# Tweeting MPs: Digital Engagement between Citizens and Members of Parliament
in the UK

Pushkal Agarwal1, Nishanth Sastry1, Edward Wood2
1King’s College London, 2House of Commons Library
<EMAIL_ADDRESS><EMAIL_ADDRESS>

###### Abstract

Disengagement and disenchantment with the Parliamentary process is an
important concern in today’s Western democracies. Members of Parliament (MPs)
in the UK are therefore seeking new ways to engage with citizens, including
being on digital platforms such as Twitter. In recent years, nearly all (579
out of 650) MPs have created Twitter accounts, and have amassed huge
followings comparable to a sizable fraction of the country’s population. This
paper seeks to shed light on this phenomenon by examining the volume and
nature of the interaction between MPs and citizens. We find that although
there is an information overload on MPs, attention on individual MPs is
focused during small time windows when something topical may be happening
relating to them. MPs manage their interaction strategically, replying
selectively to UK-based citizens and thereby serving in their role as elected
representatives, and using retweets to spread their party’s message. Most
promisingly, we find that Twitter opens up new avenues with substantial
volumes of cross-party interaction, between MPs of one party and citizens who
support (follow) MPs of other parties.

##  1 Introduction

There has been much bemoaning the apparent decrease in political engagement
amongst the electorate in western democracies such as the UK [1]. Connecting
elected representatives such as Members of Parliament (MPs) with voters
(especially young voters) is seen as a means to “revive democracy” [2] and
recently there is much hope that online methods such as Twitter will play a
key role in this [3]. Yet, traditional scholarship on legislative studies has
focused mostly on the relationship between the Parliament and the Government,
casting MPs in the core roles of legislation and scrutiny of the Executive
branch, neglecting the communication between citizens and their MPs [4].

In this paper, we are interested in characterising how engagement of
democratic representatives with their citizens is shaped by online platforms,
more specifically Twitter. We focus on the UK, where a remarkable 579 out of
650 Members of Parliament (MPs) are active on Twitter. This represents a
dramatic rise from just a few years back: only in 2011, we had just 51 MPs
“dipping their toes” in Twitter [5]. Now, however, these MPs, who represent a
nation of 65 Million, have a collective following of 12.83 Million on Twitter
(some of these users follow multiple MPs; the total number of unique users
following at least one MP is 4.28 Million.). Thus, Twitter appears to have
become a platform on which MPs can engage with a substantial number of
citizens.

To frame the discussion, we consider the nearest offline equivalent for
interaction between MPs and citizens – _constituency service_. The traditional
means by which this is done is for the elected representatives to hold open
and private meetings with those that elected them. In the UK, for example, MPs
travel back to their constituencies, typically on Thursdays, after the work of
the Parliament is done, and hold ‘surgeries’ with their constituents. ‘Town
hall’ meetings in the USA serve a similar purpose. Constituents may also phone
or email their MPs and members of Congress to let them know their positions on
key issues.

To be sure, there are differences between engagement on Twitter and
traditional constituency service. Twitter interaction can be immediate and
spontaneous, in contrast with scheduled surgeries. The public nature of
Twitter renders it unsuitable for constituency service requiring personal
information. In our dataset in 1548 cases, MPs asked to move away from public
discussions on Twitter, asking constituents to make appointments at their
surgeries, offering their email addresses or asking the respondent to “DM” or
“direct message” them. In a handful ($\approx 10$) of cases, both options were
offered111One MP wrote: “@XX, If you follow me I’ll DM you or please email
YY@ZZ and I will get back to you. Thanks, D”. These interactions represent
over 7% of replies by MPs. Also, constituency service is usually seen as MPs
engaging with and serving those in the _geographic area they represent_. In
the UK, there is even a strict parliamentary protocol that MPs do not seek to
intervene or act in matters raised by the constituents of other members [6].
On Twitter, however, it can be hard for MPs to tell the precise location of
their correspondents, and the immediacy and public nature of the medium may
lead to interactions with non-constituents.

Despite such differences, both forms of communications hold the same promise:
direct contact and engagement between elected officials and those they are
supposed to represent. Therefore, we turn to the literature on constituency
service interactions for pointers on the nature of the discourse between
citizens and UK MPs on Twitter. The traditional view of psephologists has been
that constituency service is worth only about 500 votes [7, 8], and thus, is
insufficient to make a difference in all but the closest of elections. [9, 10]
talk about the ‘incumbency factor’ and the need for MPs to develop this
relationship in order to get re-elected. Thus, more than being a campaigning
tool, engaging with citizens can be seen as a mechanism for building
relationships and achieving better representation.

Based on these considerations, we focus on a two-month period from Oct 1, 2017
to Nov 29, 2017, when there was no election going on, and thereby seek to
understand the usage of Twitter as a tool for everyday citizen engagement. The
period also encompasses times when Parliament was in session (requiring MPs to
be away from their constituencies, attending the House of Commons) and in
recess222https://www.parliament.uk/about/faqs/house-of-commons-faqs/business-
faq-page/recess-dates/ (when MPs are free to return to their constituencies),
and therefore can be expected to cover both aspects of MP activities. We study
the Tweets, Retweets and Replies of MPs towards other users, as well as from
other users towards MPs. Both groups are active, with the MPs and the citizens
respectively producing 178,121 and 2,339,898 Tweets, Retweets and Replies
directed at each other.

The parallels and distinctions between engagement on Twitter and constituency
work also drive our research questions: Norton and Wood’s seminal study of
British MPs’ constituency work in the 80s concluded that constituency service
can be extremely rewarding, although taxing, taking MPs close to “saturation
point” [11]. Therefore it is natural to ask whether Twitter imposes a burden
on MPs. Given that any additional work would also likely take time away from
other duties of the MP, we also wish to understand how MPs manage whatever
burden is imposed on them by their Twitter presence. Following the typology of
[12] who studied how MPs use constituency service to package themselves, we
ask whether MPs are using Twitter to prioritise helping constituency members,
gaining personal visibility by highlighting work they have done, for spreading
the message of their party and party leaders, or for other purposes. Finally,
we are interested in identifying the tone of the conversation online. Given
the tendency of Twitter as a polarising and sometimes aggressive sphere [13,
14], we ask what the nature and tone of the conversation is, between MPs and
others. This discussion can be crystallised into the following research
questions:

  1. RQ-1

As a new and additional medium of citizen engagement, how much load does
Twitter place on MPs, and how does this load vary?

  2. RQ-2

How do MPs manage the load imposed? Do they selectively prioritise certain
forms of engagement or seek external help (e.g., from their staff)?

  3. RQ-3

What is the nature and tone of the conversations? Is Twitter a polarising
sphere with echo chambers for each party and side of the political spectrum?
Is the tone civil or aggressive?

We find that attention to individual MPs varies dynamically: although there is
a huge amount of information overload during short periods of time which we
term as “focus windows”, there is a significant amount of “churn” in the set
of MPs who are “in focus” at any given time. MPs strategically manage their
relationship with their followers and this information overload by balancing
their different roles as representatives of their constituency and their party
[12]: They selectively reply to Twitter profiles in the UK and within their
constituency region, fulfilling their representative role, and use retweets as
a mechanism to promote their image and spread the message of their party.
Interestingly, we find evidence of significant cross-party interaction,
between citizens who support and follow MPs from one party, and MPs from other
parties. Thus, in an atmosphere of growing political divide in the UK (e.g.,
[15, 16]), Twitter seems to offer ways to avoid the “echo chamber” behaviour
which characterises much consumption of information about politics online.

###  1.1 Related Work

Earlier studies on the use of Twitter by UK politicians mostly relate to a
period when such usage was in its infancy, with a small fraction of MPs being
regular users [5, 17, 18, 19]. Nevertheless, there were early indications that
use of Twitter was entering the mainstream of electoral campaigning and
political communications generally. Although, political tweets might not look
like substantive _contributions_ to the political discourse, they appear to
have become _an increasingly integrated element of political communication in
a ‘hybrid media system’_ [20].

In earlier usage of Twitter, one-way communications (“broadcasting”)
predominated [18, 5, 17], but participatory communication through Twitter was
seen to be emergent. It seemed to fit neatly into [21]’s concept of direct
representation [18] and politicians talked about their use of Twitter in these
terms, but it was still secondary to other uses [20]. Nevertheless, [18] found
that 19% of candidates’ tweets during the 2010 general election campaign
interacted in one way or another with voters, which they argued was a fairly
substantial level of interaction compared to other forms of political
communication during the campaign. A participatory style of communications on
Twitter had potential to earn legislators political capital [5] and was the
only statistically significant strategy that had a positive impact on the size
of the community [17].

Cross-party communication _between MPs_ was found to be unusual.
Unsurprisingly, MPs indulged in one-off attacks on other politicians during
the 2010 UK general election campaign [18] but there was evidence of a more
collaborative approach amongst an “organic community” of early adopters on
Twitter [5]. Supporters of different parties tended to cluster around
different hashtags during election campaigns, creating politically separated
communication spaces [20]. Here, we focus on communications _between MPs and
non-MPs_ during periods when there is no election, and find that cross-party
talk is more prevalent.

Our focus on a period without an election also makes our efforts complementary
to the large number of works that examine the (ab)use of Twitter during
election campaigns [18, 22, 19, 20, 23, 24, 25]. Complementary to our focus on
MP efforts, [25] studies citizens’ participation in political discussions
online, and finds that extrinsic motivations such as social norms are the most
significant in mobilising efforts.

##  2 Background and dataset

In this section, we give a background about the UK democratic process,
focusing on MPs and their communication needs and motivations, and describe
the datasets333The dataset we collected is made available at
https://nms.kcl.ac.uk/netsys/datasets/tweeting-mps/ for non-commercial
research usage. Following Twitter’s Terms of Usage
(https://twitter.com/en/tos), we will only be able to share the Tweet IDs. we
have collected to answer our questions.

###  2.1 MPs and Democracy in the UK

The Parliament in Westminster is the supreme legislature in the UK. It is
composed of two houses or chambers. The primary house is the House of Commons.
It has 650 elected members. Most MPs at any given election are drawn from a
handful of major political parties. It is possible for candidates to run for
election without the backing of a political party but they are very unlikely
to get elected. The three major parties in the UK Parliament following the
2017 general election were Conservative (Cons.), Labour (Lab.) and Scottish
National Party (SNP). Other parties with MPs include the Liberal Democrats
(Lib Dems) and the Democratic Unionist Party (DUP).

Members of Parliament are elected on a “party ticket” or manifesto and when
they vote in the House of Commons they are expected to obey party discipline.
This also applies to their publicity and engagement work, where they are
discouraged from giving messages that are inconsistent with the party line.
This role of the MP as a party representative may sometimes conflict with the
role of MPs as representatives of their constituencies. However, some MPs are
more loyal to their party than others [26], and in some cases, may choose
constituency over party.

###  2.2 Datasets

#### MPs on Twitter

We start with the MPs who are active on Twitter, as obtained from a
comprehensive and up to date list444http://www.mpsontwitter.co.uk/list. The UK
House of Commons has 650 Members of Parliament (MPs). Of these, 579555we have
collected data for 559 MPs since last year, and have not included the 20 new
MPs who have joined since then. MPs (187, i.e., 32.37% are female) are active
on Twitter, with a total of $\approx$ 13 Million followers. For each MP, we
obtained the following data:

Follower and Following

Using twitter API, we fetched all the users ($\approx$ 4.28 Million) who
follow MPs and also the users that MPs followed (869K).

Tweets and replies ($\Rightarrow$)

Using the Twitter API, we obtain from the MPs’ Twitter timelines a total of up
to 3,200 original tweets, retweets and replies to other Twitter handles.This
covers the period of Oct 1 - Nov 30 2017, and we are able to fetch all MPs’
timelines within the maximum limit of 3,200 allowed by Twitter. These
collectively identify the utterances made by the MPs, directed towards other
Twitter users. We will use the symbol $\Rightarrow$ to refer to such Tweets.

Mentions and replies to MPs ($\Leftarrow$)

To fully understand the extent of the conversation, we obtain the utterances
of all other Twitter users666In the rest of this paper, we interchangeably use
the terms “ordinary” Twitter users and “citizen” to refer users who have
mentioned an MP in one of their Tweets. When the term citizen is used, it has
been verified (if relevant), that the users included are those who declare a
profile location in the UK (See Geography details above)., directed towards
the MPs. This is obtained by searching for the MPs’ Twitter handles using
Twitter’s “advanced search” API, and includes all mentions of the MPs’ Twitter
handles, whether as a reply to a tweet of an MP, or merely mentioning an MP’s
Twitter user name in a non-reply Tweet. We will use the symbol $\Leftarrow$ to
refer to such Tweets.

MPs on Twitter |  559
---|---
Verified MPs |  83.54%
Total Followers |  12.83 Million
Mentions per MP per day ($\Leftarrow$) |  Mean: 67.96,
Median: 13.25,
Standard Deviation: 302.82
Activity (Tweets, replies to others) per MP per day ($\Rightarrow$) |  Mean: 5.52,
Median: 3.3,
Standard Deviation: 6.18
Table 1: Details of the Twitter dataset (Oct 1 – Nov 30 2017).

Collectively, $\Rightarrow$ and $\Leftarrow$ capture both sides of the
conversation between MPs with Twitter handles and the rest of Twitter. To
understand how people talk about MPs who are not on Twitter, we searched for
the full (first and last) names of such MPs, obtaining 35,904 Tweets. To
ensure that this refers to MPs and not some other person with the same name,
we manually examined all Tweets and compiled a list of the most politically
related words used in conjunction with these names (mp, Brexit, Parliament,
Westminster, Tory, Minister, Party, Conservative, Labour, Vote, Democracy).
Filtering for these keywords, we are able to retain 15,083 (of the 35,904)
Tweets. Since these are Tweets mentioning MP names, we term these as “pseudo-
mentions”. Clearly we are conservative in capturing pseudo mentions, and may
have ignored several tweets, e.g., those that may not use the first and last
names of the MPs. However, pseudo-mentions introduce data about 67 MPs. Thus,
information about _646 of the 650 MPs are captured in our dataset, in one way
or another._ Statistics about the dataset are listed in Table 1.

We also perform additional heuristic processing to obtain the following
information for MPs and all users who mention them or are mentioned by the MPs
(through retweets, replies or original tweets). Where heuristics may lead to
errors, we try to perform some checks, using limited ground truth to give some
indication of the accuracy or coverage that we believe we have attained:

Geography

We assign country-level labels for each user as follows: Fetching the profile
location of users, and checking for words such as UK, London, England, Wales,
Scotland, Northern Ireland, United Kingdom, we labeled around half of the
users. For the remaining ones, the unique list of their locations is passed to
Photon library’s geocode function777http://photon.komoot.de/, an open street
API to label countries given location names (e.g., city/locality/state etc.)
that a user might have used. Using this procedure, the countries of $\approx$
82.7% (77.3%) of users retweeted (replied to) by MPs were obtained.

For users in the UK, we dig deeper. A Twitter profile location may mention
only a city name (such as ‘Cambridge, UK’), rather than a specific
constituency (such as ‘Cambridge South’, which contains a small part of the
City of Cambridge and close-by villages). Also, citizens may work in one
constituency and reside in another which is close by. Thus, to identify
interactions between a user and their local MP, we translate user locations to
the ‘postcode area’ which represents the region (e.g., ‘CB’ represents
Cambridge in Cambridge-related postcodes), and consider all interactions
between users from that postcode area, and the MPs representing that postcode
as interactions between MP and a potential constituent.

Party affiliation

For every user who has mentioned an MP, we associate the party affiliation of
the MP with the user. Users have mentioned a mean (median) of 3 (2) MPs, from
an average (median) of 2 (1) parties. We affiliate each user with one party.
For users who have mentioned MPs from more than one party, we assign them the
party they have mentioned the most. Check: By checking for the presence of
party names in the profile description amongst a sample of $\approx$ 8K
Conservative and Labour supporters, we are able to correctly label nearly
7,400, yielding a 91.4% accuracy for our heuristic.

##  3 Dynamics of Citizen Attention

In this section, we approach the first research question, and estimate the
burden caused to MPs by their Twitter presence, by studying tweets directed
_towards_ MPs by other Twitter users. Our starting point is the stark
difference in Table 1 between the number of mentions that an MP gets (marked
as $\Leftarrow$), and the average number of tweets and reply activities made
by them (marked as $\Rightarrow$). This suggests that MPs could be overloaded,
and are not able to respond to all tweets directed at them.

To examine this, we introduce metrics that measure the spread of attention
load, in terms of mentions of MPs. We study the distribution of attention
across time for any individual MP, and across all MPs during any given time
window. We find that in any given time window, a small number of MPs are ‘in
focus’, and receive a large number of mentions. However, as the news cycle
moves on, other MPs’ activities come into focus. We then illustrate this
phenomenon using examples and discuss the implications.

###  3.1 Attention is focused during small time windows

To understand how overloaded the MPs are with the number of mentions they
receive, we first examine high activity periods. We define a period of _high
activity_ as a continuous sequence of days when the daily activity is
considered as ‘high’. Formally, given an MP $i$ and a threshold average number
of mentions $T_{i}$, we define a continuous sequence of days $R$ as a high
activity window for MP $i$ if it satisfies the property

$high\\_activity_{i}(R):\sum_{d\in R}v_{id}>T_{i}|R|$

where $v_{id}$ is the number of mentions obtained by MP $i$ on day $d$. In
this paper, we set the threshold for a high activity individually for each MP.
A day qualifies as a ‘high activity’ day for an MP if the number of mentions
received by the MP that day is higher than the personal average for that
MP888Other threshold definitions were examined, but not reported here due to
space.. Note that even if MPs only receive a large number of tweets during a
short time window, this will increase their personal average for the whole 2
month duration of our data set. We term the longest continuous run of days
during which an MP $i$ has more than his or her personal average number of
Tweets mentioning them – as their focus window $T_{i}^{\max}$. We can compute
the fraction $F(T)$ of MP $i$’s mentions that are obtained during a time
window $T$ as

$F(T)=\frac{1}{V_{i}}\sum\limits_{d\in T}v_{id}.$

Here, $V_{i}$ is the total volume of Tweets mentioning MP $i$ in our dataset,
and $v_{id}$ is the number of mentions obtained on day $d$ in the window $T$.
We define the Focus of MP $i$ as the fraction of mentions
$F_{i}=F(T_{i}^{max})$ obtained during the focus window $T_{i}^{\max}$. In
other words, Focus measures what fraction of an MP’s mentions during the whole
2 months period covered by our data set is concentrated during the small Focus
Window, _i.e.,_ the longest continuous sequence of days during which the MP
receives a higher than average number of Tweets.

(a) Focus

(b) Normalised Focus

(c) Churn

(d) Gini

Figure 1: The distribution of (a) Focus of MPs’ mentions: cumulative
distribution of unnormalised fraction of mentions $F(T_{i}^{max})$ obtained by
MPs during their focus windows. For comparison, the (much smaller) fractions
of mentions $F(T_{i}^{b})$ and $F(T_{i}^{a})$ of windows just before and after
the focus windows is shown. (b) Cumulative distribution of Focus, normalised
to yield a Focus of 1 if mentions were distributed evenly. Most of the mass is
several times over 1, confirming high information overload during focus
windows. (c) Churn: Box plots of the distribution of Churn values across time
windows of different sizes. The box extends from the lower to upper quartile
values of the data, with a line at the median. The mean is shown as a green
dot. Whiskers extend from the box to show the range. Flier points are outliers
past the end of the whiskers. (d) Gini: Box plot of distribution of Gini co-
efficients of the number of mentions received by MPs during time windows of
different sizes.

Figure 1a shows the distribution of Focus values for all MPs. For comparison,
the fraction of mentions $F(T_{i}^{b})$ and $F(T_{i}^{a})$ during similar-
sized windows before and after the focus window is also shown. Mentions tend
to fall off rapidly outside focus windows: in the windows immediately
preceding (following) these periods of intense activity, MPs on average
receive less than a quarter of the tweets received during the high activity
focus window.

By definition, Focus takes values between 0 and 1. We can get a sense of how
skewed focus values are by normalising the obtained focus based on the
expected fraction of mentions given the size of the focus window: if the
$V_{i}$ mentions are evenly across a total of $D$ days, the number of mentions
expected in a focus window of $|R|$ days is simply $|R|/D$. The observed Focus
$F_{i}$ can therefore be normalised as $F_{i}D/|R|$. If mentions are uniformly
distributed, normalised focus would be $\approx$ 1\. Figure 1b shows that this
value tends to be several times larger than 1, suggesting that a
disproportionately large fraction of the mentions for an MP might come during
their one concentrated focus period. In other words, MPs are in the limelight
only for a short period of time. Empirically, we find that the focus window
period typically lasts between 3–5 days.

###  3.2 Attention is unequal but focus moves among MPs

The previous discussion suggests that MPs receive mentions in a very bursty
manner: Outside their focus window, an individual MP contributes much less to
the overall volume of mentions directed towards MPs. Yet, as seen earlier,
there is an average daily volume amounting to about 68 mentions per MP. In
this subsection, we look at how mentions are shared among the MPs on a daily
basis.

We proceed by considering all possible time windows of different sizes from
1–10 days. For instance, we can have 5-day windows from Oct 1–5, Oct 2–6
$\ldots$ Nov 24–29. Our goal is to understand the effect of MPs not receiving
many mentions outside their focus windows and how mentions are shared during
any given window.

For any time window of a given size, we ask how many of the high activity MPs
of that window – MPs who receive more than their personal average number of
mentions – continue to receive high numbers of mentions in the next window.
Formally, we define the set of active MPs during a time window $R$ as

$active(R)=\\{i|high\\_activity_{i}(R)\\}.$

We can define the churn of a time window $R$ and the time window $R^{+}$
immediately following it as the difference in the set of active users between
the two windows:

$Churn(R)=\frac{|active(R)\triangle active(R+)|}{|active(R)\cup active(R+)|}$

where the numerator is the symmetric set difference of MPs who are active in
time window $R$ but not $R^{+}$ and vice versa, and the denominator is the
union of users in the time windows. Figure 1c shows that churn is high: Nearly
70% of MPs who receive more than their personal average of mentions during one
window are not able to sustain this level of activity in the next window.
Churn increases slightly as window sizes increase, with MPs finding it
difficult to continuously receive high numbers of mentions over larger time
windows.

While churn looks at differences across time windows, we can also measure how
unequal the attention distribution is _within_ a time window, by counting the
number of mentions each MP receives during the window and computing the gini
co-efficient across all MPs receiving mentions. Gini co-efficients vary
between 0.8 and 0.92, and the closer it is to 1, the more unequal the
distribution being measured. We can get a sense of the inequality for
different time windows of a given size by looking at the distribution of Gini
co-efficients. Figure 1d shows the distributions of gini co-efficients for
time windows of different sizes. The median gini co-efficient for all window
sizes is consistently above 0.8, indicating that in any single window, most of
the mentions are for a small minority of “attention rich” MPs.

Collectively, these results suggest that during any given window, a few MPs
are attention rich and receive a large number of mentions, but this set of MPs
shifts over a period of days, so there are no overall “superstars” who are
always at the centre of attention. The examples below serve to illustrate this
phenomenon.

###  3.3 Examples and implications

Figure 2: Timeline of number of mentions for 50 MPs who have more than half
their mentions occur during their focus window.

To better visualise the attention imbalance, Fig. 2 plots the daily mentions
volumes of 50 MPs with the highest focus. The focus values for each of these
MPs is more than 0.5; i.e., more than half of their mentions were received
during their focus windows. The spiky nature of the graph illustrates how
attention can be highly concentrated during short focus windows (typically 3–5
days), and moves on to other MPs after the focus period.

Priti Patel (@patel4witham) represents an interesting example: She was
International Development Minister until 8 Nov 2017, but was forced to resign
as a result of a scandal caused by unofficial meetings with Israeli ministers
while on a holiday in that country. This resulted in a barrage of focused
attention which fizzled out as other new stories cropped up. Similarly, Philip
Hammond (@PhilipHammondUK), Chancellor of the Exchequer, had a huge number of
mentions around the Autumn Budget (22 Nov 2017). Note that there is a smaller
spike for Hammond just before the budget when he mistakenly claimed in an
interview that there are no unemployed in the
UK999https://www.theguardian.com/politics/video/2017/nov/19/philip-hammond-
there-are-no-unemployed-in-uk-andrew-marr-show-video.

These two examples illustrate two different kinds of focus windows: The
attention towards Priti Patel was completely _unanticipated_ until the event
unfolded, whereas the increased attention towards Philip Hammond as he
presented the budget was predictable and could have been _anticipated_ and
planned for (although even here, unanticipated mistakes can create spikes, as
in Hammond’s case).

Anticipated attention is mostly for positive events and is in many cases
“manufactured” by the MPs, their staff and members of their party, following
prominent speeches or comments made in Parliament, as such successes are
advertised by sharing widely on Twitter. A common source for such high
attention events is activity during Prime Minster’s Questions, which happens
every single Wednesday at noon when the House of Commons is in session, and
usually involves a lively and sometimes raucous debate. When an MP makes a
particularly valuable (or sometimes particularly witty) contribution, it is
shared by the MPs themselves, or by others, on Twitter, and then gets widely
discussed.

By contrast, unanticipated focus windows, as with Priti Patel, include mostly
negative events for the MP. For instance, during the Westminster sex scandal,
Charlie Elphicke (@CharlieElphicke), a Conservative MP, was accused of sexual
misconduct and subsequently suspended from his party. The Westminster sex
scandal, which coincided with the ’#MeToo’ movement, includes several other
resignations and castigations which also received high attention and focus
values. Similarly, Labour MP Harriet Harman (@HarrietHarman) was criticised
for mentioning an anti-semitic joke on live TV.

The focus windows of 70% (35/50) of the MPs in Fig. 2 are for events that
could have been anticipated. However, perhaps unsurprisingly, unanticipated
windows receive unusually high attention – four of the top _five_ focus
windows are apparently unanticipated, and for events which generated
considerable adverse publicity. Thus, unanticipated attention can be all the
more difficult to manage because of the volumes. Furthermore, all of the top
five focus values are for MPs from the Conservative Party, which, as the
current ruling party, tends to receive a large amount of scrutiny. Four of
these also had ministerial level roles at one point or another and another
held a senior role within the party. The fact that even such prominent MPs
obtain more than half of their mentions during a small 3–5 day focus period
illustrates that the attention of citizens is highly volatile and all too
brief.

Focus periods represent opportunities for the MPs to raise their profile and
engage with the populace on issues important to the MP. Whether the focus is a
result of a positive event that the MP can take advantage of, or a negative
event the MP should defend against, being able to appropriately handle the
situation and manage the (brief) attention overload is critical. The next
section looks at strategies that MPs use to manage citizens’ attention both
during their focus periods and out of their focus periods.

##  4 Managing citizens’ attention

Incoming Tweets mentioning MPs (marked as $\Leftarrow$ in Table 1) can be seen
as a means for UK citizens and other Twitterati to engage with the MPs. In the
previous section, we established that MPs faced an information overload with
incoming tweets, especially during focus windows. In this section, we turn to
the second research question, and ask how MPs manage this attention load in
responding back, i.e., we also take into account the MPs’ outgoing Tweets
(marked $\Rightarrow$) in terms of Tweets, Retweets and Replies, and ask how
MPs engage with the rest of Twitter.

We identify two possible adaptations: The first consists of very selective
replies, with MPs prioritising interactions with users local to their
constituency region. The second is to employ staff who can help manage the
load. We find extensive usage of the first strategy, with MPs largely
prioritising their responses to users local to their region and to UK users.
However, only some MPs appear to be using additional staff who can help manage
their social media profiles.

###  4.1 Selective replies and localism in MP actions

Geography |  %Mentions $\Leftarrow$ |  %Retweet $\Rightarrow$ |  %Reply $\Rightarrow$
---|---|---|---
UK
(Constituency (C)) |  74.16 (C:28.8) |  90.39 (C:56.7) |  89.93 (C:59.04)
Commonwealth |  3.88 |  2.35 |  1.74
USA |  5.34 |  3.42 |  4.63
EU |  3.47 |  2.53 |  1.72
Others |  13.15 |  1.29 |  2.06
Total |  100% |  100% |  100%
Table 2: Geographic Distribution of incoming mentions ($\Leftarrow$) and
outgoing actions ($\Rightarrow$) in different geographical regions. Each
column adds up to a whole (i.e., UK + Commonwealth (British Commonwealth and
Overseas Territories) + USA + EU + Others = 100%). UK replies are further
subdivided into replies within the constituency region of the MP, and those
outside (The percentage local to MPs’ constituency regions is shown in
parenthesis as C:XX.YY%). Thus, for instance, from (Row 1, Col 1), 74.16% of
all incoming ($\Leftarrow$) mentions towards an MP come from within the UK. Of
these, 28.8% of mentions are from within each MP’s constituency, and the
remaining (71.2%) are from outside their constituencies.

MPs tend to be very busy, and being active online takes time away from their
other duties, and their real-world constituency [27]. Therefore, we expect
that MPs would be selective in who they respond to (even during non-focus
periods). We test this hypothesis in two ways. First, we check the geographic
areas of those whom the MPs respond to. Next, we check the category of the
Twitter handles they respond to – whether they are responding to other MPs, or
those that they follow or are following them.

Table 2 shows the percentage distribution of the incoming mentions
($\Leftarrow$) and outgoing actions ($\Rightarrow$) – replies and retweets,
among different geographic regions. In their conversations with Twitter users
not from the UK, MPs tend to favour responding to Twitter users from countries
that the UK has ties with: USA (5.3% mentions, 3.4% retweets, 4.6% replies),
British commonwealth and Overseas Territories (3.8% mentions, 2.3% retweets,
1.7% replies) and the EU (3.4% mentions, 2.5% retweets, 1.7% replies). Other
countries get only 2% of retweets or replies although they author over 13% of
tweets mentioning MPs.

As expected, a large fraction of mentions ($\approx$ 75%) come from the UK,
but MPs show selectivity, with over $\approx$ 90% of their retweets and
replies being made to UK-based Twitter users. This suggests that Twitter is
serving as a way for MPs to keep in touch with the UK electorate. MPs are even
more responsive to Tweets from within their constituency (identified as
mentioned in the Dataset section). As shown in parenthesis in Table 2, among
incoming ($\Leftarrow$) Tweets from within the UK that mention MPs, only about
28.8% come from within the constituency region. However, MPs’ outgoing
($\Rightarrow$) tweets prioritise interactions with such local tweets: 56.7%
of retweets, and nearly 60% of replies are focussed within the constituency
region represented by the MP101010Note that this analysis only includes the
78% of Tweets for which we are able to extract a valid geographic location of
the Twitter profile with whom an MP is corresponding. We also conservatively
remove 60 London-based MPs from consideration because most MPs interact with
journalists, lobbyists etc., who tend to be based around London. London MPs
therefore appear to have an even higher localisation factor, with nearly all
their responses to users within their region..

Figure 3: MP responses (retweets and replies) put into mutually exclusive
categories. A ‘Self’ response is a retweet or reply to MP’s own tweet or
reply. ‘Mentioned’ is a response to a tweet mentioning the MP. ‘Following’ is
a response to a tweet which does not mention the MP but appears on their
timeline because the MP follows the person. ‘Protected’ tweets and replies are
not available to analyse. ‘Others’ comprises the remainder of retweets and
replies.

We then compare the responses – replies and retweets – sent to different
categories of people. Focusing first on the replies, Fig. 3 shows that
$\approx$43% of replies are to tweets that mention the MP directly; thus MPs
are using their replies to engage directly in conversation with those that
mention them on Twitter. In contrast to replies, most (57.8%) of the retweets
are for those that the MP follows. In other words, MPs are retweeting other
users even without the MP being mentioned. This is not surprising, since
Tweets from those that MPs follow appear on MPs’ timeline, and MPs may retweet
what they find interesting. However, a disproportionate number of retweets are
_tweets of other MPs, and in particular, MPs from the same party_ : on
average, other MPs constitute 7.4% of the following numbers of an MP. However,
nearly 17% of all retweet actions are made on Tweets of other MPs. A further
6% of retweets are for posts made by their party’s official Twitter handle.
Nearly 96% of the MP-MP retweets are for MPs from the same party. Thus, it
appears that MPs are using retweets as political marketing, to boost their
party’s message (termed as party maintenance by [12]).

Figure. 3 also shows that a small but significant minority of replies (9.4%)
are from the MP to themselves. This turns out to mostly be Tweetstorms – a
single post which has been split into series of related tweets (posted in
quick succession) because of Twitter’s character limit. On Nov 7 2017, close
to the midpoint of our data collection period (Oct 1–Nov 29), Twitter did
expand the character limit from 140 to 280, but this hardly affected the
volume of self-replies: Prior to Nov 7, there was an average of 36.02 self-
replies per day from all MPs, and after this date, the average was 34.17 per
day. Thus, it appears that in many cases, MPs need a larger text limit than
280 characters to discuss substantive topics.

###  4.2 Help from Staff?

Figure 4: MPs’ outgoing ($\Rightarrow$) activity by day of week and Twitter
client used. The ‘row bar’ on right side and ‘col line’ on top represents the
counts of total data by Twitter client and day of week respectively. Dark blue
represents the lowest activity and dark red the highest. Lowest activity is
found trivially on Saturdays and Sundays and the highest activity is on
Wednesdays, corresponding to Prime Minister’s Questions. Android, iPhone and
iPad clients are the most popular.

The previous subsection identified selective responses and prioritisation of
constituents as one way for MPs to cope with the load of engaging on Twitter.
As an alternate or complementary strategy, MPs may also employ staff
designated as Communications Officer or Senior Communications Officer.
Permitted (non-party political) activities of such staff include establishing
a social media presence in the constituency, publicising surgeries, following
up on social media queries and comments, publicising the MP’s parliamentary
duties on social media and proactive and reactive communications with all
media [28].

We cannot determine with certainty which tweeting instances originate from MPs
and which from their staff, but we can find suggestive evidence. For instance,
if multiple people are managing an account, it has the potential to be
detected as a bot by the Botometer tool [29, 30]. Only 45 of the 559 MPs
($\approx 8\%$) in our data are detected as bots by this
API111111https://osome.iuni.iu.edu/. We can also look at the Twitter client(s)
used, as identified by the ‘statusSource’ field of Tweets collected from the
MPs’ handles. A total of 47 different sources are used by the 559 MPs, ranging
from the Twitter Web Client and TweetDeck, to Twitter for iPhone or Android.
Fig. 4 shows that posting activity is mainly through the Web, whereas replies
and retweets happen through personal devices such as iphone or android
smartphones. Web clients can potentially come from multiple computers
belonging to different staff. We also find that MP Twitter handles which use
iPhone do not tend to also use android, and vice versa. Furthermore, the Web
Clients are active mainly during weekdays. These patterns are suggestive of
the MPs themselves, or one selected member of their staff handling the
responses (replies and retweets), with the possibility of multiple staff being
delegated the duty of posting new tweets, which may consist of advertising the
MPs’ activities; sharing videos and transcripts of their speeches etc. Note
that the highest activity for the Web Client is on Wednesday, corresponding to
Prime Minister’s Questions, which, as mentioned before, is a highly advertised
and popular activity. We also find that for over half the MPs, more than 80%
of their replies and retweets come from one source (Fig. 5), which is further
indicative of one person managing their Twitter presence.

Figure 5: Cumulative Distribution Function (CDF) of the fraction of an MP’s
activity which is captured by their most commonly used source. For over half
the MPs, more than 70% of their original tweets (posts), and 80–90% of their
retweets and replies come from one source, which could imply one person
managing their handle.

These observations can potentially be explained by the rule that MPs may not
claim for party political and campaigning activities [28]. Some activities
identified above, such as retweeting their party position, may not be
allowable due to this rule, and would therefore need to be undertaken by the
MP rather than their staff. This hypothesis is in alignment with our finding
in Fig. 5 that posts (original Tweets by MPs) are more likely to come from
multiple sources than retweets – recall that posts tend to advertise MPs’
parliamentary duties such as speeches and remarks made in the House of
Commons, whereas retweets tend to amplify messages of other party members or
the official party handle. MPs are also more likely to spend their limited
budgets on communications staff only if certain conditions are met, for
example MPs who are more junior and need to advertise themselves, or are in
marginal seats [31].

##  5 Tone of political discussion

Finally, we move to the third research question, and inquire about the nature
and tone of the Twitter conversations. We are motivated to understand whether
this platform, which appears to have gone mainstream in just five years since
the first studies, and is being widely used by nearly all MPs, is contributing
positively to the political debate.

This is an important question to answer, as various events such as Brexit have
led to a highly charged and polarised political atmosphere in the UK, and both
scholars and broadsheet newspapers have argued that the “middle has fallen
out” of UK Politics [15, 16, 32, 33]. There is also wide concern that
political discussion on Twitter involves aggressive and “trashy” language [34,
14].

Given the large scale of our data, we take a broad-brush approach, and focus
on understanding whether there is cross-party political discussion between MPs
and citizens, and on the tone and sentiments of the discussion as discoverable
by tools such as LIWC 2015 [35].

###  5.1 Cross-party political conversations

Figure 6: Cross-party conversations in $\Leftarrow$ Fraction of mentions from
citizens who support (follow) MPs from one party to MPs of other parties. Each
row adds up to 1, including mentions from citizens to MPs of their own party.
Figure 7: Cross-party conversations in $\Rightarrow$ Fraction of MPs from one
party replying to citizens who follow (support) MPs of other parties. Each row
adds up to 1, including replies from MPs to citizens of their own party.

To quantify polarisation, we divide users based on the party they support
(using the method specified in the dataset section), and ask the extent to
which they interact with MPs of other parties. Our focus on communications
between people in power (MPs) and ordinary citizens distinguishes us from
previous work that looked at how ordinary users have polarised [36, 37, 38,
14]. Additionally, the UK is a multi-party system, which provides an
interesting differentiating dimension to prior work, which has typically
looked at a two-way polarisation, focusing on two sides of a conflict [36,
38], or on two-party systems like the USA [14, 37].

In Figure 6, we examine Tweets from UK users that mention MPs, and find that
regardless of the party they support, there is a lot of cross-party talk.
Specifically, we focus on the top five parties in terms of MP numbers, and
find that supporters of all parties tend to tweet mentioning MPs of the
Conservative party, which is currently in power. Exploration with LDA topic
modelling (not discussed in the paper) suggests that citizens are interested
in topics such as the budget, Brexit, and resignations of ministers (due to
scandals during the period of our collection). All of these have a natural
focus on ministers and MPs of the governing party, which helps explain the
surprising amount of cross-party mentions. Conservative supporters have the
largest proportion of within-party mentions (69.8%). This suggests that users’
Tweets are directed at topical and current issues, and people involved in
those issues, rather than the MPs they follow and the party they support,
indicating a healthy attitude of engagement beyond the “echo chamber” of
people who have similar views in online conversations .

An extreme example of cross-party conversation is the case of the Liberal
Democrats (Lib Dems), whose supporters have more mentions of Conservative MPs
than the 12 sitting MPs whose Twitter accounts they follow (Fig. 6). In turn,
Lib Dem MPs talk more to Labour supporters (who are more numerous) than those
that follow them (Fig. 7). A similar discrepancy between the interests of the
MPs and its party supporters is observed with MPs of the Scottish National
Party (SNP), whose replies have a greater proportion of replies to Labour
supporters than conservative supporters, whereas ordinary citizens who follow
SNP MPs talk more with conservative MPs than to Labour MPs. SNP and Lib Dems
are ideologically closer to Labour than the Conservative
Party121212https://yougov.co.uk/news/2014/07/23/britains-changing-political-
spectrum/, https://www.politicalcompass.org/uk2017. We therefore conjecture
that the discrepancy may be caused by MPs replying to those of a similar
ideology as them, whereas citizens, who take a more questioning attitude (see
next section), are engaging directly with the opposing view of the
Conservatives.

Figure 8: LIWC scores for variables or categories reported by LIWC. Scores are
normalised by dividing by the base rate scores expected by LIWC 141414; if
score is greater (resp. less) than 1, as marked by the horizontal dotted line,
LIWC score is more (resp. less) than base rate. Corpus of $\Leftarrow$ tweets
mentioning MP names is marked white; $\Rightarrow$ tweets from MPs to citizens
are marked gray. A category is marked in red (resp. orange) if the score for
$\Leftarrow$ (resp. $\Rightarrow$) is 50% more than for $\Rightarrow$ (resp.
$\Leftarrow$) corpus. 1414footnotetext: http://liwc.wpengine.com/compare-
dictionaries/

###  5.2 Language and sentiments

Given the surprising result of substantial cross-party talk between MPs and
users, it is natural to ask what the tone of the conversation is between MPs
and citizens – i.e., whether the discussion between MPs and citizens is a
civilised discussion or an aggressive slanging match as in recent political
campaigns [34]. To measure this, we use LIWC to summarise the language of the
citizens mentioning MPs ($\Leftarrow$) on the one hand, and MPs replies to
citizens’ Tweets ($\Rightarrow$) on the other. We look for differences and
similarities in language usage between these two categories, to understand the
tone of the discourse between MPs and citizens.

LIWC provides 94 dimensions along which to measure different aspects of
language use [35]. For each dimension, it also gives the base rates of word
counts to be expected in normal usage. Fig. 14 shows the LIWC scores obtained
for each dimension of language use, for both $\Leftarrow$ and $\Rightarrow$,
normalised by the expected base rates of usage of that dimension.

To take a principled approach, we focus on the LIWC categories which see
higher than base rates of usage or where there is substantial ($>50\%$)
difference between $\Leftarrow$ and $\Rightarrow$. Using this approach, we can
make the following observations from Fig. 14:

  1. 1.

Both MPs and citizens show more positive emotions than LIWC base rates, which
could be suggestive of a respectful or appreciative discussion.

  2. 2.

However, citizens’ incoming ($\Leftarrow$) Tweets also express more than the
base rate of negative emotion, anxiety and anger, suggesting more conflict in
some conversations. The citizens’ Tweets also raise a lot of questions,
heavily using interrogatives, and question marks in their language. This could
potentially be related to scandals and related resignations during the period
of study.

  3. 3.

Unusually for political conversations, there is a large amount of sexual and
sex-related words, owing to the Westminster sex scandal which erupted in the
wake of the #MeToo movement, and led to the resignation of several ministers
and MPs during the period of our study151515Wikipedia provides the most up to
date account at
https://en.wikipedia.org/wiki/2017_Westminster_sexual_scandals. Sky News also
has a useful timeline of one of the most intense weeks:
https://news.sky.com/story/a-week-in-westminster-sex-scandal-
timeline-11111086.

  4. 4.

Perhaps because of this scandal, citizens’ incoming ($\Leftarrow$) tweets
towards MPs have a higher than base rate of “moralising” language, using words
such as ‘should’, ‘would’ (marked as discrep), and ‘always’, ‘never’ (marked
as certain).

  5. 5.

Words relating to power and risk figure highly in the MPs’ language as well as
the citizens’.

Dimension |  Difference |  Dimension |  Difference
---|---|---|---
Swear |  90.5% |  Nonflu |  63.2%
QMark |  79.2% |  Shehe |  63.2%
Filler |  78.4% |  Anger |  58.2%
Exclam |  72.2% |  Leisure |  58.1%
Negate |  66.4% |  Male |  56.6%
Table 3: LIWC categories which make it less likely that MPs respond back to
citizen. This table shows the percentage difference of some LIWC dimensions
that corresponds to the decreased likelihood of MPs in making responses to
incoming ($\Leftarrow$) mentions if these LIWC categories are present.

Table 3 shows that MPs appear to take into consideration the language of a
mention in deciding whether to reply back. Incoming ($\Leftarrow$) mentions
from citizens which exhibit anger or use swear words, question marks, filler
words and other non-fluencies (e.g., “err”, “um”, I mean, you know, etc…) are
less likely to elicit a reply.

Our broad-brush approach is intended only to provide a flavour of the tone of
discussion. It appears to indicate that in our study period, which was rich in
scandals that affected multiple MPs and ministers, citizens are using Twitter
as a platform to freely and directly question their representatives and
express negative emotions, anxiety and anger, as they are entitled to.
However, they also show higher than base rates of positive affect and
appreciation where warranted. In return, MPs appear to exercise restraint,
using higher than base rates of positive language, and avoiding using or
responding to negative language (which could escalate conflict). We conjecture
that the public nature of Twitter leads to MPs being conscious of the effect
of their words on their image and public perception, providing a platform for
civilised discourse. We note that this kind of behaviour may be partly due to
our focus on interactions between MPs and citizens. Previous studies in the UK
context have found that MPs have indulged in attacks on other politicians,
especially in election contexts [18].

##  6 A possible future of online Twitter engagement

We conclude with a brief case study of a novel way in which the immediacy of
Twitter was used to improve democracy by allowing citizens a part in creating
an Act of Parliament, and discuss how it speaks to our three research
questions:

Individual MPs in the UK Parliament are able to submit Bills (also known as
draft legislation). These are known as Private Members’ Bills. Priority is
given to Government-sponsored Bills, so to ensure that a proportion of Private
Members’ Bills have a chance to become law, there is a ballot of MPs each year
to assign priority for the limited amount of debating time available. However,
even Bills coming high up in the ballot are unlikely to be passed unless they
have the tacit or explicit support of the Government.

Chris Bryant, a Labour back bench MP, came top in the ballot for the 2017-19
session, and therefore was eligible to propose a Bill. However, as a member of
the Opposition Party who is also not among the prominent “front bench” MPs,
his bill would have faced an uphill task. Bryant launched a consultation on
Twitter in July 2017 (before our study period), putting forward six possible
Bills and asking Twitter users to choose their favourite through an online
survey. 45,000 people
participated161616https://www.youtube.com/watch?v=RqkSSQ3bAaA, and the winner
of the poll was a proposal to provide additional legal protection to emergency
service workers, as a result of reports of assaults by members of the public
during emergency call-outs. Bryant introduced the Assaults on Emergency
Workers (Offences) Bill 2017-19. This bill was greatly strengthened by the
evidence of public support, and was one of the few Private Members’ Bills
supported by the Government. Tweets during the passage of the Bill through
Parliament used the hashtag #ProtectTheProtectors, and received a high level
of engagement. On 13 September 2018, the Bill received Royal Assent, the final
stage on the way to becoming a law. It is now been signed into law as the
Assaults on Emergency Workers (Offences) Act 2018.

This innovative approach, and the effective use of Twitter, surveys and
hashtags has enabled the public to follow the progress of the Bill throughout
its timeline and participate by providing direct comments, creating an
experience closer to direct democracy [21]. It has also acted as a means of
garnering publicity for Bryant. We can relate this case back to the three
research questions: RQ-1 and RQ-2 seek to understand how much load is incurred
by the MPs, and how they manage this load. Clearly, with 45,000 responses to
the initial survey, this was a huge effort. The use of a survey tool was
critical to manage this huge load and summarise their response. However, the
MP also struggled to cope: Analysis of Tweets during the survey/poll in July
2017 reveals a typical pattern of activity during a focus period (Fig. 10).
Twitter users mentioned the MP (in red), often to make suggestions or enter
into dialogue, but as shown by the blue line, he was unable to respond to
many. This showcases both the potential for direct and participatory online
engagement via Twitter but also the drawbacks, if excessive activity makes a
personal response impracticable.

To study RQ-3 on the nature and tone of the discussion, we focus on the final
day of high activity during the passage of the Bill. Fig. 19 shows that the MP
gained more than 500 mentions on just one day (Sep 13), when the Royal Assent
was obtained. With a high number of mentions like this on a single day, it is
hard to respond to each mention; reiterating again that although the process
innovatively unlocked the participatory potential of Twitter, the burden of
response during such direct engagement remains an issue (RQ1). To manage the
load, the MP did a ‘thank you’ post as a collective response to all (RQ2). As
an event where high attention was anticipated, the messages were mostly
appreciative and complementary to the MP171717The volume of the whole
conversation from July 2017–Sep 2018 permits only a cursory examination, but
also seems to incorporate a mostly civil and respectful tone; with suggestions
and requests for changes, inquiries as to why the bill is required when
assault by itself is already considered a crime, as well as messages of
encouragement.. Although as expected the majority of congratulatory Tweets
were from Labour supporters, it is remarkable that close to 28% of tweets come
from non-Labour supporters, showing the broad multi-partisan support for the
Bill, offering hope for constructive participatory democracy through the
innovative use of digital tools like Twitter.

Figure 9: Left:#ProtectTheProtector tweets burst on the day of Bill getting
passed in House of Commons. Right: Status of the Bill191919as on 15th Sep
2018. 1919footnotetext:
https://services.parliament.uk/bills/2017-19/assaultsonemergencyworkersoffences.html

Figure 10: Left: Survey tweet links, MP Chris Bryant $\Rightarrow$ and
$\Leftarrow$ Right: MP Chris Bryant’s thanking video after his proposed bill
was passed the Committee stage.

##  7 Discussion and further work

As early as 1774, the political philosopher and MP Edmund Burke stressed the
importance of understanding the views of constituents [39]. Despite this early
recognition of its need, active engagement with constituents outside the
election period was rare until the mid-twentieth century [31]. However, it is
now seen as a necessity by MPs, and this is being increasingly facilitated
through digital means.

Our research agenda involves understanding the usage of Twitter as a new form
of continuous citizen engagement. In this work, we identified Twitter as an
interactive platform which seems to have become part of mainstream usage, used
by nearly all MPs, and with a high volume of activity. We investigated the
dynamics of the load imposed by the increasing volumes of Twitter activity and
the consequent attention directed towards MPs. We showed that attention can be
highly focussed, with a large proportion of the total activity directed at an
MP occurring during short focus periods of 3–5 days. MPs use selective replies
and prioritisation of local or constituents’ concerns as a way to manage this
high attention load. They use their Twitter presence strategically, balancing
their role as party representatives with the role of hearing and responding to
their citizens’ needs. We also find that Twitter presents possibilities for
immediate and direct discussion, leading to new possibilities for cross-party
discussions on a level playing field and therefore holds promise for bridging,
or at least initiating conversations, across the political divide.

However, there is additional work to be done. For instance, despite its
current widespread use, there still remain some concerns about how
representative Twitter is, as a (or the main) platform for digital citizen
engagement. Furthermore, we need to go beyond the current observational study,
to conclusively understand whether Twitter engagement is helping MPs in their
day-to-day duties or if it merely adds to their burdens. Other questions –
such as whether Twitter remains a place for “empty” conversations, or whether
actual Government or Parliamentary activity result from these online
discussions – need closer scrutiny. Case studies such as the creation of the
Assaults on Emergency Workers (Offences) Act 2018 point to ways in which
Parliamentary activity can be facilitated or directed through Twitter and
other online means, but these are early examples, and there may be other
mechanisms that become more commonplace in the near future. In this context,
it may be interesting to compare with other more formal routes, including
e-petitions, which can get discussed in parliament if sufficient numbers of
citizens declare interest in an issue.

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