Pussyfooting around November? A Longitudinal Analysis of Politicians' Twitter Use in 2014

Abstract

In the November 2014 Midterm Elections, social media was used by more members of Congress and with greater frequency than ever before. We employ an established metric for interpreting the short but influential posts made by members of Congress via Twitter to determine how they position themselves relative to other politicians, candidates, and issues. This is crucial as we invoke a traditional theory of a two-party system-the median voter model-to explain why positioning via Twitter fluctuates in the period surrounding Election Day. Based on more than 338,000 Twitter postings by 422 politicians, our analysis shows that the extent to which a member of Congress positions one's self is a function of how far ahead (or behind) Election Day is. Our findings are robust to local polynomial trend line-and fixed effects-based analyses. We also show that Republicans are much more volatile in their use of positioning, and that-for both parties-the minimum point at which members of Congress reduce their positioning is not Election Day itself but approximately two weeks before Election Day.

Full text

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Title: Pussyfooting around November? A Longitudinal Analysis of Politicians’ Twitter Use in
2014
Authors:
Matthew A. Shapiro (corresponding author)
Illinois Institute of Technology
shapiro@iit.edu
Libby Hemphill
Illinois Institute of Technology
lhemphil@iit.edu
Jahna Otterbacher
Open University Cyprus
jahna.otterbacher@ouc.ac.cy
Abstract: In the November 2014 Midterm Elections, social media was used by more members of
Congress and with greater frequency than ever before. We employ an established metric for
interpreting the short but influential posts made by members of Congress via Twitter to
determine how they position themselves relative to other politicians, candidates, and issues. This
is crucial as we invoke a traditional theory of a two-party system – the median voter model – to
explain why positioning via Twitter fluctuates in the period surrounding Election Day. Based on
more than 338,000 Twitter postings by 422 politicians, our analysis shows that the extent to
which a member of Congress positions one’s self is a function of how far ahead (or behind)
Election Day is. Our findings are robust to local polynomial trend line- and fixed effects-based
analyses. We also show that Republicans are much more volatile in their use of positioning, and
that – for both parties – the minimum point at which members of Congress reduce their
positioning is not Election Day itself but approximately two weeks before Election Day.
Keywords: electoral politics, information technology and politics; political communication;
median voter model; social media; Twitter

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“… [E]each party strives to make its platform as much like the other’s as possible…. Each
candidate ‘pussyfoots,’ replies ambiguously to questions, refuses to take a definite stand in any
controversy for fear of losing votes” (Hotelling 1929: 54).
Introduction
Elected officials and those running for political office typically achieve personal political
success – (re)election to office – as a function of how effectively they communicate their
campaign pledges or receive partisan support. These messages can be distorted in the traditional
media by targeting partisan audiences (Bernhardt et al. 2008; Caillaud and Tirole 2007) or by
highlighting incumbents’ latest accomplishments (Healy and Lenz 2014). All of this is
exacerbated by problems identified in the public’s “issue-attention cycle” (Downs 1972). In short,
misinformation from the media polarizes, confuses, and is compounded by existing biases
(Kuklinski et al. 2000; Lewandowsky et al. 2012; Nyhan and Reifler 2010). We hold constant
but acknowledge these media-related effects as we focus on similar distortions arising through
social media. Specifically, we identify crucial fluctuations in the Twitter-based communications
by members of Congress during the 2014 Midterm Election.
We acknowledge that social media-based communications from elected officials are but
a part of a larger information-sharing campaign. In this way, we are effectively building on the
body of research which focuses on the language officials use in traditional media (Cook et al.
1983; Edwards III and Wood 1999; Entman 2007; Kedrowski 2000; Lee 2009) and on websites
that report statements and speeches of public officials (Gentzkow and Shapiro 2011). Social
media-based communication is important (Gulati and Williams 2007), as it is a proxy for
politicians’ ideology and policy preferences (Hemphill, Culotta, and Heston 2013; Shapiro and
Hemphill 2014) and predicts how a candidate will behave with (potential) constituents and
campaign funders (Williams and Gulati 2013). This paper, thus, is as much a study of how
members of the U.S. Congress altered their Twitter-based communications during the 2014
election – “speech acts” in our vernacular – as it is a campaign for broader acceptance of the
importance of social media-based communications by politicians.
In the following pages, we explain how we can understand elected officials’ use of social
media in traditional theories of electoral behavior. We then examine longitudinally, from
November 1, 2013 to February 26, 2015,1
how members of Congress altered their
communication patterns in line with Hotelling’s classic argument, captured in the prefacing
quotation. Our findings show that, regardless of party and chamber, members of Congress alter
their message particularly for general elections.
Tapping the Median Voter Model
Hotelling’s (1929) claims laid the foundation for a more developed understanding of how
a two-party system like that in the U.S. operates. Namely, election seekers present themselves as
moderates in order to maximize their vote-share (Bernstein et al. 1988; Canes-Wrone et al. 2002;
1 The latter date was selected to allow us to clean and analyze the data in a timely manner.

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Downs 1957). It is a compelling and elegant argument despite the fact that the public is
ideologically divided and especially so among those who are most engaged in politics
(Abramowitz and Saunders 2008; Green et al. 2002; Greenberg 2004; Hetherington and Weiler
2009).2
Democratic voters have shifted ideologically less to the left than Republican voters have
shifted to the right (Abramowitz and Saunders 2008; Abramowitz 2013; Abramowitz 2014;
Mann and Ornstein 2012; Brady and Han 2006), possibly a function of cross-party
communication differences. That is, while repetitious communications allow information to enter
the recipient’s working memory (Chong and Druckman 2007; Chong and Druckman 2011),
dramatic changes in the content of this information around Election Day may make it difficult
for voters to track shifts in the ideological position of their candidates (Lauderdale 2013). This
would be particularly problematic given alignments between social identity and party affiliation
(Bartels 2002; Campbell et al. 1960; Hetherington 2001; Mason 2013) as well as the role of party
branding (Groeling 2010; Brady et al. 2000; Jones 2010; Kawato 1987).
We make two propositions. First, the incentives for maximizing vote share attract both
parties to the median voter but especially around the election. We conceptualize this attraction in
the form of decreased rhetoric about one’s position about an issue; i.e. to invoke Hotelling
(1929), more “pussyfooting.” Second, given rightward shifts in the median voter, as implied
above, the decrease in partisan rhetoric around Election Day is expected to differ between parties.
In light of the connections between social identity and party affiliation, it is difficult to predict
how such differences might take shape. On the one hand, Republicans may be more intense in
their rhetoric overall, and thus the expected decrease around Election Day could result in a level
of rhetoric which still exceeds that of Democrats. On the other hand, Republicans may be more
dramatic in how they approach the median voter, decreasing their use of rhetoric closer to the
election and increasing it rapidly afterward. It is worth noting that these conditions are not
mutually exclusive.
There are several additional directions in which we can examine the phenomenon of
changes in communication from members of Congress over time. An obvious choice, for
example, is cross-chamber differences, particularly given claims that Senators are more even
tempered than Representatives (Grofman et al. 1991; Theriault 2006; Kernell 1973; Hare and
Poole 2014). Given differences in how the use of rhetoric is used from politicians to politician,
we focus here primarily on how variance in the frequency of communications affects inter-party
differences.
Data Collection & Coding
Twitter remains a less preferred communication vehicle for the majority of American
voters. Yet, there were approximately 7 million followers of members of Congress on Twitter as
2 See http://www.people-press.org/2014/06/12/political-polarization-in-the-american-public/ for
details.

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of the fall of 2012,3
The collection of Twitter posts was a function of the Twitter Database Server (Green
2011), existing Twitter-collectors (Hemphill 2011), and new Twitter-collectors such as
PurpleTag which use the Twitter REST API to collect a maximum of 3,200 Twitter posts from
each legislator.
and recent research has identified a positive association between Twitter
posts by members of Congress and New York Times content (Shapiro and Hemphill 2014). We
assume, thus, that Twitter is used as a key part of politicians’ communication tools. For example,
the simple count of Twitter posts by members of Congress from November 1, 2013 to February
26, 2015 shows clear lulls around the winter break, spikes around the President’s State of the
Union address, and spikes surrounding other policy-related issues.
4 For the period from November 1, 2013 to February 26, 2015, we accumulated
nearly 338,000 Twitter postings by 422 politicians.5 Classifying positioning Twitter posts is the
result of an iterative process of establishing inter-coder reliability across a spectrum of action-
based categories and then using an automated algorithm to classify thousands more statements.
This approach addresses many if not all of the concerns raised in Grimmer and Stewart (2013)
about text analysis at such a massive scale. Six codes were ultimately identified: narrating,
positioning, directing to information, requesting action, giving thanks, and “other”, the latter
dropped in our subsequent analysis. Codes were not mutually exclusive, and Cohen’s kappa
scores (Cohen 1968) for each code indicate very strong agreement between coders. Given the
labor intensiveness of hand-coding each Twitter post, we automated the coding process by
training binary classifiers for each of the five action codes. We used MALLET (Machine
Learning for LanguagE Toolkit; McCallum 2002), which employs supervised learning
algorithms to exploit the words in Twitter posts in order to determine whether or not they exhibit
each of the five actions.6
The algorithm produces probabilistic results – each Twitter post has a
value between 0 and 1 for each action code, and the value indicates how likely that code applies
to the Twitter post. For instance, a value of 0.80 for Positioning means there’s an 80 percent
likelihood the Twitter post is actually a positioning statement. Typically, the differences between
high, medium, and low positioning Twitter posts are a function whether or not a bill, person,
party, or the Twitter post author’s opinion is mentioned.
Results
Our analysis should begin with an examination of all five speech acts over the 2014
election cycle; however, we target only positioning for the purposes of this paper. We use local
polynomial trend lines (Fan and Gijbels 1996), fitting the trend line using locally weighted least
3 The number of followers is determined by aggregating the followers for each individual
member of Congress. There are, thus, likely to multiple counts of the same individual,
particularly members of the media, political pundits, fans of Congress, etc.
4 See https://github.com/casmlab/purpletag/blob/master/README.md for details.
5 Twitter’s API makes nearly all Twitter posts available, but occasionally Twitter posts are not
indexed and are therefore not made available programmatically.
6 Further details about the development of our coding scheme and the automated classifier are
available in Hemphill, Otterbacher, and Shapiro (2013) and Otterbacher et al. (2012).

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squares. This is consistent with our expectation that more recent events matter more than those
across the entire time period. To definitively show that positioning is occurring in line with our
expectations, we engage in a second analysis which restricts the time period from August 2014 to
the end of February 2015 and employ fixed effects modeling techniques. The fixed effects
approach is especially important given the potential for unidentified attributes of individual
members of Congress to drive shifts in positioning.
Twitter is used by many but dominated by a few, shown in Table 1. Given the possibility
that a handful of users can determine the impact of our graphical results, we control for variance
across high- and low-frequency Twitter users in Congress by grouping members of Congress
into quartiles based on their Twitter usage from November 1, 2013 to February 25, 2015.7 Party-
based differences are presented in Figure 1, where each local polynomial trend line represents a
quartile (lowest frequency Twitter posters are in the first quartile; highest frequency Twitter
posters are in the third quartile).8
In this figure, we observe that both Democrats and Republicans
begin to decrease their positioning from late-July 2014 in anticipation of Election Day (the
vertical blue line), and that the greatest differences among quartiles centers on Election Day. We
also observe that both parties increase their positioning after Election Day. More similarities
between parties include the divergence of quartile-based trend lines from November 2013 up to
Election Day as well as the increased positioning which occurs from April 2014, peaking in mid-
summer 2014. We attribute this peak to politicians’ appeals during primary elections to the
median voter within their respective parties. In sum, our expectation that November 2014 will
coincide with the lowest point for positioning in order to attract the median voter is conditionally
satisfied. A close inspection of Figure 1 reveals, though, that positioning is least likely to occur
not on Election Day itself but rather approximately two weeks before.
TABLE 1 HERE
FIGURE 1 HERE
In terms of party-based differences, Republicans exhibit much more dynamic shifts in
their use of positioning while using Twitter, possibly challenging voters’ understanding and/or
ability to track Republican ideology. The crucial cross-party distinction lies in our comparison of
quartiles of frequency. For Democrats, members of Congress that are most likely to position are
the least frequent users of Twitter. Yet, the opposite is true for Republicans. More importantly,
while we continue to see for Democrats a clear shift from decreased-to-increased positioning in
the two weeks preceding Election Day, a close examination of the first and second Republican
quartiles indicates that there are small shifts back and forth within the last two weeks of the
election. Specifically, Republicans in the first quartile increase their positioning before
remaining static in the last few days before the election; Republicans in the second quartile shift
between slight increases and decreases in the last couple of weeks, but then increase their
7 These quartiles are not based on distributions within party and/or chamber.
8 All parties other than Republicans are grouped with Democrats.

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positioning in the last few days before the election. Despite these differences, the overall pattern
is one in which there is a negative correlation between frequency of Twitter posting and strength
of positioning for Democrats. The correlation is positive for Republicans.
Complementing the previous analysis are fixed effects results which predict over time the
positioning by party and chamber subgroup. Party-level effects can be superseded by those of
individual politicians (Jacobson 2009; Jacobson 1978; Stokes and Miller 1962), so a fixed effects
modeling approach focusing on each individual member of Congress allows us to control for and
guard against the possibility of omitted time invariant measures that also might be correlated
with positioning. The marginal effects, i.e. the predicted changes relative to November 2014, are
presented in Table 2. Statistically significant differences between each month and November
2014 are represented by bold font. We realize that November 2014 represents the entire month
and not solely the election on November 4 and, thus, that the coefficients in marginal effects are
at best representations of general time trends. Nonetheless, the results remained unchanged when
using two week bands of time for the period preceding and following the 2014 election.
TABLE 2 HERE
The dynamics revealed in Table 2 are not entirely consistent with the expectations set
forth in Hypothesis 1. Rather than moderating language incrementally more in the months
leading up to the election, members of Congress tend to fluctuate in their positioning. Relative to
November 2014, House Democrats, for example, position less in September and October only.
Senate Democrats, House Republicans, and Senate Republicans all position less in August and
October only. Finally, for all four subgroups, positioning increases in the months following the
election, implying that the period surrounding Election Day is indeed a minimum point.
Conclusion
Researchers have not reached consensus regarding how the Internet impacts the political
campaign process (Druckman et al. 2014), but the evidence provided here confirms that
communications via social media are consistent with and contribute to the corpus of electoral
behavior and media-based communication. Our research confirms that the November 2014
election – possibly every general election from this point forth – can be used to predict how
members of Congress will use rhetoric via Twitter. Higher levels of positioning by members of
Congress between election cycles and lower levels of extreme language use in the period leading
up to the election have the potential to draw in the median voter and dictate what is reported in
the traditional media. These findings are robust to both local polynomial trend line- and fixed
effects-based analyses. Yet, there are a number of alternative explanations for our findings, and
we make a broad call for future and sustained research on political behavior and communication
through social media.
In terms of inducing theory about political communication, we observed that members of
Congress represent themselves and/or their colleagues in very different ways in the final days

7
before the election. We do observe conflicting evidence with regard to our expectation that
members of Congress will be more restrained in their use of positioning. For both parties, the
final two weeks of an election is a period during which politicians seem to assert themselves by
positioning more. This may occur because a member of Congress believes that more extreme
positioning in the final days before the election will produce an appearance of confidence about
the election’s outcome. Yet, roughly one quarter of all voters do not finalize their vote choice
earlier than two weeks before the election (Nir and Druckman 2008), a share that has continued
to grow in recent years (Box-Steffensmeier et al. n.d.). Perhaps politicians expect that voters
have essentially made up their minds about their vote choice, and thus the final two weeks
provide an opportunity to further secure the votes of the one’s existing base of support. Whatever
the case – and to again repeat the call for future research on this topic – the late-campaign shift
represents but another piece of Twitter-derived evidence not previously identified for members
of Congress en masse or longitudinally.

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Figures and Tables
Table 1. Most frequent Twitter posters by congressional chamber and political party, November
1, 2013 to February 26, 2015
House Democrats
Senate Democrats
House Republicans
Senate Republicans
Chaka Fattah 3,228
Bernard
Sanders 3,240
John A.
Boehner 3,244
Mitch
McConnell 2,796
Keith Ellison
3,193
Patty Murray
3,223
Darrell Issa
3,205
John McCain
2,429
Kyrsten Sinema 3,190
Richard
Blumental 2,961 Bradley Byrne 2,814 Rand Paul 1,889
Frederica
Wilson 3,161
Christopher
Murphy 2,867 Billy Long 2,702 Ted Cruz 1,603
Paul Tonko 3,118
Tammy
Baldwin 2,568
Robert
Pittenger 2,311 Daniel Coats 1,498
Tony Cardenas 2,532
Heidi
Heitkamp 2,557 Keith Rothfus 2,022 Roy Blunt 1,457
James
Langevin 2,299
Charles
Schumer 2,134 Renee Ellmers 1,975 Orrin Hatch 1,419
Dina Titus 2,214
Benjamin
Cardin 1,934 Steve Scalise 1,841 Mark Kirkfe 1,407
Eric Swalwell
2,204
Richard Durbin
1,736
Martha Roby
1,745
James Inhofe
1,330
John
Garamendi 2,150 Thomas Carper 1,292
Michael
Fitzpatrick 1,631 Michael Crapo 1,297

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Figure 1. Local polynomial probabilities of positioning, 11/1/2013 to 2/26/2015, by party
Note: Vertical line indicates Election Day, 2014. Quartiles represent groups of members of
Congress according to frequency of Twitter posts.
.3 .35 .4 .45
10/01/2013
01/01/2014
04/01/2014
07/01/2014
10/01/2014
01/01/2015
04/01/2015
10/01/2013
01/01/2014
04/01/2014
07/01/2014
10/01/2014
01/01/2015
04/01/2015
Democrat Republican
first quartile
second quartile
third quartile
Positioning probability

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Table 2. Probabilities (as percentages) of positioning by party and chamber in the months
surrounding November 2014
House
Democrats
Senate
Democrats
House
Republicans
Senate
Republicans
August 2014
34.3
33.2
32.5
33.4
September 2014
33.8
36.8
36.8
37.8
October 2014
34.1
33.8
33
33.4
November 2014
35.1
36.6
36.2
37.2
December 2014
36.6
37.5
36.9
37.1
January 2015
37.2
38.9
40.8
40.8
February 2015
36.5
40.8
39.2
40.2
Note: Marginal measurements based on fixed effects modeling; bold text indicates significant
differences at the p<0.1 level relative to probability of positioning in November 2014.

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