Building protest online: engagement with the digitally networked #not1more protest campaign on Twitter

Abstract

This article examines engagement with digitally networked, politically contentious actions. Maintaining engagement over time is a key challenge for social movements attempting to network digitally. This article argues that proximity serves as a condition to address this challenge, because it configures the personal networks upon which transmission depends. This is a paradox of digital activism: it has the capacity to transcend barriers; however, proximity is essential for sustaining relations over time. Examining Twitter data from the #not1more protest campaign against immigrant deportations in the United States, quantitative and social network analyses show a differentiated development of engagement, which results in a particular geographical configuration with the following attributes. First, there is a robust and connected backbone of core organizers and activists located in particular major cities. Second, local groups engage with the campaign with direct actions in other cities. Third, a large and transitory contingent of geographically dispersed users direct attention to the campaign. We conclude by elaborating how this geographically differentiated configuration helps to sustain engagement with digitally networked action.

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Social Movement Studies
ISSN: 1474-2837 (Print) 1474-2829 (Online) Journal homepage: http://www.tandfonline.com/loi/csms20
Building protest online: engagement with the
digitally networked #not1more protest campaign
on Twitter
Sander van Haperen, Walter Nicholls & Justus Uitermark
To cite this article: Sander van Haperen, Walter Nicholls & Justus Uitermark (2018) Building
protest online: engagement with the digitally networked #not1more protest campaign on Twitter,
Social Movement Studies, 17:4, 408-423, DOI: 10.1080/14742837.2018.1434499
To link to this article: https://doi.org/10.1080/14742837.2018.1434499
© 2018 The Author(s). Published by Informa
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https://doi.org/10.1080/14742837.2018.1434499
Building protest online: engagement with the digitally networked
#not1more protest campaign on Twitter
Sandervan Haperena, WalterNichollsb and JustusUitermarka
aAmsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The Netherlands;
bDepartment of Urban Planning Policy and Design, University of California Irvine, Irvine, CA, USA
ABSTRACT
This article examines engagement with digitally networked, politically
contentious actions. Maintaining engagement over time is a key challenge
for social movements attempting to network digitally. This article argues
that proximity serves as a condition to address this challenge, because it
congures the personal networks upon which transmission depends. This is a
paradox of digital activism: it has the capacity to transcend barriers; however,
proximity is essential for sustaining relations over time. Examining Twitter
data from the #not1more protest campaign against immigrant deportations
in the United States, quantitative and social network analyses show a
dierentiated development of engagement, which results in a particular
geographical conguration with the following attributes. First, there is a
robust and connected backbone of core organizers and activists located
in particular major cities. Second, local groups engage with the campaign
with direct actions in other cities. Third, a large and transitory contingent
of geographically dispersed users direct attention to the campaign. We
conclude by elaborating how this geographically dierentiated conguration
helps to sustain engagement with digitally networked action.
Introduction
Los Angeles-based organizers launched a social media campaign in 2013 to forge a broad nationwide
coalition of immigrant rights activists, unions and other organizations in the struggle against the
deportation of undocumented migrants: the #not1more campaign. e campaign was designed to
facilitate open participation and digital networking, so that anyone could adopt and adapt the campaign
message to personal circumstances (Franco, Loewe, & Unzueta, 2015). With little top-down command,
participants could contribute to the campaign as desired, for example to organize local direct actions
using its slogan and imagery. Using the hashtag, information about such actions could then be shared
with the growing network to spread the campaign message online to others and inspire new actions.
Social media can be used to create and share content, helping to spread information far and wide,
fast. In the #not1more campaign, digital media were used to share information, support, slogans and
tactical repertoires through personal relations, in an increasingly broad nationwide network. It was,
in other words, a digitally networked campaign (Bennett & Segerberg, 2012).
© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://
creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the
original work is properly cited, and is not altered, transformed, or built upon in any way.
KEYWORDS
Networks; social movements;
social media; Twitter;
immigration; geography
ARTICLE HISTORY
Received 14 September 2016
Accepted26 January 2018
CONTACT Sander van Haperen s.p.f.vanhaperen@uva.nl
OPEN ACCESS
Social MoveMent StudieS
2018, vol. 17, no. 4, 408–423

SOCIAL MOVEMENT STUDIES 409
Initially a small group of organizers used Twitter as a tool to connect with activists across the
nation using unifying slogans and symbolism. Over the course of 20months, the campaign became
widely endorsed in immigration rights struggles, tying in closely with direct actions such as rallies,
sit-ins and blockades of detention centres across the nation. Eventually, widespread engagement with
the #not1more campaign contributed to a push for immigration reform leading up to controversial
executive action by President Obama on November 20, 2014, providing administrative relief for up
to 4.9 million immigrants (Nicholls, Uitermark, & van Haperen, 2016; ‘United States Department of
Homeland Security: Executive Actions on Immigration’ 2015). Employing a strategy of digital network-
ing, the campaign developed a coalition that was durable enough to push for substantial policy impact.
A key challenge in digitally networked action is to sustain engagement over longer periods of time
(Bennett & Segerberg, 2012, pp. 760–761; Tufekci, 2017). Engagement with digital action can take many
forms. In the case of the #not1more campaign, organizers sought attention for a particular frame: Not
one more deportation. Social media allow for the sharing and adaptation of this frame through personal
networks. e #not1more organizers intended to connect action in the highly fragmented eld of immi-
gration advocacy by employing a similar logic: casting ‘a broader public engagement net using interactive
digital media and easy-to-personalize action themes, oen deploying batteries of social technologies to
help citizens spread the word over their personal networks’ (Bennett & Segerberg, 2012, p. 742). Bennett
and Segerberg suggest that sustained engagement with digitally networked action depends on the trans-
mission mechanisms which enable the sharing of action frames through personal networks (2012, p. 754).
When transmission mechanisms fail, frames are not shared through personal networks, and connective
action breaks down. is article therefore discusses and analyses such transmission in more detail.
We suggest that the conguration of personal networks may be conducive or limiting to transmis-
sion mechanisms. e structural conguration of networks shapes transmission dynamics (Watts &
Strogatz, 1998). While relations within clusters of personal networks can be tight, digital networks as
a whole are typically loosely connected. Because relations in personal networks are congured around
social foci of geography and similarity (Baldassarri & Bearman, 2007; Centola, 2015; Feld, 1981), we
argue that proximity is a condition for transmission mechanisms in connective action. We therefore
emphasize geographical and social proximity in our empirical analysis, to understand how engagement
with digitally networked action is sustained.
Following review and discussion of literature and methods, an analysis of the overall development
of the campaign is presented. We rst show varying degrees of engagement, ranging from sending a
single message to engagement with direct actions. Second, we show how engagement is rooted in a
distinct geography: a backbone of core users is located in particular cities, local groups latching on
to the campaign are concentrated a range of locations and the network is complemented by a large
transient contingent of geographically dispersed users. ird, we show how social and geographical
proximity serves as a condition for transmission in digital networking: when people are far away they
are likely to develop relations if they share an aliation, when people do not share an aliation they are
more likely to develop relations with others living nearby. We conclude by elaborating on the paradox
of proximity in digital activism: while it has the capacity to transcend barriers, proximity congures
the networks undergirding digital interactions.
Proximity and transmission mechanisms in digitally networked action
Central to the #not1more campaign is the use of social media as a networking strategy. Organizer
Marise Franco refers to it as an ‘open source campaign’, seeking to use social media to connect activists
and organizations in the highly fragmented eld of immigration advocacy. rough years of experience,
organizers know well the functioning of activist networks throughout the country, the established
organizations and unions, the policy makers and media landscapes. e campaign emphasizes open
participation through digital media to mobilize personal networks. As such, it can be understood as
an instance of organizationally enabled digitally networked action.

410 S. VAN HAPEREN ET AL.
e concept personal action frame is key to digitally networked action, as it draws a distinction
between the traditional logic of collective action (Olson, 1965) and the logic of connective action
(Bennett & Segerberg, 2012). e logic of collective action analyses action as the unied outcome
of resource concentrations, structural features or the formation of collective identities. Accordingly,
collective action frames are conceptualized as an alignment of meaning structures such as experiences
(Benford & Snow, 2000, p. 623), or claims about injustice, agency and identity (Gamson, 1995). By
contrast, the logic of connective action emphasizes the sharing of personal action frames on digital
media networks and does not presuppose frame alignment. Against the backdrop of increasingly
fragmented and individualized societies, engagement with politics develops around personal action
frames: individualized orientations that are expressions ‘of personal hopes, lifestyles, and grievances’,
rather than collective action frames: expressions of ‘social group identity, membership, or ideology’
(Bennett & Segerberg, 2012, pp. 743–744). More than was possible before, social media enable fast
and far-reaching transmission of individual expressions without requiring the adoption of unifying
collective action frames. e resulting connective action emphasizes the aggregation of diverse expres-
sions of identity, rather than alignment of meaning structures or the forging of collective identity.
A key challenge in digitally networked action is to sustain engagement over longer periods of time
(Bennett & Segerberg, 2012, pp. 760–761; Tufekci, 2017). Understanding how engagement is sustained
in digital networking calls for analysis of the ‘transmission mechanisms involved’ (Bennett & Segerberg,
2012, p. 747). As with the sharing of personal action frames, engagement with connective action is a
relational act: interacting with others about individual orientations. Digitally networked action cannot
be sustained when transmission fails and personal action frames are not shared in personal networks.
e medium, for instance Twitter, may serve to bridge barriers at ‘the intersections of social networks
dened by established political organizations, ideologies, interests, class, gender, race, or ethnicity’
(Bennett & Segerberg, 2012, p. 747). According to Bennett and Segerberg, digital technology can be
thought of as a transmission mechanism because it enables the sharing of frames (2012, p. 754). More
specically for the case of organizationally enabled digitally networked action, transmission depends
on ‘a stable core of organizations sharing communication linkages and deploying high volumes of
personal engagement mechanisms’ (Bennett & Segerberg, 2012, p. 761). ey suggest that sustaining
connective action depends to some degree ‘on the kinds of social technology designed and appropri-
ated by participants, and the kinds of opportunities that may motivate anger or compassion across
large numbers of individuals’ (Bennett & Segerberg, 2012, p. 754). In our understanding, the term
‘mechanism’ refers to the digital networks used to share frames, such as the social medium Twitter,
and ‘transmission’ refers to the act of sending frames.
We argue that proximity can be conducive or limiting to the transmission of personal action
frames. is argument is prompted by research showing that the conguration of networks shapes
transmission dynamics (Watts & Strogatz, 1998), and by research demonstrating that proximity con-
gures social relations (Conover et al., 2013; Feld, 1981; McPherson, Smith-Lovin, & Cook, 2001;
Rivera, Soderstrom, & Uzzi, 2010). Accordingly, we believe that proximity is a condition in digitally
networked action. Because it congures the personal networks upon which transmission depends,
proximity should be taken into account when analysing digital networking.
Potentially, digital networking diminishes geographical constraints on the development of a protest
campaign. Digital media provide particular aordances which are leveraged for networking by sharing
personal action frames. Aordances are ‘possibilities for action’ (Evans, Pearce, Vitak, & Treem, 2017,
p. 36) provided by digital media, arising from the relation ‘between an object/technology and the user
that enables or constrains potential behavioural outcomes in a particular context’ (Evans et al., 2017, p.
36). More specically, Twitter provides the aordance of visibility which is crucial to the possibilities for
connective action it provides activists. Visibility serves the expression of personal action frames as a form
of engagement. Easily creating and sharing messages facilitates the visibility of ideas and orientations
pertinent to the campaign, by reducing informational transaction costs (Coiera, 2000), creating common

SOCIAL MOVEMENT STUDIES 411
ground and maintaining relationships (cf. Evans et al., 2017; Vitak, 2014). Twitter can be used to create
and broadcast action frames, which in turn can be adapted and rebroadcast easily throughout increasingly
further reaching personal networks. In this way, engagement in the form of creating and sharing person-
alized content enables ‘coordinated adjustments and rapid action aimed at oen shiing political targets,
even crossing geographical and temporal boundaries in the process’ (Bennett & Segerberg, 2012, p. 753).
Potentially, this facilitates viral diusion of information, inspiring others to plug in to the campaign and
undertake local direct actions elsewhere. To engage with the #not1more campaign, activists in one place
can use Twitter to easily create and share content, spreading action frames quickly, far and wide to others.
Despite the potential of social media for networking with others far and wide, prior research
establishes proximity as a key conguring element of personal networks (Centola, 2015; Feld, 1981;
Kossinets & Watts, 2006; McPherson et al., 2001; Onnela, Arbesman, González, Barabási, & Christakis,
2011). Proximity eects on digital networks can be geographical (Borge-Holthoefer et al., 2011; Borge-
Holthoefer, González-Bailón, Rivero, & Moreno, 2014; Conover et al., 2013; Nicholls, 2009) or interest
based (González-Bailón, Wang, & Borge-holthoefer, 2014; Tremayne, 2014; Tremayne, Zheng, Lee, &
Jeong, 2006). Moreover, connectivity in digital networks is generally highly uneven (Borge-Holthoefer
et al., 2011; Borge-holthoefer, Magdy, Darwish, & Weber, 2015; González-Bailón, Borge-Holthoefer,
& Moreno, 2013; Tremayne, 2014; Varol, Ferrara, Ogan, Menczer, & Flammini, 2014). People tend to
sustain interaction more readily with others who are like them and geographically nearby. Accordingly,
we argue that proximity is relevant in the conguration of personal networks, and because digital net-
working depends on transmission of frames through personal networks, we examine how proximity
operates as a condition for transmission in digital networking.
To summarize, the #not1more campaign is an instance of connective action. Social media are
leveraged in an eort to organize a nationwide coalition among a broad range of activists and organi-
zations. e key challenge in digitally networked action is to sustain engagement over time. To address
this challenge, we examine how proximity operates as a condition for transmission in the personal
networks upon which connective action depends. While the aordances of digital media potentially
help activists to transcend boundaries, proximity is a key to the conguration of personal networks.
is is a paradox of digital activism: while digital communication technologies are particularly well
suited to enable the sharing of personal action frames and aord the capacity to dissolve spatial bar-
riers, proximity is essential for sustaining relations over time.
Data and methods
Twitter data
To study engagement with the online #not1more campaign, data were collected from Twitter. e
dataset consists of tweets with the hashtag #not1more, posted in the period between January 2013
through August 2014, 20months aer the hashtag rst appeared on Twitter in relation to immigration
and deportations. is hashtag was selected aer preliminary analysis of hashtags related to immigrant
rights struggles, sampling for volume and topic specicity. Tweets were made publicly available by
Twitter through its API with the consent of its users (Twitter Inc, 2014). As per the Twitter terms of
service, personal information with which individuals or groups might be identied was anonymized.
e dataset consists of 108,198 tweets from 16,113 unique user accounts. Each of these users repre-
sents a node in our network analysis, each mention and retweet between them represents a directed tie.
A user is understood to be ‘active’ when they tweeted within the given period under analysis. ere were
168,393 directed ties among all nodes. Analyses were based on 15,019 reciprocal, undirected edges.
ere are signicant limitations to the use of Twitter data in social movement research. Four inter-
related concerns are recognized and stressed here: inference, power inequalities, representativeness and
ethics. First and foremost, scholarship has pointed out that online participation and activity cannot be
equated with a social movement pars pro toto (Flesher Fominaya & Gillan, 2017). As this article focuses
on a specic Twitter campaign, it analyses activity on Twitter. We think of this as an instance of digitally

412 S. VAN HAPEREN ET AL.
networked action situated within much broader social movement phenomena about which we make no
general claims. Second, broadly speaking, digital methods commonly ignore power imbalances involved
in the lived experience, media ecology and use of social media (Flesher Fominaya & Gillan, 2017; cf.
Juris, 2012; Tufekci, 2014). We note that adequately addressing this concern would require a dierent
kind of research design that includes thorough qualitative inquiry, which is beyond the scope of the
current study. Extensive prior ethnographic eldwork and familiarity of the authors with immigrant
rights activism as well as the use of digital media goes some way to abate this concern, although analyses
do not explicitly stress this in the current article. ird, and more specically, power inequalities are
reected in the demographic of Twitter users, which poses serious concerns about representativeness
(González-Bailón, Wang, Rivero, Borge-Holthoefer, & Moreno, 2014; Mislove, Lehmann, Ahn, Onnela,
& Rosenquist, 2011; Tufekci, 2014). ese biases are exacerbated by reliance on a single platform and
single hashtag. Given the centrality placed by the campaign’s organizers on this specic platform and
particular hashtag, we think this focus is justied. While the Twitter demographic (or geographical
distribution thereof) is certainly not representative for everyone involved in immigrant rights strug-
gles, users of the #not1more hashtag can be understood to be involved in digital networking as part
of this particular campaign. Fourthly, inequalities give rise to ethical concerns beyond Twitter’s legal
terms of service, which stipulates the use of data and consent but not risk of harm to users (Moreno,
Goniu, Moreno, & Diekema, 2013). It cannot be assumed that users are aware that their digital activity
is published in research. To address this, no information by which users can be identied is reported
in this article, and analyses are concerned with aggregate levels.
Patterns of engagement: activity, connectedness, persistence
e #not1more campaign provides an opportunity to examine the transmission of personal action
frames in digitally networked action empirically. People engage with the #not1more campaign by
creating and sharing personal expressions. On Twitter, transmission of frames takes the form of
posting, mentioning and retweeting messages. To examine patterns of engagement with the campaign
online, we analysed activity and connectedness. Because we are interested in how such engagement is
sustained, we also examined persistence. e analysis emphasizes transmission as sending of frames,
over the meaning of individual tweets in terms of a user’s perception or adoption. We assume that the
inclusion of the #not1more hashtag signals a degree of awareness of the campaign.
Activity dierentiates users who engage very oen from those who tweet only sporadically. It was
measured individually as a user’s number of tweets, and cumulatively as tweet volumes at dierent
times. Cumulative measures were used to identify the campaign’s most active locations. For every
month in the dataset, the number of active unique users was calculated for every location. e user
base for which a location accounted is dened as the number of active users in a city, divided by the
total number of active users. is measure is reported for periods of 3, 12 and 20months.
Connectedness dierentiates users who engage online with many others in the campaign, from
those who do not. is concerns transmission in terms of reciprocation. It was measured individually
as a user’s reciprocated ties, and cumulatively as degree distributions over time. Cumulative degree
distributions serve as an indication of how concentrated relations are in a core of activists, or shared
more evenly among all participants. is was calculated as the power law exponent using the method
proposed in (Clauset, Shalizi, & Newman, 2009).
Persistence dierentiates dedicated users who remain engaged with the campaign for a long time
from more ephemeral users. is concerns transmission in terms of the relation between sending per-
sonal action frames and subsequent ongoing engagement with the campaign. A user was considered
a new recruit on the rst day someone tweeted with the #not1more hashtag. Individual persistence
was calculated as the proportion of days remaining in our dataset aer this rst tweet. Cumulative
persistence was measured as consistent activity in a particular location. To determine the turnover of
a location’s user base, we calculated the dierence between the maximum and minimum number of
unique monthly active users, divided by the average number of users per month (for the full 20-month

SOCIAL MOVEMENT STUDIES 413
period) in each city. e resulting normalized measure is the factor by which the spikes of user activity
are removed from the average number of users, with zero indicating a perfectly constant user base,
and higher values indicating a more inconsistent user base.
To understand what generates engagement with the campaign, we examined events referenced
in tweets during particular peaks of activity. We dierentiated local and global events. We think of
direct actions related to the campaign as local events. First, the tactical repertoire of the campaign was
derived from manual analysis of tweet captions and prior ethnographic research (Nicholls & Fiorito,
2015; Nicholls & Uitermark, 2017). is led to a lexicon of relevant direct actions: ‘blockade’, ‘march’,
‘demonstration’, ‘rally’, ‘vigil’, ‘petition’, ‘heckle’, ‘banner’, ‘occupation’, ‘sit-in’, ‘undocubus’ (referring to
activists touring the United States in a bus), ‘hunger strike’ and ‘disobedience’. Second, every tweet in
the dataset was referenced against this lexicon to identify actions reported on Twitter. ird, to avoid
duplicate counts, this set of actions was validated manually. Every direct action referenced in tweets
was coded for location, type of action and topic, by examining text and photos in tweets. Where
available, hyperlinks in these tweets were followed to conrm event announcements and websites.
is procedure yielded 439 direct actions in the 20-month period under examination. While certainly
not an exhaustive list of local events related to immigrant rights struggles in the period under study, it
does provide good coverage of actions that are referenced in the dataset of #not1more tweets.
Proximity
To examine the conguration of online personal networks in the #not1more campaign we deter-
mined social and geographical proximity. e eect of proximity on engagement was calculated as
the percentage of reciprocated ties in the empirical network where users share a location or aliation.
is was compared to simulated permutations of the network. One hundred network permutations
were created by randomly reassigning aliations and locations among users in an identical network
structure. e percentage of ties with shared location or aliation was the average of all permutations.
is average indicates what proximity eects might be expected at random, and served as a baseline
for comparison of the empirical eect.
e term aliation is used as a shorthand to indicate social proximity based on interests, and was
derived from the self-described individual or organizational biography of an account. We followed a
basic semi-supervised procedure. First, categories were inferred from manual examination of 1612
self-reported user proles. is sample (10%) was randomly selected from the complete user base.
Coding yielded 23 categories (e.g. DREAMers, unions, faith-based organization) with a lexicon of
corresponding keywords (e.g. ‘dream’, ‘union’, ‘church’ respectively). Second, every prole in the dataset
was referenced against this lexicon. is semi-supervised procedure assigned aliations to 10,943
users (68%).
Geographical proximity is dened as the geodesic distance between the last known coordinates of
two network nodes sharing a tie. Users who are located up to 30 miles from each other were coded
as being ‘nearby’. User locations were derived from the self-reported bio and location elds on public
Twitter proles, rather than the opt-in coordinates at tweet-level which only 0.2% of tweets include.
Self-reported locations were geocoded using the Google Maps Geocoding API (Google, 2014; Kahle &
Wickham, 2013), which resolved ambiguous, misspelled and colloquial names, (‘DC’ to Washington,
D.C.), and returned longitude and latitude coordinates as well as a measure of accuracy. is measure
of accuracy was used to lter out extraneous results such as ‘USA’ or ‘Earth’. Given the campaign’s focus
on a national policy debate, we restricted analysis to locations in the US.1 To validate that automated
geocoding achieved higher than the 85% accuracy deemed necessary for statistical reliability (Ratclie,
2004), the location of 870 proles was validated manually by cross-referencing usernames from the
dataset with current biographical information available online. is procedure yielded accurate loca-
tions for 3116 users in the United States (19% of all users).

414 S. VAN HAPEREN ET AL.
Results
Patterns of engagement
While the hashtag is used by a small group of users shortly aer its introduction, activity related to the
#not1more campaign on Twitter increases over time. More people become involved and more tweets
are sent as time progresses. is activity develops in peaks of tweet volumes, which become more fre-
quent in later stages of the campaign (Figure 1). is increasing number of users adopting the hashtag
suggests that engagement with the campaign was sustained over time among an increasingly broad user
base, while uctuating activity suggests the campaign was driven by consecutive bursts of attention.
Activity is based in specic locations, generating a distinct geographical pattern to online engage-
ment with the #not1more campaign. Users exhibit dierent levels of tweeting activity in dierent
locations. e proportion of users as well as the trac that is generated varies per state. Striking
dierences between California and the District of Columbia stand out. In California, 23.0% of users
generate 18.0% of trac volume, whereas in D.C. 3.7% of users generate 13.7% of trac. Figure 2
shows the number of unique users by county and illustrates that users in the campaign are mostly
concentrated in metropolitan areas.
ese results indicate that the campaign is driven by varying degrees of engagement in dierent
places. Core users, who are highly connected, persistent and account for large proportions of trac,
are concentrated in specic places. California is home to a tapestry of community-based groups and
individual activists, while Washington, D.C. harbours large advocacy organizations that have dedicated
resources to tweeting routinely and prolically (Nicholls et al., 2016). Zooming in closer on the top
30 cities that form the campaign’s hubs (Table 1), we nd that four urban areas harbour the core users
and together account for 35.0% of the entire user base: Washington, D.C., New York, Los Angeles and
Chicago. ese four places are the campaign’s hubs.
In terms of persistence, the user bases in the four campaign hubs are not only disproportionately
large, they are also more consistent at 1.9 in comparison to the average turnover of 2.9 of all cities with
more than nine users active in a month. ere are 22 additional cities with more than nine active users
per month, such as San Francisco, Austin, Seattle and Philadelphia, together accounting for 23% of the
user base. e campaign’s user base in these cities is less consistent than in the hubs (3.3). Cities other
than the hubs are less consistent and show more distinct spikes in user activity. e cities with the most
volatile patterns are Tacoma, New Orleans, Kansas City, Salt Lake City and Boston. Here, sudden spikes
Figure 1.Number of tweets per day.

SOCIAL MOVEMENT STUDIES 415
Figure 2.Number of unique users by county after 20months.
Table 1.Turnover and size of user base per city over time.
Note: Correlation of turnover and average proportion of user base: −0.401.
City Total users Turnover % after 3months
% after
12months
% after
20months % Average
Washington, DC 1462 1.97 9.3 6.74 9.78 8.61
New York, NY 1441 1.71 10.57 9.98 9.89 10.15
Los Angeles, CA 1231 1.72 10.27 9.49 8.93 9.56
Chicago, IL 922 2.3 5.29 6.95 6.24 6.16
San Francisco, CA 479 1.96 2.3 3.29 3.18 2.92
Austin, TX 306 3.99 1.34 1.3 1.8 1.48
Seattle, WA 289 3.67 1.92 1.46 1.8 1.73
Philadelphia, PA 284 3.17 0.79 1.41 1.66 1.29
Boston, MA 280 4 2.35 1.77 1.87 2
Phoenix, AZ 275 1.75 4.38 2.75 2.24 3.12
Oakland, CA 249 1.85 1.53 1.82 1.7 1.68
Miami, FL 230 1.48 1.79 1.61 1.69 1.7
Houston, TX 218 2.39 2.25 1.39 1.52 1.72
Atlanta, GA 176 2.39 0.62 0.89 1.13 0.88
San Diego, CA 150 2.13 0.89 1.05 1.01 0.98
Dallas, TX 120 2.17 1.24 1.01 0.94 1.06
Tucson, AZ 113 3.36 0.57 0.8 0.75 0.71
Minneapolis, MN 107 3.18 1.36 0.78 0.76 0.97
Denver, CO 92 2.17 0.81 0.69 0.66 0.72
Portland, OR 92 2.39 0.38 0.5 0.57 0.48
New Orleans, LA 89 5.39 0.62 0.66 0.59 0.62
Charlotte, NC 79 2.78 0.41 0.49 0.55 0.48
San Antonio. TX 78 2.82 1.15 0.57 0.58 0.77
Las Vegas, NV 77 3.9 0.79 0.52 0.54 0.61
Columbus, OH 75 1.87 0.91 0.68 0.58 0.72
London, UK 67 2.39 0.26 0.34 0.42 0.34
Sacramento, CA 61 2.3 0.53 0.39 0.41 0.44
Salt Lake City, UT 60 4.67 0.43 0.28 0.37 0.36
Kansas City, MO 47 4.68 0.1 0.24 0.28 0.21
Tacoma, WA 26 7.69 0 0.03 0.12 0.05
Average 305.83 2.94 2.17 2 2.09

416 S. VAN HAPEREN ET AL.
in the size of the user base stand in stark contrast to the national average turnover as well as to what
might be expected from prior and subsequent activity in these cities. A further 1016 cities accommodate
the remaining 39.0% of users, none of which have more than nine users active in a month. ese cities
have an average turnover of 7.2. is pattern indicates that the user bases in particular cities is much
more consistent, harbouring the core users of the campaign who remain persistently active. Other cities
have more volatile turnover, where attention is more subject to bursts generated by one or a few events.
Some activists have more online connections in the campaign than others. roughout the cam-
paign a core of strongly connected users is complemented by users who interact less intensively and
less persistently. Engagement, in terms of connectivity, is distributed unevenly: a few core users are
highly connected, while the majority of users have only a few ties. A perfectly even distribution of ties
would indicate that everyone has exactly the same number of interactions. In the #not1more cam-
paign, many users interact with just a few others, while only a few interact with many others. is is
reected in a power law exponent of approximately 2.07. Figure 3 expresses this uneven distribution
Figure 3.(a) Connectedness over time: cumulative indegree distributions after 3months (light), 12months, 20months (dark); (b)
Connectedness over time: cumulative outdegree distributions after 3months (light), 12months, 20months (dark).

SOCIAL MOVEMENT STUDIES 417
as the probability of randomly selecting a node with more than a certain number of interactions. is
uneven structure is established early on: aer the rst month the top four targets (1.0%) account for
20.0% of incoming trac and the top three (1.0%) generate 31.6% of all outgoing trac. With regard
to in-degree at maximum system size, the top 1.0% of users (88 out of 8,796) receive 55.1% of all
directed ties (92,851 out of 168,393 total) and the top 10 users (0.1%) account for roughly a quarter
of all received ties (24.8%). is pattern is less pronounced for out-degree at maximum system size
with the top 1.0% of users directing 38.6% of all ties to others, and the top 10 users (0.1%) accounting
for 8.9% of outgoing ties. ese ndings are in line with other analyses of digital networking and
suggest that the #not1more campaign is topologically similar to other protests playing out on Twitter
(Borge-Holthoefer et al., 2011; Conover, Ferrara, Menczer, & Flammini, 2013; González-Bailón, Borge-
Holthoefer, Rivero, & Moreno, 2011).
is pattern of engagement, peaks of activity that are generated by a dierentiated user base and
rooted in a distinct geography, can be better understood when examining events at particular peak
times, as illustrated by some examples. Online attention for the #not1more campaign is driven by both
national and local events, generated by national media coverage of related topics and local actions
in places like Tacoma and Boston (Table 1). One example of a local peak takes place in March 2014
in Tacoma. At that time, Tacoma’s ICE Northwest Detention Center was the site of a massive hunger
strike, and rallies and fasts were held throughout the city. Almost 1200 people went on hunger strike,
generating a lot of national media attention as well as many tweets. In and around Tacoma, 45 new
recruits latch on in relation to these actions. On average, these recruits engage persistently (0.4% of the
remaining campaign). Another example of a local peak takes place in Boston around 17 April 2014,
marking the Boston ‘Not 1 More’ rally, the blockade of the Suolk County House of Corrections and the
subsequent arrest of 19 activists. is day of action generated 35 new recruits in Boston. On average, they
are relatively persistent: 0.2% of the remaining campaign. ese two examples show that direct actions
generate a lot of local activity and draw in recruits who engage relatively persistently with the campaign.
Other peaks of attention are generated by national events, which generate a more diuse pattern,
with less persistent and more geographically dispersed recruits. 17 December 2013 stands out as one
such peak. On this day, there were 182 new recruits nationwide, 178 of whom referenced an announce-
ment about a radio interview with an NDLON organizer. e radio station is located in New York
City, but has a national audience thanks to its online broadcasts. One hundred and forty of these new
recruits only remained active for one more day, and none longer than three days. Recruits hail from
across the nation, without any clear concentrations in particular locations. Another example of how
a national event is tied in to the #not1more campaign is President Obama’s annual State of the Union
address on 29 January 2014. On this day, there were 98 new recruits. e address generated a lot of
tweets containing the hashtag, most prominently retweets of a message urging Obama to consider
deportations as part of his legacy. Recruitment was not concentrated in any particular area, and most
recruits (79 out of 98) were not persistent. ey sent a single message, only on the day of the address.
eir contribution was isolated and short-lived, simply retweeting a single action frame.
ese examples illustrate that local and global events generate dierent kinds of engagement.
Localized bursts of engagement are generated by local direct actions. Users outside of the major cam-
paign hubs plug in to the campaign with actions in their own locality, generating local peaks of activity
(Table 2). Furthermore, we nd that users whose rst tweet concerns a direct action, and are based in
the same location as that action, are more persistent, have more local contacts and a slightly higher
number of overall contacts than other recruits. is pattern of engagement can be dierentiated by
type of action: some types of direct action generate more, and more persistent, recruits.
Dierent tactical repertoires generate varying levels of engagement with the campaign. In absolute
frequencies, the tactic of choice is rallies, followed by marches and vigils. In terms of recruitment
rate (the number of new recruits among users tweeting about an action), occupations, hunger strikes
and blockades are most successful in drawing in new recruits. ese repertoires were most salient
for people to start using the #not1more hashtag. However, in terms of commitment to the campaign,
marches and hunger strikes generated the most persistent new recruits online. ese ndings suggest

418 S. VAN HAPEREN ET AL.
that users who become involved by tweeting about local direct actions are more persistent than the
average users and have a greater degree of connectivity. In addition, some types of action generate
more, and more persistent recruits.
Transmission mechanisms of engagement
We now turn our attention to how users engage with each other in the #not1more campaign, focusing
on proximity as a condition for transmission. While a lot of interaction in the campaign occurs between
people who live in close proximity, there are also many interactions between people across the nation.
e structure of trans-local ties between users (Figure 4) is organized along the campaign’s met-
ropolitan hubs: primarily Los Angeles, Washington, D.C., New York City and Chicago. Ties between
Table 2.Engagement by type of direct action.
Notes: Occurrences: the number of times this type of action occurs during the period. Tweets: the number of tweets that contain
reference to this type of action during the period. Accounts: the number of accounts that tweet at least once with reference to
this type of action during the period. Recruitment: the number of new recruits, whose first tweet contains reference to this type
of action, and the percentage of new recruits over all users tweeting about this type of action. Persistence: the adjusted average
remaining period that new recruits remain active after first tweeting about this type of action.
Action Occurrences Tweets Accounts Recruitment Persistence
Rally 121 1128 718 229 (32%) 1.34
March 62 1605 818 237 (29%) 3.47
Vigil 54 642 387 96 (25%) 1.8
Blockade 28 374 307 135 (44%) 1.22
Petition 27 1415 854 330 (39%) 0.99
Hunger strike 26 2360 1251 574 (46%) 2.8
Banner 16 255 179 39 (22%) 1.51
Sit-in 12 222 195 60 (31%) 0.6
Demonstration 4 52 41 7 (17%) 1.71
Occupation 4 629 450 218 (48%) 0.56
Heckle 3 90 68 17 (25%) 1.24
‘Undocubus’ 2 71 65 9 (14%) 0.22
Disobedience 1 444 318 90 (28%) 1.96
Total 360 10,929 6700 2472
Figure 4.Geography of reciprocated ties.

SOCIAL MOVEMENT STUDIES 419
these hubs account for 32.9% of reciprocated ties. is results in a pattern that resembles a hub and
spoke network, similar to ndings in other studies (Conover et al., 2013; Hemsley & Eckert, 2014).
To analyse the eect of proximity on the conguration of personal networks, we compare the
empirical network with randomized simulations of the network. Table 3 shows the percentages of
ties connecting nodes of the same aliation and location. In the #not1more campaign, users tend to
engage with each other when sharing background and shared locations, more than might be expected
at random.
Even with the network structure le intact in randomizations of the node attributes, the pattern of
aliation and location is striking in the empirical network when compared to random permutations.
ese ndings conrm that participants in the campaign (1) traverse geographical distance when
socially proximate and (2) traverse social distance when geographically proximate.
Conclusions
How is engagement sustained in digitally networked action? Based on the #not1more campaign that
began in 2013 in the United States protesting against deportations, we have examined this question
using quantitative, geographical and social network analyses of Twitter data. We nd a dierentiated
development of engagement rooted in a distinct geography. Proximity, the condition generating this
distinct geography, is shown to sustain engagement to varying degrees. While digital media may help
activists to transcend boundaries easily, the personal networks, upon which transmission depends,
remain congured primarily around place and similarity.
Our research shows a dierentiated engagement with digital networking. e backbone of the
#not1more campaign consists of highly active, well-connected and persistent core organizers and
activists, located in particular major cities. Local groups of activists plug in to the campaign, engaging
with direct actions. ere is a range of locations that show episodic bursts of recruitment in relation to
local direct actions. Cities such as Boston show sudden spikes of localized recruitment at specic times.
is hub-and-spoke structure is complemented by a large contingent of transitory and geographically
dispersed users who direct their attention to points of conict. Engagement with the campaign is
generated by both national and local events.
Our ndings show that the dynamics of contention online are dierentiated by existing structures of
proximity. Scaling up, in terms of active and persistent engagement with the campaign, can be related
to proximity. National events such as the State of the Union address reach broad and geographically
dispersed publics. Someone who witnesses a rally or March in their home town is likely to engage
in a more active, connected and persistent manner than someone who engages with the campaign
in relation to national events. Moreover, there are dierences in the level of persistence generated by
dierent types of local actions.
With regard to the structure of personal networks in this digitally networked campaign, we nd
a polycentric structure also congured by geographical and social proximity. Conover et al. (2013)
found that relations across space are established through the sharing of slogans and relations within
a place are established through the sharing of resources. Our ndings suggest a dierent, though
complementary condition: geographical proximity makes it more likely that social distance is bridged,
and social proximity helps to bridge geographical distances. is helps to understand how digitally
Table 3.Effect of proximity in empirical versus random networks.
Same aliation (%) Dierent aliation (%) Total (%)
Same location Empirical: 9.0 Empirical: 29.5 Empirical: 38.5
Random: 0.5 Random: 0.6 Random: 1.1
Different location Empirical: 11.3 Empirical: 50.1 Empirical: 61.41
Random: 1.5 Random: 97.5 Random: 99.0
Total Empirical: 20.3 Empirical: 79.6
Random: 2.0 Random: 98.1

420 S. VAN HAPEREN ET AL.
networked action can be sustained despite the ephemeral nature of open participation. e #not1more
campaign was propagated, particularly in its early stages, by a core of activists who had developed
strong pre-existing ties among themselves from intensive contact on the basis of proximity.
e #not1more campaign is in some ways unique and should not be conated with social movements
in general. We believe that proximity has always been of importance to the conguration of networks in
social movements, but that digital data allow for new ways to analyse these congurations empirically.
As an instance of digitally networked action, insight into proximity as a condition for transmission
can inform analysis of social movements emerging on the interface of urban and online spaces, such as
Black Lives Matter. Based on our ndings and insider accounts (Juris, 2012; Schneider, 2012; Schwartz,
2011), we would expect to nd the same pattern in other digitally networked movements: dierentiated
involvement, and a core group of activists who drive the campaign (cf. Lee & Chan, 2016). Our nd-
ings show that the dierentiated activity is rooted in geography, with connections forged within and
between particular cities (Nicholls & Uitermark, 2017). Writing about the diusion of sit-ins in 1960,
Andrews and Biggs found ‘little evidence that social networks acted as a channel for diusion among
cities’ (2006, p. 752). In the #not1more campaign, these channels are demonstrably provided by social
media mechanisms. We would further expect that the development of other digitally networked move-
ments is similarly inuenced by proximity as a condition for transmission that produces dierentiated
engagement. In short: geography remains of importance in the conguration of social relations in
digital networking. Some scholars have suggested that place-specic qualities become more important
as the friction of distance decreases (Sassen, 1991; Storper, 1997) and this might also be true for social
movements. An avenue for further research is to examine proximity and transmission of personal
action frames in terms of reception and amplication of meanings. Analysis of personal action frames
that takes into account substantive content would be an important step in that direction, which might
be inspired by theorization of collective action frames geared to meaning structures (Benford & Snow,
2000; Gamson, 1995). is would allow for consideration of digital networking as the mimetic mech-
anisms discussed in terms of scaling up (McAdam, Tarrow, & Tilly, 2001; Tarrow & McAdam, 2004)
and diusion (Andrews & Biggs, 2006; Chabot & Duyvendak, 2002; Givan, Roberts, & Soule, 2010).
While social media allow activists to digitally network with others far and wide, solidarities tend to
emerge in accordance to location and interest. We believe that geographical and social proximity pro-
vides sucient levels of solidarity needed to ensure some stability within these movements. Proximity
continues to be crucial to the conguration of the networks in digital activist campaigns. Herein lies the
paradox of digital activism: while it has the capacity to dissolve spatial barriers, proximity is essential
for sustaining relations over time.
Note
1. Geodesic distances are calculated using the R geosphere package (Hijmans, 2016). Coordinates are reverse
geocoded to county levels (with FIPS number) in the United States using the latlong2state functionality of the
Data Science Toolkit API (Elmore & Heiss, 2014) and plotted in R using the choroplethr package (Lamstein &
Johnson, 2014).
Acknowledgements
We are grateful for insightful and constructive comments of the anonymous referees.
Disclosure statement
No potential conict of interest was reported by the authors.
Funding
e authors received no specic grants for this work.

SOCIAL MOVEMENT STUDIES 421
Notes on contributors
Sander van Haperen is PhD candidate at the Amsterdam Institute for Social Science Research, Department of Sociology,
University of Amsterdam. He is interested in governance and the development of social movements, digital networks,
and leadership. Recent work includes “e networked grassroots. How radicals outanked reformists in the United
States’ immigrant rights movement” (with Walter Nicholls and Justus Uitermark, published by JEMS, 2016).
Walter Nicholls is Associate Professor of Urban Planning and Public Policy at the University of California, Irvine.
Nicholls is interested in social movements, urban governance and immigration politics. His books include “Cities and
Social Movements” (with Justus Uitermark, published by Wiley, 2017) and “e DREAMers” (published by Stanford
University Press, 2013).
Justus Uitermark is Associate Professor of sociology at the University of Amsterdam. Uitermark is a political sociologist
interested in urban governance and social movements. His books include “Dynamics of Power in Dutch Integration
Politics” (published by University of Amsterdam Press, 2012) and “Cities and Social Movements” (with Walter Nicholls,
published by Wiley, 2017).
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