multiple locations in a single layout. Inviral cartography, location references may also be impliedby the context of a broader conversation or current eventsat the time a viral map is created and shared.

Scale

The scale associated with a viral map is not concep-tually distinct from other forms of cartography, and itrefers to the ratio of distance on the map to the dis-tance on the earth. We suggest that viral maps may bemore likely to deviate from cartographic design normswith respect to scale compared to maps designed byprofessionals. For example, scale information may notbe provided on many viral map layouts, and audiencesfor viral maps may have limited experience interpretingscale measurements on maps.

Visual variables and symbology

Maps for sighted users employ visual variables andsymbology to encode information about data. A con-cern for understanding viral cartography relates to thefact that we know relatively little about the map read-ing and interpretation skills of social media users.Confusing symbols, color schemes that do not appro-priately match data, and the use of colors that areproblematic for color blind readers may be reasonablyexpected to hinder the ability of a map to go viral, butuntil we evaluate this hypothesis we cannot be sure. Itis also possible that such design issues may simplyimpact how particular users interpret what they see,rather than augmenting viral potential.

Projection

As with scale, we suggest that projection informationshould be evaluated for viral maps whenever possible.Map projections transform the three-dimensionalEarth into two-dimensional planimetric formats. Thechoice of map projection has major implications formap reading, and we suggest that the widespread dis-semination of viral maps may amplify these implica-tions. For example, we note the recent surge of viralmaps that purport to show the range of North Koreanballistic missiles, some of which use projections appro-priately, and some that do not.

Marginalia

In traditional map design, marginalia refer to the widerange of supporting graphical and textual elements thatcan be overlaid on or adjacent to a map. Titles, notes,source information, and supporting data graphics areexamples of map marginalia. Marginalia is particularlyimportant in viral cartography as pertains to sourceand authorship information, which may be missing orincomplete (or modified downstream as a viral mapmoves across a network). We suggest that the text onewrites to introduce a graphic shared via social media isa form of marginalia, akin to a caption or title on atraditional map.

Message context

Viral maps need to be considered in the context fromwhich they were generated. We propose that messagecontext in viral maps concerns the social/environmentalphenomena that set the scene for mapping to take place.In some ways this attribute can be considered a broaderversion of the aforementioned map purpose element,which concerns the specific aim of a given map.Message context in contrast focuses on the broaderconditions of the world that a given map was craftedwithin. For example, a viral map may have a purpose ofcommunicating the voting tendencies of a given groupof voters, but the message context for this map would bea major election that is taking place later that year.

Social engagement

Understanding virality in maps from social mediarequires attention to characteristics of popularity.Such characteristics can include the audience reached,the rate at which that audience was reached, and thereactions that map may have inspired. For example, inthe context of a viral map posted on Twitter, one couldanalyze the overall spread of that viral map by consid-ering the network of people who retweeted that map.One could also capture the number of times Twitterusers made a viral map one of their favorites. Replies toa viral map tweet are also noteworthy signs of socialengagement, and these replies could potentially includenew or derived maps, such as those we explore later inthis article. Other social media platforms aside fromTwitter, as well as future social platforms that willemerge, may have additional ways in which their audi-ence can be characterized, their viral disseminationvectors can be understood, and their audience engage-ment through reactions (including map-oriented reac-tions) may be measured. It is also theoretically possible

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to characterize broader types of engagement outside ofa social media platform, for example to evaluate theextent to which a viral map has gained attention intraditional news media formats.

Additional dimensions

Depending on the specific context for a viral map,there may be reason to characterize its design acrossadditional common map design dimensions. For exam-ple, a viral map may leverage a specific form of general-ization which heavily shapes its message. The analyticalprocess by which data is derived for a viral map couldhave a significant impact on how phenomena are inter-preted. The layout and visual hierarchy and typo-graphic design in a viral map may also be importantfactors to consider. We do not exclude the potentialthat these and other map design dimensions notexplored as primary elements in our design frameworkmay warrant deeper consideration in viral map analy-sis. We focus here on the map design elements that webelieve are the most likely to have unique considera-tions and dimensions in a viral mapping context.

Capturing viral maps and their derivations

Current social media platforms provide varying levels ofpublic access to data. For example, Twitter currentlyprovides a wide range of search and retrieval functionalityto public users via a dedicated Application ProgrammingInterface (API). Other social networks, such as Facebook,do not provide public users with free access to postedcontent via an API. As a result, our ability to system-atically capture viral maps in order to evaluate theirelements as proposed in our viral cartography frameworkwill vary considerably depending on which social mediaplatform is chosen. Furthermore, our ability to captureviral maps is very likely to change over time as newplatforms emerge and existing platforms evolve.

For the purposes of developing case study examplesto evaluate our proposed viral cartography design fra-mework, we have used the Twitter API (dev.twitter.com) to collect tweeted maps that successfully reachedlarge audiences in rapid fashion.

A particular challenge for our present work is thatthe Twitter API does not allow one to query and collectall replies to a given tweet. As a result, manual or semi-automated scraping processes must be used to try togather as many replies as possible from a specific tweet.For this project, we saved the full HTML file of each ofthe viral map “seeds” (the tweeted map that went viraland provoked subsequent responses) and scraped theimages and other metadata from that HTML file.

Specifically, we captured every reference in the HTMLto an 18-digit Tweet ID, and scraped all of the imagesthat were referenced in the HTML code, and thenmanually identified which images contained maps. Inaddition, using the 18-digit Tweet IDs, we used aPython script that leverages the Tweepy library (www.tweepy.org) and the Twitter API to retrieve structureddata about each reply to a viral map tweet.

This process for collecting replies to tweets is imper-fect, as one must scroll continuously through the tweetreply thread from the original post in a web browser inorder to try and retrieve as many replies as possiblebefore capturing the full HTML file. There is no guar-antee that all replies will be shown, and some usersmay block the ability to retrieve their tweets via theAPI. In addition, it is possible for users to delete pastTweets, and Twitter itself may withhold certain tweetsto limit their reach as part of its attempt to curbabusive behavior (Ohlheiser, 2015).

Case studies in viral cartography

To explore the application of our viral mapping designframework, we focus here on two recent case studyexamples where maps have been widely shared onTwitter. In the first example, we explore the viralWhat if Only Women Voted electoral college map. Inthe second example, we analyze a viral satirical mapabout the 2017 Solar Eclipse. These two examples arechosen in part to explore the challenge associated withidentifying a popularity threshold for viral mapping.The audience for the first case study example is con-siderably larger than the second. The author of the firstcase study example is a well-known media figure with ahuge following, while the author of the second casestudy example is a well-known figure within the geos-patial sciences, with a large following among membersof that (far smaller) group of users.

In both cases we characterize the engagement witheach initial tweet, and then characterize the design ofeach map according to elements in our viral mappingframework. We follow these aspects by describing theinteractions that result from each of these viral maps,including the development of modified versions ofthose maps in response to the original artifact.

Following the presentation of these general charac-teristics for each case study example, we apply imageanalysis and machine learning techniques to furtherexplore the collections of maps that are posted inresponse to an initial viral map. In both instances theoriginal viral map encouraged the generation of a largenumber of derived maps as well as completely newdesigns.

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What if Only Women Voted?

In the first case study example, we explore design anddissemination dimensions of a map that forecasts the out-come of the 2016 United States Presidential election if onlywomenwere to participate in voting (Figure 1). Nate Silver,a reporter and editor for the website FiveThirtyEight.com,posted this hypothetical electoral college map in October,2016. (https://twitter.com/NateSilver538/status/785972385836961792)

Nate Silver’s map was designed and shared in the wakeof a major news story regarding a presidential candidate’streatment of women. It came at a time whenmany electiontracking sites were creating and sharing their projectionsfor electoral college outcomes as well. This map fusedtogether a political scandal at that moment with a provo-cative message and packaged it in the form of what hadbecome a ubiquitous way of consuming election newsthrough projected electoral college maps. The reaction tothis map was a significant one, in part due to the fact thatother similar projections could be easilymade (e.g. “What ifonly millennials voted?”) and in part due to the reaction ofpolitical factions on both ends of the spectrum who usedNate Silver’s message to advance their own objectives.

In addition to Nate Silver’s original map, nine mapswere found in the set of 675 direct replies to his tweet.In addition to these direct replies, we also searched formaps that were potentially inspired by this tweet bysearching for and retrieving tweets posted after NateSilver’s initial post that contained the words “map ifonly voted.” We were able to capture 1722 tweets thatcontained these words. These tweets linked to hun-dreds of images, from which we identified over 500unique maps. Because authorship and sourcing is notpossible to verify from these sources, we are not able toreport a firm total number of maps created or re-purposed for this viral topic. Furthermore, given theaforementioned limitations in how retrieval works withTwitter, we cannot be certain we are retrieving everysingle example using that set of search terms. In somecases users posted images that do not have any of thedesign characteristics of maps. In other cases the mapsshared are simply reposted from other sources butincluding a narrative caption provided by the userthat suggests a re-interpretation of the original work.We expand on these types of challenge in our discus-sion on research priorities later in this article.

Figure 1. Design and social engagement elements for Nate Silver’s viral map which hypothesizes a presidential election outcome ifonly women were to vote. Image reproduced by permission of Nate Silver, FiveThirtyEight.com

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There are no more eclipse maps to make

In our second case study example, we characterize thedesign and dissemination of a viral map that was cre-ated by a cartographer and shared widely among othercartographers, graphic designers, and visualizationexperts. Joshua Stevens created and shared a tongue-in-cheek map (https://twitter.com/jscarto/status/892933635761995780) (Figure 2) that juxtaposed pur-ported Bigfoot sightings with the path of the 2017 TotalSolar Eclipse. Stevens had previously created a viralmap on Bigfoot sightings in 2013 that generated sig-nificant media attention (Morris, 2013). Stevens’ 2017map overlays the track of the solar eclipse with Bigfootsightings, creating a clever juxtaposition that drawstogether two phenomena that frequently inspire publicintrigue. As in the Nate Silver election map example,Stevens’ design presents an unorthodox twist on whathad become a familiar type of map showing the eclipseswath. The response to this map included a wide rangeof news articles and repostings across popular media,but unlike the election map example, there was nothingcontroversial about the message conveyed by theeclipse map; instead this work was about making some-thing broadly entertaining.

The eclipse map example has a smaller total audi-ence than the election map example, but we note thatStevens’ map generated 32 direct replies that featured

derived and inspired maps created by other designers.One of these replies using the same approach to showWaffle House restaurants within the eclipse swathachieved virality on its own (Pirani, 2016). We believethis example highlights the potential for viral maps toserve as catalysts for cartographic production and shar-ing in social media.

Analyzing viral map derivations

Each of the case study examples we have applied ourviral cartography framework to characterize has trig-gered the development of a large number of derivedmaps as well as completely new map designs (hereafterreferred to as viral map derivations). These mapsinclude direct modifications of the original work,repurposed maps found elsewhere on the web, andnew map designs that were inspired by the originalviral work. We believe such maps represent a uniqueform of cartographic dataset and that they providefurther affordances by which viral cartography canbe characterized. In the sections that follow we useimage analysis techniques, computational content ana-lysis, and treemap visualization to evaluate viral mapderivations from both the election and eclipse map casestudies from a variety of perspectives.

Figure 2. Characteristics of viral cartography as demonstrated by Joshua Stevens’ 2017 Total Solar Eclipse map. This figure has beenreproduced with the originator's permission.

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Image analysisIn order to characterize the visual attributes of viralmap derivations from the election and eclipse casestudy examples, we used the Imageplot extension tothe ImageJ system (Schneider, Rasband, & Eliceiri,2012). This toolset supports analysis and visualizationof directly measured image attributes such as hue,saturation, and lightness. This type of analysis canaugment traditional forms of analyzing the designcharacteristics of each individual map by providing anoverview of basic image attributes from which it maybe possible to more general trends in visual depiction.We apply image analysis here for that reason, to gain asense of the general visual design trends across largecollections of maps.

In Figures 3 and 4, we show montages of the 32maps found in replies to the eclipse viral map and the500 maps we sampled from those inspired by the elec-tion viral map. In both montages, we order the mapsby their median hue, from top to bottom. From these

views, it is possible to gain a sense of the overall typesof contributions people provided in response to theinitial viral maps. The eclipse examples hew closely tothe original design created by Joshua Stevens, with onlya few straying from the original overall layout.

In the case of the viral election map, we see a greatervariety in designs and the dominance of red and bluecolors that correspond to the election in the UnitedStates. A large number of layouts with strong colorelements are categorized as having a low median hue(shown at the top of the figure) because the balance ofpure white pixels is overwhelming the non-white pixels.

Content analysis with machine learningTo further explore the content of each of these maps,we used the Google Cloud Platform Cloud Vision API(GCV) to classify the map images into categories,detect objects in them, and associate relevant words.GCV (https://cloud.google.com/vision/) uses machinelearning methods to classify images, detect objects inthem, analyze their visual contents, and identify

Figure 3. Viral map derivations (n = 32) from the eclipse viral map case study are shown here, ordered from top to bottom by theirmedian hue.

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partial/complete URL matches for them. GCV is acommercial, proprietary service, and as such the designof its methodology is not publicly described. It is one ofseveral similar services available today which supportthe automated analysis of images via cloud computingand machine learning. We apply it here because itrepresents a candidate service which may realisticallybe applied in real-time to analyze the content in viralsocial media posts to identify maps and describe theircharacteristics without requiring human intervention.GCV has been used in recent research to characterizephotographs of nature (Richards & Tunçer, 2017),analyze animated GIFs (Gygli & Soleymani, 2016),and to develop assistive technologies for the blind(Mulfari, Celesti, Fazio, Villari, & Puliafito, 2016).

Using GCV’s labeling algorithm, we are able toexplore a range of topics that are computed to be

relevant to each map image. In Figure 5, we showtreemaps that summarize the labels associated with500 maps we collected in the election case study, and32 maps contributed in the eclipse case study. Thesetreemaps were created using Tableau (www.tableau.com). In each figure the treemaps are sized accordingto the sum of a topicality measure that GCV calculates.Topicality measured by GCV ranges from 0 to 1, with avalue of 1 corresponding to the full relevance of acertain label to an image. For example, the topicalitymeasure of “airplane” would be higher in an image ofan airport tarmac full of planes than it would be in animage of a landscape in which an airplane is visible inthe distance. By summarizing machine learning resultsin this way, we are able to explore how a method likeGCV can help us characterize attributes of mapcollections.

Figure 4. Viral map derivations (n = 500) from the election viral map case study are shown here, ordered from top to bottom bytheir median hue.

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Since hundreds of labels were detected for each casestudy example, we filter our treemaps to show onlythose that added up to a sum of greater than 10 for atopicality across the entire corpus for the electionmaps, and a sum greater than 1.5 for the eclipse

maps. These thresholds are the result of multiple itera-tions to explore the long tail of results for each collec-tion. What we see in both examples is that GCV findsthat map is the most prominent topic for each case, aresult that we would expect to see. Since we provided a

Figure 5. Treemap visualization of image labels for maps posted in response to viral Election and Eclipse map examples. The sum oftopicality measures from GCV is used to size each cell, and both panels are filtered to show sums greater than 10 and 1.5 for theElection and Eclipse case studies, respectively.

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curated input to GCV of examples that we consideredto be maps, we are able to verify that there is agreementbetween how the authors see maps and how thismachine learning approach “sees” the same images.

Going further, we can probe the dozens of otherlabels that GCV finds in our image collections. In theelection map case study in the top half of Figure 5, we

see many labels related to graphic design like diagram,line, area, font, and drawing. Some labels correspond tothe content of each image, rather than their structuralattributes. These include water resources, ecoregion, sky,tree, and pattern. We see virtually no evidence thatGCV finds these images to be pertaining to politics,elections, or specific candidates – something we argue

Figure 6. Treemap visualization of web entities for maps posted in response to viral Election and Eclipse map examples. Thenumber of detected instances for each entity is used to size the cells, and thresholds of 5000 and 25 were used to filter the Electionand Eclipse case studies, respectively.

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that human reviewers would be far more likely toassociate with many maps in this collection.

In the eclipse maps, we see similar results from thelabel analysis (Figure 5 (bottom)), with a variety ofgraphic design attributes and to a lesser extent somecontent-oriented labels that loosely correspond to gen-eral environmental characteristics. One label thatpiques our interest is that of angle which we suspectcorresponds to the large swooping line that cuts acrossvirtually all of the maps designed in this case studycollection. Once again, while GCV provides some gen-eral attributes relevant to each image, we do not gain aclear sense of the topic context for the eclipse mapcollection. We do not see labels corresponding tosolar eclipses, satire, humor, entertainment, or other

labels that we would likely see if we had humans labelthese same images.

In addition to labeling, GCV categorizes what it callsWeb Entities for each image. These are defined byGCV as web references to an image. In contrast tolabel detection, GCV here is using the context in web-sites that feature a given image to make a guess as to itscontents. In the label detection case, GCV is looking atthe image itself to make this determination, rather thanits context on a webpage. In Figure 6, we show tree-maps that summarize these web entities, using size toindicate the number of times a given entity is men-tioned in each case study collection.

The results of web entity analysis by GCV highlightthe topics and context we did not see in the GCV label

Figure 7. A map generated in response to Nate Silver’s original What if Only Women Voted? Viral map. This map became somewhatof a viral sensation itself, and was reposted many times. This map has been reproduced with the originator's permission.

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detection analysis. For the election maps, we see thatmost of the related web entities correspond to theelection, its candidates, and the relevant geography.For the eclipse maps, we see references to the eclipseitself, bigfoot (a key aspect of the original viral map),and references to design and visualization, whichmakes sense given the fact that so many of the map-makers in this particular example are well-knownvisualization experts.

In summary, we find that using both label detection aswell as web entities can help us formulate a more com-plete picture regarding the elements associated with amap collection. Label detection seems to provide a morefundamental parsing of what is found in a map image interms of its general structure, while the web entitiesassociated with that same image help us better under-stand the context for producing and sharing that image.The former helps us understand what we are seeing, andthe latter helps us understand why we are seeing it. Wenote however that human intervention would still benecessary in order to further characterize the carto-graphic design choices associated with each map exampleand to compare those choices to the original viral map.

Provenance in viral cartographyIn addition to topic labeling and entity connection,GCV provides a wide range of additional lenses bywhich you can analyze images. Among other things,GCV can characterize the mixture of colors present inan image, make a guess as to whether or not an imagecontains adult-oriented content, and detect faces andcategorize their emotional state. Additionally, GCV canidentify exact and partial image matches on the inter-net, meaning that it has the potential to aid our studyof the provenance of viral maps. We can be sure thatmany maps shared on social media are derived fromexisting sources, so this aspect of GCV can help usexplore those sources, as well as to help explain thedissemination of viral maps well beyond the socialfeeds where they may have been captured.

For example, we noticed the repeated disseminationof the map in Figure 7 in the midst of the response toNate Silver’s initial viral map in 2016. It purports toshow a range of potential electoral vote outcomesdepending on hypothetical situations in which differentsubgroups of people were the only ones to vote. Withthe image alone we can perhaps guess how it may haveoriginated, but its initial postings featured no attribu-tion or source information. We found that one personhas claimed to be its author (https://medium.com/@ste.kinneyfields/do-you-know-this-graphic-i-made-it-heres-why-f97bcf88408c) and they purportedly used acombination of FiveThirtyEight.com and 270toWin.

com sites to manually recolor the electoral map foreach panel to create the combined graphic.

Given that we have some ground truth for this bit ofsocial cartography,we nowexplorewhat theGCVplatformcan tell us about its provenance based on image analysisalone (e.g. what if we did not have any of the backstory?).GCV detected 48 exact matches to this image in URLs, and50 partially-matching URLs (see Supplementary data forthese URLs). In these collections, we note the presence ofthis image on image hosting sites that correspond toWordPress blogs, Twitter, 4Chan, and Tumblr. Since thematching and partial matching URLs are most often refer-ences to image files on a server, it can be difficult to under-stand their context. Working backward, we can look at theroot addresses in URL references to explore their sourcepages in more detail. For example, we found multipleconservative blogs hosted on WordPress which appearedto be using this map image to drive discussions aboutits political implications. In other cases we find this imagehas been archived by web crawling services which aremonitoring ephemeral sources like 4chan for which post-ings only exist for a few hours before being automaticallydeleted.

Similar investigations can be done for viral maps andtheir derivations to characterize the spread of viral mapsacross the web and to identify the potential sources forrepurposed and modified maps. We hypothesize that suchmethods may also help us identify maps that are artificiallygenerated and/or those that have been promoted by bots inorder to drive audiences to engage with them.

Research challenges for viral cartography

Based on our initial work to characterize design dimen-sions in viral cartography and to explore the presence ofthose elements in recent case study examples, we reflecthere on key research challenges that emerge regarding thestudy of viral maps.

What are the types of maps associated with viralcartography?

Based on our initial work to study examples of viralcartography, we have identified at least four types ofmaps that are relevant to the study of viral cartography.First, we have explored the design and disseminationcharacteristics of a primary viral map; a map that bysome measure has been designed and shared via socialmedia and has been viewed by a large audience. Wehave also noted the presence of repurposed maps, forwhich a social media user provides commentaryattached to an existing map design. We have alsoobserved the creation and sharing of derived maps,

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for which social media users are modifying an existingmap design and re-sharing. Finally, we have collectedexamples of inspired maps that are new designs whichhave been prompted by the development and sharingof a primary viral map.

It is possible that other map types may be character-ized from social media sources, and even if we are ableto define the four types we propose here, it is non-trivial to capture all of them as many current socialmedia APIs do not support exhaustive searches, weknow very little about the provenance of graphics thatare shared via social media, and some shared graphicsmay be considered more or less map-like depending onwho is interpreting a given source of media. As anexample of the latter, we note “maps” such as the out-line of the lower 48 United States used as a mask over apicture of a pizza – an example we found frequentlywhen collecting viral map derivations in the electionmap case study.

How can viral maps be detected?

A key issue lies within how virality in mapping can bedetected and measured. We hypothesize that virality inmapping will have to be measured relative to the potentialaudience for a specific map. For example, a map that isshared widely among cartographers on social media maybe important to detect and study, but its raw number ofuser engagements (retweets, replies, and favorites in thecontext of Twitter) will almost always be far lower than anymap shared by a major news organization or public figure.

A first step in future work would be to analyze the so-called structural virality of map-related tweets as proposedby Goel et al. (2016), which offers a mechanism for mea-suring the virality that focuses on the nature of socialsharing rather than focusing on the raw magnitude interms of audience size.

How can viral maps be captured?

Assuming that a threshold exists for determiningwhether or not a map is viral, there emerges a secondkey concern regarding our ability to capture an originalmap, its repostings, its modifications, and its inspiredderivatives. Some social media sources provide accessto raw data via an API, but others do not. Even thosethat do provide API-level access, like Twitter, imposelimitations in what can be accessed for free, and howmany records can be retrieved at a given time. In manycases it is not immediately possible to capture allrepostings, modifications, and derivatives. For

example, a user may be inspired to create an eclipse-related map after seeing an initial post about one, butmay not directly reply to the original message, use thesame hashtags, or otherwise make an explicit connec-tion to that original source. Maps made and shared bynews organizations are very often reposted by othersocial media accounts using different headlines, andthese may also pose challenges for collection.

Paid sources do exist for making exhaustive searchesthrough social media sources, such as those provided byGNIP for searching the entire Twitter archive. However,exhaustive searches in these data sets can be extremelyexpensive to execute, and result in hundreds of millionsof potential records to explore. We requested a quote forone full year of all tweets that include media and mentionthe word “map” from the Sifter service provided byTextifier, which leverages the GNIP data archive ofTwitter. The cost for one full year of data for that broadquery was quoted at more than 22 million dollars. It ispossible to significantly reduce the cost by introducingsampling to those queries and narrow the search muchfurther by using multiple specific keywords, but thesemodifications will make it harder to find all relevant infor-mation to viral maps.

We have shown here how machine learning methodssuch as those employed byGoogle’s CloudVision platformcan help suggest exact and partially-matching images onthe web, and how this platform seems to be capable ofidentifying maps and their thematic contents. One fruitfuldirection for future work would be to systematically checkthe matches suggested by machine learning platforms tosee how closely human coding matches these automatedtechniques. For example, do human coders agree with thelabel codes assigned to maps by a platform like GCV? Inaddition, platforms likeGCV support analysis of streamingdata, making it potentially feasible to automatically flagimages that contain maps and to provide an initial evalua-tion of their contents.

Is it possible to intentionally craft viral maps?

The authorship of viral maps deserves a great deal offurther research. We do not know very much yet aboutwho is making maps on social media in general, let alonewho appears to be successfully making viral maps on socialmedia. A recent surge in automated content generation bybots on social media services (Nied, Stewart, Spiro, &Starbird, 2017) also begs us to explore the extent to whichviralmapsmay be crafted by automatedmethods, aswell asthe extent to which bots may help facilitate the virality of amanually-created map by automating the process of

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widespread dissemination. One initial aspect of viral mapauthorship to explore is to analyze the time it takes for newmaps to appear in the wake of current events. Even inexpert hands it should take a non-trivial amount of timefor a new map to be created, even if it is a derived work.Maps that appear very rapidly and are not found to havebeen directly repurposed from existing web sources couldbe flagged as algorithmically generated. Artificial intelli-gence techniques already exist for creating convincing syn-thetic photos and videos (Liu, Breuel, & Kautz, 2017;Roose, 2018).

How are viral maps perceived by users?

We need to know if the rhetorical power of maps is main-tained in a viral social media context. Future user studiesshould seek to characterizewhether or notmapsmaintain alevel of authority and trustworthiness in users’ minds. Ifthey do, then wemust be especially careful to anticipate theproblems associated with the amplification potential asso-ciated with rapid social dissemination. It is not implausibleto imagine an intentional disinformation campaign thatcouples viral social media and the power of maps, forexample. A related need is to understand the roles ofcreation and endorsement when it comes to user interpre-tations of viral maps. What does it take for users to trusttheir creators? What types of endorsement (e.g. a mapretweeted by a famous person or a government agency)influence user perceptions of the meaning of viral maps?Finally, what is the role of emotional connection to viralmapping? Previous work has already established that con-tent that prompts an emotional response tends to be cor-relatedwith virality (Berger&Milkman, 2012, 2013). Thereis an opportunity here to connect contemporary work toexplore the role of emotion in cartographic design (Griffin& McQuoid, 2012; Pánek, Pászto, & Marek, 2017) towardunderstanding why maps go viral.

What are the memetic affordances of viral maps?

In the case study examples we presented above, a largenumber of maps and graphics were generated as a resultof an initial viral map. Some of these derivations could beconsidered memetic in nature (Blommaert & Varis, 2017;Shifman, 2013), and we need to know more about whatcharacteristics of viral maps lend themselves to supportingmeme generation. We also need to know more about thesocial media users who are actively creating memetic deri-vations of viral maps. There are fewer barriers than ever fora non-expert to make a map or alter an existing image, sopresumably the potential community of meme-mapperswould be quite large. However, we know very little rightnow about patterns of memetic mapping in social media.

What tasks do viral maps serve?

It is unclear what task-oriented roles viral maps may serve.In a crisis event, we may find that a viral map conveysimportant information that is able to be widely utilized forthe benefit of those affected.On the other hand, Blommaertand Varis (2017) argue that viral social media are phatic innature, functioning as a form of digital “small talk” tosupport general social engagement. Phatic maps in socialmedia would therefore not serve critical tasks, and insteadsimply support the general social atmosphere (akin tosaying “How’s it going?” to a coworker in the hallway).Future work should attempt to identify what tasks viralmaps can support to evaluate the extent to which they cantruly augment human behavior, or simply serve as fodderfor low-impact phatic conversation. Of equal interest is theextent to which we can characterize the motivation forusers to create and share viral maps. We need to under-stand why users might share a map and support its viraldissemination, as well as why some users are motivated tocreatemaps (both original andderivedworks) of their own.

Significance

This work provides a new framework to support the struc-tured analysis of viral maps. The elements of viral carto-graphy that we propose here fuse together traditionalelements of cartographic design with contextual andengagement attributes that correspond to the uniqueformof rapid andwidespreaddissemination that is possiblevia social media. We argue that the chaotic nature of howthis sharing takes place and the consequent reactions thatare possible whenmaps go viral representmajor challengesto existing methods for characterizing and evaluating mapdesign.

Through our case study examples we show how ourdesign framework can be applied to analyze recent viralmaps on Twitter. In these examples we have also shownhow a variety of analytical approaches can be applied toexplore viral cartography dissemination and social engage-ment.We believe the fact that viralmapsmay spark a rangeof derived maps as well as new maps in response opens anew area for further cartographic research to exploresocially-driven cartography.

We have also proposed a range of important researchquestions that academic cartographers should seek toaddress to better understand viral mapping. Answeringthese questions could help us understand how viral mapswork, how they can be captured, and how they could bedeliberately crafted. Establishing this knowledge could aidcartographers in designing maps that better leverage ourcontemporary means for dissemination and engagement.

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Finally, in light of recent nation-state actions to disruptpolitical processes by creating and disseminating fake news,we highlight the potential here that fake mapsmay alreadybe present in social media sources, either created manuallyor even created by automated processes.

Limitations

We recognize several important limitations in our presentwork. First of all, we have chosen only two case studies toexplore here, and have focused in on a specific social mediaplatform to do so. Our case studies may merely reflect thestate of viral cartography at a given moment, and weacknowledge that the nature of social media is in a con-tinual state of change, as is the ability for users to generatenew or derived maps.

Our work has also focused on English language exam-ples and political/cultural contexts that are specific to theUnited States. Therefore, our lessons may not be transfer-able to all global situations. Social media prevalence is notat all uniform around theworld, and neither is access to thetechnology necessary to engage with social media(Poushter, 2016).

Finally, we note the significant difficulties associatedwith finding and collecting all of the relevant data we seekin a project such as this one. As mentioned earlier, Twitterdoes not make replies easy to discover, and it is not feasibleto be truly exhaustive with searching via the public API.Additionally, viral phenomena often spawn responses out-side of the initial discussion thatmay have sparked interest,as nothing requires users to reply to an initial post or to usea common identifier that would aid later retrieval.

Conclusion

In this research we have contributed a new framework fordefining and analyzing the elements of viral cartography.We have applied this framework to two viral map casestudy examples, and we have analyzed the collection ofmaps created in response to those viral maps usingmachine learning. This work has prompted us to proposea set of new research questions that can guide future workby cartographers and geographers to better understandviral cartography.

In futureworkwe recommend testing our design frame-work with human coders, perhaps using a crowdsourcingapproach to evaluate a large corpus of viral maps gatheredfrom social media sources. While we found interestingresults from applying a black-box machine learningmethod on analyzing our map collections, we do not havea good sense yet of how well those characterizations wouldmatch what people may see. GCV, like many othermachine learning platforms, does not provide

documentation to explain how it works, therefore we donot know yet how it has been trained to recognize features,and it seems unlikely that it would have been carefullytuned to analyze maps in particular.

Viral map derivations appear to be a rich resource fromwhich future cartographic research can be developed.Specifically, derived maps could be individually comparedto the original viral map to examine differences in theircontent, symbolization, and other cartographic design ele-ments. It is also possible to envision a quality assessment ofviral map derivations that would shed light on the extent towhich best practices in cartographic design (debatable asthose may be) are followed.

We also encourage future research to focus on methodsthat would support analyzing viral maps in real-time. Ourwork here has been retrospective, but platforms like GCVhave already been widely applied to ingest and analyzestreams of images and we highlight the potential for oneto craft a geovisual analytics system that could couplecomputational methods for identifying structurally-viralcontent and machine learning for image analysis thatcould result in the real-time (or nearly so) detection andcharacterization of viral maps.

Continued research on viral cartography has the poten-tial to help mapmakers better leverage social media as ameans for map dissemination. Cartographers havestruggled to make maps that matter; maps that promptchange for the betterment of society and the environment,rather than simply react to those changes (Norwood &Cumming, 2012; Robinson et al., 2017), and understandinghow attention is directed in viral cartography could help usmeet that challenge in the future.

Acknowledgments

We acknowledge Ken Field and Jim Thatcher for helpfuldiscussions about the concept of a viral map. We also thankthe University of Salzburg Interfaculty Department ofGeoinformatics Z_GIS for providing an outstanding researchenvironment in which these ideas could be developed in 2017.

ORCID

Anthony C. Robinson http://orcid.org/0000-0002-5249-8010

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