Embed Size (px)
Citation preview
1
Social Media Influence and Electoral Competition
Yotam Shmargad Lisa Sanchez
University of Arizona University of Arizona
[email protected] [email protected]
ABSTRACT
Do social media platforms help or hinder democracy? Internet enthusiasts posit that social media
could have a democratizing effect by lowering the costs of promotion, while skeptics argue that
these platforms replicate or even exacerbate pre-existing inequalities. We inform this debate by
combining campaign finance and electoral outcome data from the Federal Election Commission
with Twitter metrics of candidates who ran in the 2016 U.S. congressional elections. We find
that poorer candidates, who spent less than their competitor, performed better if they had indirect
influence on Twitter – getting their tweets shared by users whose own tweets are widely shared.
The effect of indirect influence on election outcomes was more pronounced in races with larger
financial inequities between candidates or fewer total expenses across candidates. Moreover,
poorer candidates with indirect influence saw smaller vote gaps than their party’s candidate in
the same district (in House races) or state (in Senate races) in 2014.
Keywords: Campaign Spending, Indirect Influence, Political Elections, Twitter, Two Step Flow
2
1. INTRODUCTION
Social media platforms facilitate information exchange between voters (Bond et al. 2012)
and provide new ways for politicians to engage with their constituents (Golbeck et al. 2010).
Because messages can travel quickly and broadly on social media (Nahon and Hemsley 2013),
these platforms have become a widely used tool for political campaigning (Gulati and Williams
2013). In the 2016 U.S. presidential race, for example, Hillary Clinton and Donald Trump both
spent considerable efforts to infiltrate social media with their respective messaging. By many
measures, Trump had the upper-hand on social media despite his relative disadvantage in terms
of campaign funds raised (Allison et al. 2016), money spent on television ads, and time devoted
to face-to-face canvassing (Easley 2016). He maintained a 30% larger following on Twitter than
Clinton, received twice as many Facebook likes (10 million compared to Clinton’s 5.2 million),
and had over 20% more followers on Instagram than Clinton (2.2 million versus 1.8 million for
Clinton). Aware of this discrepancy, Trump had this to say in an interview for the CBS news
program 60 Minutes, just days after the election: "I think that social media has more power than
the money [Clinton’s campaign] spent, and I think maybe to a certain extent, I proved that”
(Morin 2016).
While Trump was likely unaware of it, in making this statement he was taking sides in
the long-standing debate about whether online platforms equalize or normalize inequalities. On
the one hand, social media lower the costs of promotion, which can help poorly-financed groups
gain a more prominent voice (Earl and Kimport 2011). However, managing an effective social
media campaign can itself be costly. The learning curve for using these tools effectively can thus
serve to replicate or even exacerbate pre-existing inequalities (Schradie 2018). We contribute to
this ongoing debate by combining campaign finance and electoral outcome data from the Federal
3
Election Commission with Twitter metrics of candidates who ran in the 2016 U.S. congressional
elections. These metrics were constructed using data obtained with Twitter’s API and a snowball
sampling method that lets us identify users who retweeted (i.e., shared) candidates’ tweets in the
months before the election. We explicitly consider how electoral outcomes varied with Twitter
metrics of financially worse- and better-off competitors, the extent of financial inequity between
them, and the total spending by candidates in a given race.
As expected, financially equal races tend to have close electoral outcomes while unequal
races see large outcome gaps (about 35 percentage points, on average). We investigate how this
relationship between financial and electoral equality varies with Twitter metrics of the competing
candidates, finding that it is significantly moderated when the poorer candidate in a race has
indirect influence on Twitter – getting their tweets shared by users whose own tweets are widely
shared. Indirect influence thus helps to explain when financially unequal races exhibit relatively
close electoral outcomes. The effect persists after controlling for other Twitter metrics, state-
level fixed effects, and several election and candidate-specific variables, including exposure on
traditional media. Moreover, the relationship between money and votes does not depend on the
influence possessed by financially better-off candidates (direct or indirect), adding to the body of
evidence showing that social media can have equalizing effects (e.g., Samuel-Azran et al. 2015).
To explain the effect of indirect influence in financially unequal races, we draw on work
by political scientists on “reactive” spending (e.g., Jacobson 1978). Competing candidates often
reach financial equality when political parties attempt to outspend each other in races perceived
to be close. Empirically, such reactive spending results in higher spending overall. We argue that
the role of indirect influence in financially unequal races can thus be explained, at least in part,
by the fact that total spending in these races is typically low – implying that candidates have less
4
to spend on traditional advertising. Indeed, we show that the effect of indirect influence is more
pronounced in lower cost races. Finally, we compare election outcomes in 2016 to those in 2014,
finding that poorer candidates with indirect influence see smaller electoral gaps than their party’s
candidate in the same district (in House races) or state (in Senate races) in the previous election
year. Our results thus do not stem from geographic variation in social media use.
In the next section, we discuss previous research on congressional campaigning and
online influence, framing our work as a test of the normalization versus equalization argument
(e.g., Gibson et al. 2014). In Section 3, we discuss the details of our data and empirical models,
and describe our novel method of using the Twitter API to snowball out from candidate accounts
to measure their indirect influence. We discuss the results of our analyses in Section 4, providing
graphical support for our findings and describing candidates and tweets that garnered the most
indirect influence. We conclude in Section 5 with implications of our findings for contemporary
elections and discuss limitations of our study and directions for future research.
2. CONGRESSIONAL CAMPAIGNS AND ONLINE INFLUENCE
Congressional members were initially slow to adopt social media as a viable campaign
resource, but even early scholarship views social media use by congresspeople as a “vehicle for
self-promotion” (Golbeck et al. 2010). In other words, social media present yet another venue for
campaigning. More recent work on online campaigning has noted a steady increase in the use of
social media by congressional campaigns (Gulati and Williams 2013). Today, it appears that
members of congress have realized the potential of social media platforms for engaging with
their constituents, and have begun to fully embrace its use. For example, in the 115th Congress
5
of the United States (active from 2017 to early 2019), only four congressional members did not
have a Twitter account (Littman 2017).
While candidates may have come around to using social media platforms for political
campaigning, the extent that behaviors on social media are associated with political outcomes is
debated. DiGrazia et al. (2013) find that when members of congress are mentioned on Twitter,
they reap dividends in their general election vote share. However, Jungherr et al. (2017) show
that the relationship between Twitter mentions and electoral outcomes is tenuous, arguing that
mentions are a better proxy of attention towards politics rather than political support. Even if a
relationship between Twitter mentions and electoral success were to exist, mentions are not
directly tied to a candidate’s own campaign activities. The implications of such a relationship for
political campaign strategy thus remain unclear, even though the implications for polling may be
significant. Moreover, Twitter mentions represent just one class of metrics on a single platform,
and the findings of Jungherr et al. (2017) may or may not translate to other metrics or platforms.
Can political candidates leverage social media to influence electoral outcomes? While
scholars of technology and politics acknowledge the import of this question (Gibson et al. 2014),
so far research relating social media influence to electoral success remains scarce. One notable
exception is research by Bode and Epstein (2015), who use a unique data collection strategy that
relies on Klout – an online service that compiles data from several platforms to construct a single
“influence score.” They find that a candidate’s Klout score can help predict their vote share, even
after controlling for other candidate and race-specific variables. This work provides important
evidence that social media influence may play a role in shaping electoral outcomes, but the Klout
score is a blunt instrument that does not reveal which specific social media activities are
associated with electoral success. Here, we build on the work of Bode and Epstein (2015) by
6
employing a novel snowball sampling method that allows us to construct several classes of
metrics that can each be separately evaluated for their relationship with electoral outcomes.
Importantly, these are all metrics that campaigns could feasibly construct, in real-time and for
free, using Twitter’s Application Programming Interface (or API).
To motivate the classes of metrics we develop, we leverage previous research on the flow
of political information. Hilbert et al. (2017) distinguish between one step, two step, and network
step flows on Twitter. While one step flows capture messages that reach the broader population
directly from the source, two step flows are mediated by so-called “opinion leaders” (Katz and
Lazarsfeld 1955). Network step flows, meanwhile, are more intricate processes that are tied to
the underlying interconnectivity between people. Choi (2015) finds evidence of two step flows in
political discussions on Twitter, noting that “those whose messages were frequently retweeted
were highly likely to be opinion leaders” (p. 705). The idea that messages may rely on several
“steps” before reaching the broader population helps us reconcile how Twitter, a platform used
by fewer than a quarter of Americans (Wojcik and Hughes 2019), could have effects that go
beyond the platform itself. For example, in their study of political campaign websites, Norris and
Curtice (2008) find evidence of two step flows to explain how online campaign information can
reach people who do not visit candidate websites. While few people visit these sites, those who
do are more likely to discuss politics with people that they know. Many voters can thus discover
political information indirectly, without participating in online discussions.
Reflecting on this work, we posit that social media could benefit candidates by way of all
three of these flows. First, social media could help via one step by allowing candidates to engage
directly with more people. By building a large audience, candidates can gain celebrity-like status
(Wheeler 2013), which could have electoral effects. Second, social media can help via two steps
7
by allowing candidates to garner the attention of opinion leaders. The diffusion of information on
social media is usually driven by large broadcasts (Goel et al. 2016), implying that candidates
can gain indirect influence by having their messages shared by influential users. Finally, social
media could help candidates via a network step. Klar and Shmargad (2017) show that networks
which are structurally diverse – providing access to many different social network regions – can
accelerate the diffusion of messages from low-resourced groups. We thus test for the effects of
social media influence via one, two, and network step flows.
Beyond the general relationship between social media influence and electoral outcomes,
there is also debate concerning how the benefits of social media campaigning are distributed
across better- and worse-off candidates. Scholars who have contributed to this debate, sometimes
called the “normalization versus equalization argument” (Gibson et al. 2014), have tended to
focus on candidates’ initial adoption of online campaigning rather than metrics that capture the
effectiveness of online campaigns. For example, in a special issue of the Journal of Information
Technology and Politics alone, scholars investigated the adoption of online campaigning in
Denmark (Hansen and Kosiara-Pedersen 2014), France (Koc-Michalska et al. 2014), Germany
(Marcinkowski and Metag 2014), and Poland (Koc-Michalska et al. 2014). Our work helps to
inform this debate by analyzing how specific metrics are associated with electoral success,
extending prior work that has primarily focused on the initial adoption of online campaigning.
Scholars of social movements have noted that the success of modern movements depends
on their ability to leverage specific affordances of the internet. In particular, they cite low costs
of mobilization and asynchronous participation as prime opportunities that the internet affords
(Earl and Kimport 2011). In congressional politics, social media can give candidates with few
resources the ability to enter into a political arena where monetary costs are low, the transmission
8
of ideas is relatively unfiltered, and name recognition can be built quickly. This is in contrast to
traditional media outlets, like television, where costs of promotion run high. For example, the
costs of running a political ad on a local television station is estimated at $200-$1500 per 30
second ad, per run. For those with little money to build name recognition through traditional
channels, social media can provide a cheaper alternative. Campaigning on social media can thus
help marginalized candidates attract attention, broaden exposure to their messages, and build
name recognition.
In this light, the benefits of social media can be viewed as being qualitatively similar, in a
mild sense, to the advantages that are bestowed upon political incumbents. Political incumbents
– officials who currently hold the office for which they are running – enjoy several benefits over
those who challenge them, including free publicity (Cain et al. 1987) and wider name recognition
(Kam and Zechmeister 2013). Social media platforms, given their democratic premise that
anyone’s ideas can go viral (Nahon and Hemsley 2013), could similarly provide marginalized
candidates with the prospect of gaining high levels of exposure. We return to this analogy when
describing the setup of our empirical analysis which shows qualitatively similar patterns for
incumbency and social media influence.
We acknowledge that the benefits of social media could help well-resourced candidates
as well. However, well-resourced candidates have less to gain by using social media because
they already tend to have name recognition, established donor bases, and alternate means of
unfiltered communication with their constituents. Well-resourced candidates are thus more likely
to be at a point of saturation, wherein they have already expended resources to get information
out to voters (Jacobson 1990). Social media platforms can thus serve as a useful tool, one among
many, that well-endowed candidates employ, but is not a game changer. On the other hand, for
9
candidates with few financial resources, social media could decrease the promotional resource
gap, thereby narrowing the gap in vote shares.
3. DATA AND MODELS
Data for this project were collected with the help of ten research assistants. Each assistant
was assigned one or several U.S. states, and was tasked with collecting the Twitter handles of the
top two candidates (in terms of vote share) competing in 2016 House and Senate races in those
states. Data about the vote shares of candidates in each electoral race were obtained from the
Federal Election Commission (FEC) and were combined with the FEC candidate financial
summaries. The combined FEC data include several useful data points about the congressional
candidates, including their incumbency status and total disbursements (i.e., expenses) during the
campaign season. Data from the FEC were collected for electoral races in 2014 and 2016, so that
comparisons could be made across election years.
Before discussing the data that we gather from Twitter, we relate financial and electoral
equality across election years. We exclude uncontested races (i.e., those with a single candidate)
and any candidate who came in third or worse. We measure the financial equality of a race with
the ratio of the poorer to richer candidates’ expenses. A ratio of 1 thus implies that the race was
financially equal, while a ratio close to zero implies that the richer candidate outspent the poorer
candidate by a considerable amount. We measure the electoral equality of a race by subtracting
the vote share of the poorer candidate from that of the richer candidate. We use the two-party
vote share, which is the percentage of votes that a candidate receives divided by the total number
of votes that went to the top two candidates. All of the analyses in this paper use the two-party
vote share, though the results are nearly identical when using the raw vote percentages instead.
10
In addition to investigating financial and electoral equality across years, we also classify
races by the incumbency status of the candidates. In particular, we designate races as either open
(i.e., no incumbent present), richer incumbent, or poorer incumbent. Figure 1 depicts financial
and electoral equality across years and incumbency. In general, the patterns are similar in open
races and those that feature the richer candidate as the incumbent. In particular, financially
unequal races (i.e., those in which the ratio of expenses between poorer and richer candidates is
near zero) have large electoral outcome gaps. This is evident from the large y-intercepts in the
left two columns. However, as races get more financially equal the gaps tend to zero, implying
that poorer and richer candidates see similar vote shares.
Panels in the rightmost column of Figure 1, which depict the relatively few races where
the poorer candidate was also the incumbent, reveal a different pattern. In particular, electoral
outcome gaps do not appear to vary, on average, with the extent of financial inequality between
candidates. The intuition behind this pattern is simple: one candidate is richer while the other is
the incumbent, and in the end neither type of candidate consistently gets elected in these races.
Empirically, the rightmost column shows a flattening, or moderation, of the relationship in the
two leftmost columns. We similarly search for such flattening when considering the Twitter
metrics that render financial inequities irrelevant, or at least less pertinent, for electoral equality.
More directly, we are interested in Twitter metrics that exhibit qualitatively similar patterns as
poor incumbency.
[INSERT FIGURE 1]
11
3.1 Twitter Data
The final step of the data collection procedure took place in March of 2017, and relied on
Twitter’s API to extract data from candidates’ accounts. Often, candidates maintained more than
one account, and research assistants were instructed to keep every handle that they could locate
in such cases. The assistants then each ran a script, written in the programming language R, to
collect data capturing candidates’ tweets, users who retweeted (i.e., shared) candidates’ tweets,
tweets of users who retweeted candidates’ tweets, and users who retweeted tweets by candidate
retweeters. The data about candidates were thus detailed enough to let us create each candidate’s
ego social network, which includes information about which users retweeted a candidate’s tweets
and the extent that these users retweeted each other.
Figure 2 depicts our Twitter data collection strategy and the metrics that we construct to
capture one, two, and network step flows. At one step, we include the number of retweets and
likes that candidates received on their tweets, on average. At two steps, we include the average
number of followers that candidates’ retweeters had at the time of data collection (in thousands),
and the number of retweets and likes their tweets received, on average. At the network step, we
include two measures of structural diversity: the clustering coefficient (Watts and Strogatz 1998)
and social network constraint (Burt 1992). These measures capture the extent that a candidate’s
retweeters are located in different social network regions – where a low clustering coefficient
and network constraint both imply high levels of structural diversity. Both measures range from
0 to 1, and we multiply them by one hundred to ease the interpretation of our model estimates.
[INSERT FIGURE 2]
12
We restrict data collection to the three months leading up to the morning of election day,
November 8th, 2016. Practically, this means that the script requested tweets with ID numbers
greater than 760836150760050689 (a tweet by Clinton on August 3rd, 2016) and smaller than
795954831718498305 (a tweet by Trump on the morning of November 8th, 2016). To constrain
the time spent collecting data, the script requested at most 100 tweets from each candidate and
retweeter account. Twitter’s API restricts data collection to the most recent 3,200 tweets per
account, including replies and retweets. Since data collection took place more than six months
after the start of the period of interest, it is possible that some tweets from some candidates were
not retrieved. In Figure 3, we depict the total number of tweets collected and a count of the
earliest tweet collected by candidates, broken down by day and political party.
[INSERT FIGURE 3]
Twitter’s API also restricts the number of retweeters that can be retrieved for each tweet
to the most recent 100, implying that retweeter data was incomplete for tweets that were widely
retweeted. In Figure 4, we depict the retweeter retrieval rate by the average retweets and political
party of the candidates. The average retweeter retrieval rate across candidates was just over 90%,
though the retrieval rate drops off steadily for candidates who received more than 100 retweets,
on average. Finally, while analyzing our data it became clear that one of the research assistants
did not properly complete the task, so that Twitter metrics are missing for candidates in Illinois,
Maryland, Oklahoma, New Mexico, and West Virginia. Still, the full set of Twitter metrics was
available for 400 candidates, and 142 electoral races included the full set for both candidates. We
restrict our main analysis to the 284 candidates in these races.
13
[INSERT FIGURE 4]
In Table 1, we report summary statistics and a correlation matrix of the Twitter metrics.
On average, candidates’ tweets received about 20 retweets and 30 likes. Retweeters, on average,
saw significantly lower levels of engagement with their tweets than candidates, receiving about 2
retweets and just over 3.5 likes per tweet. The correlation matrix reveals that some of the Twitter
metrics are highly correlated. In particular, correlations were high between the metrics capturing
retweets and likes that candidates received, as well as between metrics capturing the followers,
retweets, and likes that candidate’s retweeters received. We revisit this correlation table when
discussing the metrics that we can comfortably include in the same model. We now turn to
describing the empirical models that we use to test how Twitter metrics were associated with the
outcomes of the 2016 U.S. congressional races.
[INSERT TABLE 1]
3.2 Empirical Modeling
We start by modeling the relationship between financial and electoral equality depicted in
Figure 1. To formalize the financial equality of a given electoral race, we construct the expense
ratio to be the ratio of expenses between the poorer and richer candidates, constrained to the top
two candidates in terms of vote share. For candidate expenses, we follow Margolin et al. (2016)
and use the TTL_DISB column from the 2016 FEC candidate summaries.
14
We then define the vote gap in an electoral race to be the difference in vote counts of the richer
and poorer candidates, divided by the total vote count across these two candidates:
We first employ a simple Ordinary Least Squares regression to relate the vote gap to the
expense ratio:
(Model 1)
Our next specification adds a vector of Twitter metrics and control variables, , an interaction
between this vector and the expense ratio, and state-level fixed effects :
(Model 2)
The coefficients in capture the effects of Twitter metrics and control variables in financially
unequal races (where the expense ratio is zero), while the coefficients in capture the effects of
Twitter metrics and control variables as races tend towards financial equality. The flattening we
observe for poor incumbency in Figure 1 would thus be exhibited by a negative coefficient in
and a positive coefficient in .
While the state-level fixed effects control for variation across states, they do not control
for variation across congressional districts. If certain districts consistently see larger vote gaps,
and these same districts are more likely to feature particular Twitter activities among candidates,
15
then the Twitter metrics in would be endogenous. In order to control for time-invariant district-
level characteristics, we match candidates in 2016 with vote shares of their party’s candidate in
the same electoral race in 2014. Formally, we define the vote gap in 2014 as:
We then model the difference in vote gaps between 2016 and 2014 as:
(Model 3)
If certain Twitter metrics are associated with smaller vote gaps in 2016 than 2014, we attribute
the shrinking across years to these metrics.
Finally, we discuss the control variables that we also include in . These are: the total
disbursements across both candidates in the race (in millions); an indicator equal to 1 for Senate
races and 0 for House races; the total number of candidates in the race; an indicator equal to 1
when an incumbent is present and is the richer candidate; an indicator equal to 1 when an
incumbent is present and is the poorer candidate; an indicator equal to 1 if the richer candidate is
affiliated with the Republican party; exposure of both candidates in traditional media – measured
as the number of mentions candidates received in CNN transcripts (DiGrazia et al. 2013) over
the same time period that we used for Twitter data collection; and candidates’ engagement on
Twitter, measured as the average number of days that passed between two consecutive candidate
tweets. Next, we describe the results of estimating several specifications of the models presented.
16
4. RESULTS
We start by establishing the empirical relationship between financial and electoral
equality and the moderating effect of poor incumbency (as depicted in Figure 1). In Table 2, we
report estimates from several models that use FEC data from electoral races in 2014 and 2016.
The left column shows that financially unequal races see large vote gaps – about 35 percentage
points, on average. However, vote gaps tend to zero as races get more financially equal (i.e., as
the expense ratio approaches 1). In the two right columns of Table 2, we add indicators for poor
and rich incumbency and fixed effects for state and year. Under both specifications, incumbency
of the poorer candidate is associated with a statistically significant moderation of the relationship
between financial and electoral equality. Specifically, the indicator for poor incumbency has a
negative baseline coefficient ( ) and positive interaction with the expense ratio ( ).
[INSERT TABLE 2]
Next, we test for moderating effects of the Twitter metrics. We begin by estimating
Model 2, which includes state-level fixed effects and the full set of control variables at both the
baseline level and as an interaction with the expense ratio. Since some of the Twitter metrics are
highly correlated (see Table 1), we start by adding them separately into the model. Each metric
enters the model in four ways: at both the baseline level and as an interaction with the expense
ratio, and constructed for both the poorer and richer candidates in a given race. In Table 3, we
report estimates of the seven metric-specific specifications. Only two of the seven metrics show
statistical significance: the average number of retweets and likes received by poorer candidates’
retweeters. Notably, none of the metrics shows statistical significance for the richer candidate in
17
the race. Moreover, for the poorer candidate, indirect influence appears to play a more significant
role than their direct influence (i.e., number of retweets or likes on their own tweets).
[INSERT TABLE 3]
These two metrics, average retweets and likes among poorer candidates’ retweeters,
exhibit qualitatively similar empirical patterns as poor incumbency. In particular, they show a
significant negative baseline coefficient and positive interaction with the expense ratio – a
flattening of the relationship between financial and electoral equality. Indirect influence thus
helps to explain financially unequal races that see relatively close outcomes. However, we are
careful not to attribute causality to these effects. The specifications in Table 3 do not account for
variation across congressional districts. It is possible that high engagement candidates compete
in districts that tend to lean strongly towards one political party, thereby exhibiting larger vote
gaps. In Model 3, we address the alternative explanation that our results stem from geographic
variation in social media use. In particular, we control for district-level variation by comparing
races in 2016 to the prior congressional election year of 2014.
The next analyses include multiple Twitter metrics in the same specification. We exclude
the metric capturing followers across a candidate’s retweeters, since it is highly correlated with
retweeters’ retweets and likes (see Table 1). Moreover, unlike retweets and likes, the follower
metric had no significant effects on its own (see Table 3). This null result is consistent with prior
work suggesting that follower counts are a poor measure of a user’s influence on Twitter (e.g.,
Cha et al. 2010). Since retweeter retweets and likes are also highly correlated with each other, we
exclude the metric capturing average likes across a candidate’s retweeters. The results are nearly
18
identical when excluding the number of retweets instead. We also exclude the metric capturing
likes that candidates themselves received, though specifications that exclude retweets instead
show nearly identical results. Alternative specifications, which include average likes across
candidate and retweeter tweets, are available in the appendix.
In Table 4, we report estimates from Models 2 and 3 in the left and right panels,
respectively. Both specifications include state fixed effects, all control variables, and four
Twitter metrics. We do not report estimates of the control variables here for the sake of brevity,
but they are available in the appendix. Notably, the number of observations is lower in Model 3
than in Model 2. This discrepancy is due to: 1) some states holding senate races in 2016 but not
in 2014, 2) some races in 2014 being uncontested so that vote gaps could not be calculated, and
3) some races featuring a third-party candidate in one year but not in another (so that races could
not be matched by party across years). Across both specifications, we find strong support for the
role of indirect influence. In financially unequal races, poorer candidates see smaller vote gaps in
2016, and compared to their party’s candidate in 2014, when they have indirect influence. The
effect of indirect influence is thus not an artifact of geographical variation in social media use.
[INSERT TABLE 4]
4.1 Total Spending
Next, we address the following question: why is the effect of indirect influence more
pronounced in races that feature large financial inequity between candidates? To address this
question, it helps to understand how such inequity arises in the first place. Political scientists
have long argued that candidates’ spending decisions reflect their perceived chances of winning
19
(e.g., Jacobson 1978). As Jacobson (1990) writes, “campaign spending may affect the vote, but
the (expected) vote affects campaign contributions, and thus spending, because potential donors
give more to candidates in races that are expected to be close” (p. 335). Donations thus pour into
races that are perceived to be competitive as political parties attempt to outspend each other. The
result of such reactive spending, paradoxically, is that competitors end up spending roughly the
same amount. Indeed, in our dataset, total spending in a race is highly correlated with the extent
of financial equity (i.e., the expense ratio) in the race (ρ = .429).
Financial inequity is thus more likely to arise in races that feature less spending overall.
In these races, candidates have less to spend on traditional forms of advertising, opening the door
for the relatively inexpensive promotional capabilities provided by social media. We thus expect
that social media will be more effective in races that feature lower spending. In our final model
specifications, we investigate whether social media mattered more in low spending races. If the
effect of indirect influence is more pronounced in low spending races, this would help to explain,
at least in part, why such influence is also more pronounced in races that feature larger financial
inequity between candidates.
In Table 5, we report estimates of Models 2 and 3, modified to include interactions with
total spending instead of the expense ratio. As in Table 4, both specifications include state fixed
effects, all control variables, and four Twitter metrics. We do not report estimates of the control
variables here for the sake of brevity, but they are available in the appendix. Across both models,
we find strong support for the role of indirect influence in low spending races. In low spending
races, poorer candidates see smaller vote gaps in 2016, and compared to their party’s candidate
in 2014, when they have indirect influence. Moreover, the effect of indirect influence tends to
20
zero as total spending increases, though the interaction only reached marginal significance in the
latter specification (p = .053).
[INSERT TABLE 5]
The estimates in Table 5 also point to the role of network influence in how candidates
performed relative to their party’s candidate in 2014. In low spending races, richer candidates
performed better (i.e., saw larger vote gaps) when their social networks were highly clustered.
This effect reverses for high spending races, wherein richer candidates perform better when their
networks are structurally diverse (i.e., less clustered). In high spending races, poorer candidates
also perform better when their networks are structurally diverse, as is evident from the larger
vote gaps associated with a larger network constraint. While we did not theorize about the effects
of social media in high spending races, our results suggest that more intricate network dynamics
may also underlie how social media shape political competition (Klar and Shmargad 2017).
4.2 Graphical Support
In Figure 5, we depict our findings graphically. In the top panels, the y-axis reflects vote
gaps in 2016, while in the bottom panels it reflects differences between 2016 and 2014. The left
and right panels capture the expense ratio and total spending on the x-axis, respectively. Each
point represents a different electoral race, with its size reflecting how many retweets retweeters
of the poorer candidate in the race received, on average. Gray points capture races where poorer
candidates did not have indirect influence, while black points capture races where they did. This
split roughly approximates the median of this metric (1.83 across all 142 races; 1.99 across the
21
111 races where comparisons to 2014 could be made). The lighter and darker bands reflect 95%
confidence intervals of the best fitting line for the gray and black points, respectively.
[INSERT FIGURE 5]
In the top left panel, the flattening of the relationship between financial and electoral
equality is evident. Indirect influence helps to explain when poorer candidates see smaller vote
gaps in races with large financial inequity (i.e., low expense ratios). In the bottom left panel, we
see that these races also saw smaller vote gaps in 2016 than 2014. In the right panels, it appears
that no high spending races feature a poorer candidate with indirect influence. Moreover, all of
the eleven rightmost points in the top right panel are gray, each representing a senate race. The
appendix includes a figure that excludes senate races, where the statistical support is similar
across races that feature low and high levels of indirect influence for the poorer candidate.
Still, in the top right panel, we find visual confirmation that, in low spending races,
poorer candidates with indirect influence see smaller vote gaps. In the bottom right panel, we
also find evidence that indirect influence helps to explain when vote gaps were smaller in 2016
than 2014, though the bands do overlap, possibly because there are fewer races here and thus less
statistical power. Overall, the patterns of the raw data conform to the estimates of our empirical
models, which point to the equalizing effect of indirect influence in elections the feature large
financial inequity or low overall spending. They suggest that social media may be particularly
useful for candidates who, in some sense, have been forgotten my political parties and donors.
These candidates spend significantly less than their competitor, and tend to compete in races that
do not receive much funding more generally.
22
4.3 Descriptive Analysis
To conclude this section, we discuss candidates and tweets that achieved high levels of
indirect influence (that is, were retweeted by users whose own tweets were widely retweeted). In
Table 6, we list the ten candidates who had the most indirect influence, but were outspent by
their competitor. The list is roughly split across party, with six Democrats and four Republicans.
Candidates from both parties can thus achieve relatively high levels of indirect influence. Of the
ten candidates, only two won their race: Brian Mast and Val Demings, one Republican and one
Democrat, who both ran for house seats in Florida. The most influential retweeters for these two
candidates, respectively, were @Braveheart_USA (an activist) and @donnabrazile (a political
strategist and commentator). Other influential retweeters include then Speaker of House Paul
Ryan (@SpeakerRyan) and a political action group that arose from Bernie Sanders’ presidential
primary campaign (@OurRevolution).
In Table 7, we report the most influential tweets from the candidates listed in Table 6. For
each candidate, we list all of their tweets that received an influence score of at least 10, implying
that their retweeters received at least 10 retweets on their own tweets, on average. For candidates
that did not have tweets with an influence score of at least 10, we report their tweet with the most
indirect influence. We also include the text of the most influential tweet by each candidate listed.
Republicans often pronounced their complaints with the current administration, while Democrats
posted about events and encouraged people to vote. Of course, this is a small selection of tweets
from a much larger corpus (see Figure 3), and thus may not represent candidate tweets overall.
Future work could investigate textual characteristics that are associated with achieving indirect
influence. While outside of the scope of the current paper, such an analysis would complement
existing studies, which typically apply measures of direct influence (e.g., number of retweets).
23
5. DISCUSSION
In this paper, we investigate how the benefits of social media campaigning are distributed
across competitors with financial inequities. While social media provide an inexpensive way for
groups to promote their messages (Earl and Kimport 2011), there is a significant worry that these
platforms merely serve to recreate or even exacerbate pre-existing inequalities (Schradie 2018).
We contribute to this debate, sometimes called the “normalization vs. equalization argument”
(Gibson et al. 2014), by analyzing Twitter use among 2016 U.S. congressional candidates. We
find that a specific class of Twitter metrics helps to explain when financially unequal races see
relatively close electoral results. These metrics capture the extent that the poorer candidate in the
race builds what we call indirect influence by having their tweets shared by users whose own
tweets are widely shared. We argue that social media platforms may thus serve an equalizing role
by letting candidates broaden their dissemination via two step flows (Katz and Lazarsfeld 1955).
These results have implications for how we go about measuring the success of social
media campaigns. The success of a campaign is typically evaluated based on the number of times
its posts have been liked or shared. Our findings point to another important aspect of a campaign
that informs its success – the extent that it achieves indirect, rather than direct, influence. Indeed,
electoral outcomes did not vary with metrics tied to candidates’ own tweets, such as the number
of times they were retweeted. The metrics of indirect influence we use are simple, and could be
feasibly constructed by campaigns, in real-time and for free, using Twitter’s API. Moreover, they
reflect the findings of previous work (e.g., Bakshy et al. 2011), which suggests that successful
diffusion arises from support among many users of moderate influence rather than a few highly
influential ones. Future research should investigate the types of messages that garner high levels
of indirect influence, as these appear to be indicative of a political campaign’s success.
24
Our findings also have important implications for the competitiveness of U.S. elections.
Electoral outcomes in the U.S. are seldomly close, which has generated some concern about the
“vibrancy of American democracy” (Fraga and Hersh 2018). Our results highlight that social
media platforms can provide lower-resourced candidates with an edge, especially in races that
feature large financial inequity or lower spending overall. In some sense, these races are the ones
that have been overlooked by political parties and donors, and social media may serve as an
alternative means generating attention in these cases. Of course, it is possible that political tweets
which achieve such influence have normatively negative qualities, such as being ideologically
extreme or even un-democratic (Tucker et al. 2017). Electoral competition is just one measure of
a vibrant democracy, and social media could hurt democracy in other ways. For example, if
tweets containing misinformation or disinformation are more likely to be propagated, social
media platforms would broaden exposure to these messages as well (e.g., Vosoughi et al. 2018).
There are two limitations to our study that are worth noting. First, while we compare
across election years to control for geographic variation in social media use, it is possible that
some candidates are simply better at attracting attention and would do so even in the absence of
social media. However, given that the influence we uncover requires messages to be shared by
many, moderately influential users, the networked nature of social media likely makes them
more conducive to this kind of decentralized influence. Second, while prior research suggests
that the diffusion of information is usually driven by large broadcasts (Goel et al. 2016), the
mechanisms that connect broadcasts to electoral outcomes need further investigation. For
example, does indirect influence merely broaden exposure to a candidate’s messages, or does it
add legitimacy to these messages through social signals? Future work should seek to inform our
understanding of the mechanisms that convert social media attention to voting behavior.
25
Figure 1: Financial and Electoral Equality by Year and Incumbency
26
Figure 2: Data Collection Strategy and One Step, Two Step, and Network Step Metrics
27
Figure 3: Volume of Total and Earliest Candidate Tweets by Day and Political Party
28
Figure 4: Retweeter Retrieval Rate by Average Retweets and Political Party
29
Figure 5: Vote Gaps by Poorer Candidate’s Indirect Influence
30
Table 1 Summary Statistics and Correlation Matrix of Twitter Metrics
Mean S.D. X1 X2 X3 X4 X5 X6
One Step Metrics
Avg. Retweets (X1) 18.3 81.3
Avg. Likes (X2) 31.3 140 .976
Two Step Metrics
Avg. Followers (X3) 5.56 7.29 -.048 -.037
Avg. Retweets (X4) 2.17 2.95 -.054 -.054 .654
Avg. Likes (X5) 3.59 4.52 -.059 -.054 .705 .973
Network Step Metrics
Clustering Coef. (X6) 2.35 6.27 -.067 -.069 -.092 -.054 -.051
Net. Constraint (X7) 7.07 9.80 -.140 -.141 -.181 -.182 -.210 .211
Notes: Number of Candidates = 284. Large correlations boxed.
31
Table 2 OLS Models Relating Financial and Electoral Equality
Model 1 + Incumbency + Fixed Effects
Coef. SE Coef. SE Coef. SE
Constant 34.6** .698 31.0** 2.26 31.1** 2.41
Expense Ratio -42.8** 1.98 -42.4** 4.50 -43.0** 4.75
Poorer Incumbent
Baseline -46.8** 13.5 -51.6** 13.9
x Expense Ratio 55.6** 16.9 63.4** 17.4
Richer Incumbent
Baseline 3.68 2.38 3.29 2.49
x Expense Ratio 4.25 5.17 4.27 5.43
State Fixed Effects No No Yes
Year Fixed Effects No No Yes
Adjusted R2 .403 .423 .430
Observations 694 694 694
Notes: * p < 0.05; ** p < 0.01.
32
Table 3 OLS Models with Twitter Metrics Included Separately
Model 2
Poorer Candidate Richer Candidate Adj.
R2 Baseline x Exp. Ratio Baseline x Exp. Ratio
Coef. SE Coef. SE Coef. SE Coef. SE
One Step
Avg. Retweets -.110 .165 -.021 .486 .035 .022 -.219 .266 .500
Avg. Likes .003 .077 -.041 .133 .025 .013 -.263 .180 .504
Two Step
Avg. Followers -.293 .382 .236 1.33 .156 .296 -.109 .795 .462
Avg. Retweets -2.69** .975 3.17* 1.42 -.161 .576 .529 2.22 .501
Avg. Likes -2.09** .707 2.44* 1.03 -.159 .387 .477 1.35 .507
Network Step
Clustering Coef. -.300 .279 1.44 1.50 .410 .388 -.765 1.32 .468
Net. Constraint .082 .163 .688 .950 -.661 .652 .762 1.56 .476
Notes: * p < 0.05; ** p < 0.01. Number of Observations = 142. Each row includes estimates from a
different model. State-level fixed effects included. All control variables included at both the baseline
and interaction levels. Statistically significant estimates boxed.
33
Table 4 OLS Models with Interactions between the Twitter Metrics and Expense Ratio
Model 2: Vote Gap 2016 Model 3: Vote Gap 2016 – 2014
Baseline x Expense Ratio Baseline x Expense Ratio
Coef. SE Coef. SE Coef. SE Coef. SE
Constant 44.5** 10.7 -65.6* 25.2 15.4 13.0 -61.3 37.5
Poorer Candidate
Avg. Retweets -.121 .179 .296 .540 .137 .423 -.727 1.94
Avg. Ret. of RTers -2.37* 1.02 2.48 1.52 -2.34* .900 2.00 1.54
Clustering Coef. -.227 .321 .376 1.97 -.112 .296 -.420 1.93
Net. Constraint .112 .174 .896 1.27 .201 .212 1.55 1.39
Richer Candidate
Ave. Retweets .038 .025 -.409 .302 -.005 .062 -.216 .735
Avg. Ret. of RTers .013 .601 -.747 2.70 .307 .501 -2.28 2.65
Clustering Coef. .764 .432 -.815 1.43 1.11 .665 -1.94 1.81
Net. Constraint -.811 .816 -.030 1.89 -.493 .862 -2.32 2.22
State FE Yes Yes
Adjusted R2 .507 .458
Observations 142 111
Notes: * p < 0.05; ** p < 0.01. All control variables included at both the baseline and interaction
levels. Estimates of control variables omitted here for brevity but are available in the appendix.
Statistically significant estimates boxed.
34
Table 5 OLS Models with Interactions between the Twitter Metrics and Total Expenses
Model 2: Vote Gap 2016 Model 3: Vote Gap 2016 – 2014
Baseline x Total Spent Baseline x Total Spent
Coef. SE Coef. SE Coef. SE Coef. SE
Constant 26.8** 9.83 -.602 2.06 12.5 12.4 -11.6** 3.87
Poorer Candidate
Avg. Retweets -.165 .190 .013 .027 -.012 .443 .088 .125
Avg. Ret. of RTers -3.48* 1.44 .528* .250 -4.49** 1.34 .468 .235
Clustering Coef. .037 .325 -.128 .238 .091 .385 -.330 .326
Net. Constraint .149 .184 .047 .109 .061 .243 .338* .136
Richer Candidate
Ave. Retweets .024 .036 -.005 .016 .012 .102 -.047 .060
Avg. Ret. of RTers -.637 .727 .233 .333 .197 .631 -.022 .350
Clustering Coef. .387 .636 -.017 .330 2.28* .847 -1.07** .380
Net. Constraint -.175 .890 -.159 .266 -1.52 .976 .453 .338
State FE Yes Yes
Adjusted R2 .505 .468
Observations 142 111
Notes: * p < 0.05; ** p < 0.01. All control variables included at both the baseline and interaction
levels. Estimates of control variables omitted here for brevity but are available in the appendix.
Statistically significant estimates boxed.
35
Table 6 Poorer Candidates with the Most Indirect Influence
Candidate State–
District Party
Exp. Ratio /
Total Spent
Vote Gap /
Win or Loss
Indirect
Influence
Most Influential
Retweeter
Morgan Carroll CO–6 Dem .876 / 6.71M 8.88 / Loss 23.3 @OurRevolution
Brett Murdock CA–39 Dem .021 / 3.74M 14.5 / Loss 9.33 @CA_Dem
Brian Mast FL–18 Rep .264 / 13.7M -10.9 / Win 9.21 @Braveheart_USA
Frank Guinta NH–1 Rep .994 / 3.10M 1.54 / Loss 7.76 @SpeakerRyan
Jim Reed CA–1 Dem .167 / .944M 18.1 / Loss 6.85 @_HorsesForLife
Kai Degner VA–6 Dem .076 / 2.16M 33.6 / Loss 5.50 @OurRevolution
Jim Lawrence NH–2 Rep .048 / 2.28M 4.65 / Loss 5.37 @rpollockDC
Brady Walkinshaw WA–7 Dem .646 / 4.91M 12.0 / Loss 4.97 @latinovictoryus
Val Demings FL–10 Dem .284 / .065M -29.7 / Win 3.82 @donnabrazile
Scott Garrett NJ–5 Rep .924 / 9.08M 4.51 / Loss 3.73 @SpeakerRyan
Notes: A candidate’s indirect influence is the average number of retweets their retweeters received on
their own tweets. A candidate’s most influential retweeter is that who received the most retweets on
their own tweets, on average. All of the candidates in this list competed in House races. Patty Judge of
Iowa was the senate candidate with the most indirect influence (average retweets of retweeters = 2.14).
36
Table 7 Influential Tweets from Poorer Candidates with the Most Indirect Influence
Candidate Tweet ID Indirect
Influence Text of the Most Influential Tweet Recovered
Morgan
Carroll 794237520150073344 76.9
EXCITING NEWS: Senator Bernie Sanders will
host a rally for us THIS SATURDAY! Link for
the event here - RSVP now!
https://act.myngp.com/Forms/-
2459545269794961408
Brett
Murdock 776852891990499328 9.33
Hey @RepEdRoyce , when are you going to
debate @Brett_Murdock1 ?
Brian Mast
780520118082166784
777988548016222208
789506829021749248
786243524102545408
52.7
41.9
22.6
18.5
Shameful. When our Veterans are desperate for
our help, the @DeptVetAffairs doesn't answer the
phone.
https://apnews.com/0c2c10e0c6b14b28bd592079
0884f5d1 #FL18 #sayfie
Frank
Guinta
794932832678412288
790968356069371904
788006023101607936
69.0
46.0
10.8
There's a #BetterWay to fight poverty
https://iqconnect.lmhostediq.com/iqextranet/view
_newsletter.aspx?id=101415&c=NH01FG Read
abt @HouseGOP plan in my latest "Weekend
Read"
Jim Reed 793519489249792000 6.85 I posted a new video to Facebook
http://fb.me/6bvdpwc08
Kai Degner 791707858463236096
793129772767408128
38.3
10.6 NO TWEETS RECOVERED
Jim
Lawrence
783445782783463424
791068400122490880
783447447406247936
794679638039678976
794685942455996416
794680227901374464
794683872453820416
27.0
23.0
13.7
13.6
13.6
13.6
10.8
I said two years ago that #Obamacare was going
to raise costs and it has. We need market-driven
solutions. #nh02 #nh02debates #nhpolitics
Brady
Walkinshaw 792027330608635904 9.40
Vote today; the climate can't wait! Learn more
about Brady's environmental leadership:
http://bradywalkinshaw.com/environment/
Val
Demings 790519281909846016 10.7
It is the first day of in-person early voting! Join
@HillaryforFL @SMurphyCongress & I for a
rally in Orlando this am
Scott Garrett 773561242724229120 14.7
I proudly voted for this bill to make sure every
survivor of sexual assault is empowered to seek
justice (Quoting a tweet by @SpeakerRyan: Last
night, the House unanimously passed a bill to
protect sexual assault victims. @RepMimiWalters
@RepZoeLofgren http://spkrryan.us/2cy6XUK)
Notes: A tweet’s indirect influence is the average number of retweets its retweeters received on their
own tweets. Only tweets with an indirect influence score of at least 10 are included in this list, unless
none exist for the candidate in which case their tweet with the most indirect influence is included.
37
AUTHOR INFORMATION
Yotam Shmargad is an Assistant Professor in the School of Government & Public Policy at the
University of Arizona, [email protected]
Lisa Sanchez is an Assistant Professor in the School of Government & Public Policy at the
University of Arizona, [email protected]
DATA AVAILABILITY
Data on electoral outcomes, incumbency, and party affiliations of candidates in 2014 and 2016
are available here: https://transition.fec.gov/pubrec/electionresults.shtml
Data on the campaign finance activities of candidates in 2014 and 2016 are available here:
https://classic.fec.gov/finance/disclosure/ftpsum.shtml
Twitter and other data are available from the first author upon request, subject to Twitter’s
Developer Policy: https://developer.twitter.com/en/developer-terms/policy
The authors will link to a GitHub account with the code used for snowball sampling and list of
candidate Twitter handles used.
SOFTWARE INFORMATION
R was used to collect data from Twitter (with the twitteR package), analyze candidate networks
(with the igraph package), and visualize data (with the ggplot2 package). Stata MP 13.1 was used
for model estimation. A .do file replicating regression analyses is available from the first author.
38
REFERENCES
Allison, Bill, Mira Rojanasakul, Brittany Harris, and Cedric Sam (2016), “Tracking the 2016
Presidential Money Race,” Bloomberg Politics, December 9, 2016.
https://www.bloomberg.com/politics/graphics/2016-presidential-campaign-fundraising/.
Bakshy, Eytan, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts (2011), “Everyone’s an
Influencer: Quantifying Influence on Twitter,” Proceedings of the 4th International
Conference on Web Search and Data Mining, Hong Kong (February 9–12), p. 65–74.
Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer, Cameron C. Marlow,
Jaime E. Settle, James H. Fowler (2012), “A Massive Scale Experiment in Social Influence and
Political Mobilization,” Nature 489(7415), p. 295–298.
Bode, Leticia and Ben Epstein (2015), “Campaign Klout: Measuring Online Influence During
the 2012 Election,” Journal of Information Technology & Politics 12(2), p. 133-148.
Burt, Ronald (1992), Structural Holes: The Social Structure of Competition, Cambridge, MA:
Harvard University Press.
Cain, Bruce, John Ferejohn, and Morris Fiorina (1987), The Personal Vote: Constituency Service
and Electoral Independence, Cambridge, MA: Harvard University Press.
Cha, Meeyoung, Hamed Haddadi, Fabricio Benevenuto, Krishna P. Gummadi (2010),
“Measuring User Influence in Twitter: The Million Follower Fallacy,” Proceedings of the 4th
International Conference on Web Search and Data Mining, Hong Kong (February 9–12), p.
10–17.
Choi, Sujin (2015), “The Two-Step Flow of Communication in Twitter-Based Public Forums,”
Social Science Computer Review 33(6), p. 696-711.
DiGrazia, Joseph, Karissa McKelvey, Johan Bollen, & Fabio Rojas (2013), “More Tweets, More
Votes: Social Media as a Quantitative Indicator of Political Behavior,” PLoS ONE 8(11):
e79449.
Earl, Jennifer and Kimport, Katrina (2011), Digitally Enabled Social Change: Activism in the
Internet Age, Cambridge, MA: MIT Press.
Easley, Cameron (2016), “Exit Poll: Clinton Bests Trump with Ground Game.” Morning
Consult. November 8, 2016, https://morningconsult.com/2016/11/08/clinton-bests-trump-
ground-game-per-exit-poll-data/.
Fraga, Bernard L. and Eitan D. Hersh (2018), “Are Americans Stuck in Uncompetitive
Enclaves? An Appraisal of U.S. Electoral Competition,” Quarterly Journal of Political Science
13(3), p. 291-311.
Gibson, Rachel, Andrea Römmele, and Andy Williamson (2014), “Chasing the Digital Wave:
International Perspectives on the Growth of Online Campaigning,” Journal of Information
Technology & Politics 11(2), p. 123-129.
39
Goel, Sharad, Ashton Anderson, Jake Hofman, and Duncan J. Watts (2016), “The Structural
Virality of Online Diffusion,” Management Science 62(1), p. 180-196.
Golbeck, Jennifer, Justin M. Grimes, and Anthony Rogers (2010), “Twitter use by the US
Congress,” Journal of the American Society for Information Science and Technology 61(8), p.
1612-1621.
Gulati, Girish J. and Christine B. Williams (2013), “Social Media and Campaign 2012:
Developments and Trends for Facebook Adoption,” Social Science Computer Review 31(5), p.
577-588.
Hansen, Kasper M. and Karina Kosiara-Pedersen (2014), “Cyber-Campaigning in Denmark:
Application and Effects of Candidate Campaigning,” Journal of Information Technology &
Politics 11(2), p. 206-219.
Hilbert, Martin, Javier Vásquez, Daniel Halpern, Sebastián Valenzuela, and Eduardo Arriagada
(2017), “One Step, Two Step, Network Step? Complementary Perspectives on Communication
Flows in Twittered Citizen Protests,” Social Science Computer Review 35(4), p. 444-461.
Jacobson, Gary C. (1978), “The Effects of Campaign Spending in Congressional Elections,”
American Political Science Review 72(2), p. 469-491.
Jacobson, Gary C. (1990), “The Effects of Campaign Spending in House Elections: New
Evidence for Old Arguments,” American Journal of Political Science 34(2), p. 334-362.
Jungherr, Andreas, Harald Schoen, Oliver Posegga, and Pascal Jürgens (2017), “Digital Trace
Data in the Study of Public Opinion: An Indicator of Attention Toward Politics Rather Than
Political Support,” Social Science Computer Review 35(3), p. 336-356.
Kam, Cindy D. and Elizabeth J. Zechmeister (2013), “Name Recognition and Candidate
Support,” American Journal of Political Science 57(4), p. 971-986.
Katz, Elihu and Paul F. Lazarsfeld (1955), Personal Influence: The Part Played by People in the
Flow of Mass Communications, Piscataway, NJ: Transaction Publishers.
Klar, Samara and Yotam Shmargad (2017), “The Effect of Network Structure on Preference
Formation,” The Journal of Politics 79(2), p. 717-721.
Koc-Michalska, Karolina, Darren G. Lilleker, Pawel Surowiec & Pawel Baranowski (2014),
“Poland’s 2011 Online Election Campaign: New Tools, New Professionalism, New Ways to
Win Votes,” Journal of Information Technology & Politics 11(2), p. 186-205.
Koc-Michalska, Karolina, Rachel Gibson, and Thierry Vedel (2014), “Online Campaigning in
France, 2007–2012: Political Actors and Citizens in the Aftermath of the Web 2.0 Evolution,”
Journal of Information Technology & Politics 11(2), p. 220-244.
Littman, Justin (2017), "115th U.S. Congress Tweet Ids", Harvard Dataverse,
https://doi.org/10.7910/DVN/UIVHQR.
40
Marcinkowski, Frank and Julia Metag (2014), “Why Do Candidates Use Online Media in
Constituency Campaigning? An Application of the Theory of Planned Behavior,” Journal of
Information Technology & Politics 11(2), p. 151-168.
Margolin, Drew B., Sasha Goodman, Brian Keegan, Yu-Ru Lin, and David Lazer (2016), “Wiki-
worthy: collective judgment of candidate notability.” Information, Communication and Society
19(8), p. 1029-1045.
Morin, R. (2016), “Trump says social media was key to victory,” Politico. November 12, 2016,
http://www.politico.com/story/2016/11/donald-trump-social-media-231285.
Nahon, Karine and Jeff Hemsley (2013), Going Viral. Cambridge, UK: Polity Press.
Norris, Pippa and John Curtice (2008), “Getting the Message Out: A Two-Step Model of the
Role of the Internet in Campaign Communication Flows During the 2005 British General
Election,” Journal of Information Technology & Politics 4(4), p. 3-13.
Samuel-Azran Tal, Moran Yarchi, and Gadi Wolfsfeld (2015), “Equalization versus
Normalization: Facebook and the 2013 Israeli Elections.” Social Media + Society 1(2), p. 1-9
Shradie, Jen (2018), “The Digital Activism Gap: How Class and Costs Shape Online Collective
Action,” Social Problems 65(1), p. 51-74.
Tucker, Joshua A., Yannis Theocharis, Margaret E. Roberts, and Pablo Barberá (2017), “From
Liberation to Turmoil: Social Media and Democracy,” Journal of Democracy 28(4), p. 46-59.
Vosoughi, Soroush, Deb Roy, and Sinan Aral (2018), “The Spread of True and False News
Online,” Science 359(6380), p. 1146-1151.
Watts, Duncan J. and Steven H. Strogatz (1998). “Collective Dynamics of ‘Small-World’
Networks,” Nature 393(6684), p. 440-442.
Wheeler, Mark (2013) Celebrity Politics. Cambridge, UK: Polity Press.
Wojcik, Stefan and Adam Hughes (2019), “Sizing Up Twitter Users,” Pew Research Center.
April 24, 2019, https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/.
