Research Proposal : Political Representation of Different types of voters on Social Networked Sites.

Candidate Number: 14098
London School of Economics and Political Science
GV249: Research Design in Political Science
Summative Coursework
Research Design Proposal
Political representation of different types of voters on
Social Networked Sites.
Date: 22nd
March 2016
Word Count: 3098

Introduction
Political representation requires effective measurement of citizens’ collective political
preferences to function. A fundamental characteristic of democracy is that citizens
ultimately possess the power to shape and influence policy-making by expressing political
preferences. This can be directly through a plebiscite or a referendum. However, to do this
for all political decisions greatly slows the democratic process and requires unfeasible
investments of time and attention. Modern democracy is thus indirect where politicians are
chosen and legitimised through elections to represent people in the political process
(Dalton, 1985) to effectively and responsively represent requires representatives to have a
complete and current understanding of their electorates’ collective political preferences.
Approaches of deducing collective preferences from the previous century are limited. Voting
during elections expresses political preferences before and after a representative’s terms of
office, assess them in general rather than specific, and face logistical difficulty in revealing
weak or mixed preferences of citizens due to the geographical distance to polling stations.
Attempts to overcome these limitations for greater insight include electronic voting that
reduces logistical difficulty (Everett et al., 2008) as well as polls, surveys and grassroots
operations that gather preferences in-between elections on specific issues (Gaventa, 2004).
Traditional attempts are being overshadowed by recent technological developments. Social
networked sites (SNS) have become integral parts of modern societies as online platforms
for users to express themselves, react to the expressions of others and curate how they do
so on multiple issues (Boyd and Ellison, 2010) including politics. Political actors recognise the
potential from this emerging online discourse in influencing real-world voting behaviour
(Bond et al., 2012) (Effing, van Hillegersberg, and Huibers, 2011) and have increased their
online presence to influence it in their interest. When this politicisation of a digital medium
converges with Big Data innovations that enable data of user behaviour to be collected,
managed and analysed in much greater amounts (Andreessen, 2011) in order to deduce
preferences, an immensely powerful platform emerges that potentially allow for cheaper,

more immediate, nuanced and comprehensive forms of political preference measurement
and aggregation superior to existing methods (Neuman et al., 2014).
Deductions can only be made by understanding how representative the preferences
measured on SNS are of the overall electorate. People have different levels of political
engagement and express them differently across different mediums. Moreover, people can
interact differently on SNS compared to traditional mediums. The logistical barriers are
lower, allowing those with weaker or more mixed preferences to express themselves more,
but those with higher levels of engagement are able to express themselves more frequently
and over a longer period. Currently, which types of users interact more on Facebook
politically and the extent which the SNS amplifies or muffles political preferences is
unknown.
The proposed research question “Which types of voters are more politically represented on
Facebook and to what extent?” engages this puzzle. Facebook was chosen as the medium
given its relative dominance in terms of overall usage (Alexa.com, 2016) and user numbers
(cdn2.business2community.com, 2013) amongst alternate forms of SNS. Moreover, the
platform accommodates and invite a wide variety of content and interactions that make it
relatively ideal to generate political preferences. The research proposes an inductive
approach to answering the question by first examining the causal literature before
generating data for a descriptive approach. The literature of which types of voters are more
politically expressive on SNS will be explored a method of measuring via Facebook
interactions their expressiveness, and thus degree of representation, is outlined.

Literature Review
There exists much research on political representation complementary to what is proposed.
The idea of political representation in this research is “indicative” rather than “responsive”
(Shapiro et al., 2010); that preferences expressed on SNS are congruent to society’s
preferences rather than on whether they are enacted by representatives (Dalton, 1985).
Such representation is constrained in terms of social characteristics (Norris, 1997), rather
than political preferences (Conover et al., 2011) (Rainie and Smith, 2012). The research
seeks a sociological explanation to levels of political engagement (Phelps, 2006). Lastly, such
representation concerns itself on obstacles and remedies affecting private citizen
representation in the political process rather than on factors affecting potential or actual
electoral candidates (Norris and Lovenduski, 1994) (Saggar, 2000) (Bird, 2003).
There exist much complementary research involving the intersection of politics and SNS.
This research focuses on preference expression and measurement based on publicly
available data of SNS users rather than on the potential outcomes of preference expression
online (Tang and Lee, 2013), in the ballot box (Bond et al., 2012) or in alternate forms of
political expression (Gladwell, 2010) (Morozov, 2009) (Anduiza, Cristancho, and Sabucedo,
2013). It focuses on activities solely on the platform rather than how online activity can be
used to predict, preference expression and measurements in offline settings (Tumasjan et
al., 2010) (Asur and Huberman 2010) (Gayo-Avello, 2012) (Cummings, Oh, and Wang, 2011).
Finally, the research constrains itself to Facebook in contrast with other papers that looks at
representativeness in other SNS such as Twitter for reasons political (Barbera and Rivero,
2014) or in general (Mislove et al., 2012).
Having established the scope of the proposed research the proposal will now examine the
literature for political and SNS engagement to qualitatively explore why certain social
classes have greater political representation on Facebook. Previous research has explored
general factors for political or Facebook engagement (Boulianne, 2015) (Valenzuela, Park,
and Kee, 2009). However, the proposed research examines how they relate to each other

specifically with the chosen user types to explore how they determine overall political
representation on Facebook. We choose to concentrate on varing users based on 2
politically significant variables; “Gender” and “Ethnicity”. We will not test other variables
such as location (Barbera and Rivero, 2014), youth (Phelps, 2006), education levels
(Abramson and Aldrich, 1982) and socioeconomic status (Verba and Nie, 1972). Selection
bias may occur given that these variables may act as confounders to the Independent (IV)
and Dependent Variables (DV). The research is unfortunately limited in this regard given
Facebook privacy settings and existing data mining capacity prevents the accounting of
these omitted variables.
There is an empirical and theoretical puzzle regarding the variation of political
expressiveness based on Gender. Empirically there is a global gender gap with women being
less politically expressive, informed and interested than men (UN Women, 2013) (Inglehart
and Norris, 2003). However, there seems to be a reversal of this in Western countries given
how women have exceeded men in terms of voter turnout (CAWP, 2015) (Inglehart and
Norris, 2003) although there is gender parity (The Electoral Commission, 2004) in the case of
the UK. Initial reasons for the lag in Western developed countries like the UK (Engeli,
Ballmer-Cao, and Giugni, 2006) include disapproval of the rising suffrage movement
(Firebaugh and Chen, 1995), the desire to follow the lead of their husbands (Manza and
Brooks, 1998) and socialisation that discourage women from developing political awareness
or place them in situations that would allow them the opportunity to do so (Welch, 1977)
(Mueller, 1987). However, these reasons reversed over time due to increased proportion of
working (Clark and Clark, 1986), educated (Welch, 1977) women, a rise in female social
status (McDonagh, 1982) and the growing feminist movement (Andersen, 1975) that
emphasised the importance of political expression to celebrate women’s rights (Hammond,
2014).
This complements a similar puzzle regarding the variation of SNS expressiveness based on
gender. Women, although initially using the internet less than men (Bimber, 2000), over
time have grown to use Facebook more (Duggan et al., 2013) although this gap is closing

(Helsper, 2010) (Anderson, 2015). SNS is popular amongst women as the platform caters to
the greater tendency of women to build and maintain relationships (Tannen, 1990) (Lenhart,
2009) (Raacke and Bonds-Raacke, 2008). However, this popularity does not necessarily
translate to political expressiveness on SNS with the current literature indicating either a
gender gap (Brandtzaeg, 2015) or parity (Acquisti and Gross, 2006). Factors for SNS
engagement are complimented by those involving general, gendered political
expressiveness.
For the other IV, ethnicity, there is a similar puzzle regarding the variation of political
expressiveness. Different ethnicities have different levels of expressiveness (Uhlaner, Cain,
and Kiewiet, 1989) and the sub-groups within them cannot always be homogenised (Lien
1994). However, we can examine if members of ethnic majorities or minorities are more
expressive. In the UK, the effect is not homogenous. South Asian ethnicities vote more than
the majority White ethnic group while those of the other minority groups vote less (Ipsos
Mori, 2016). Minorities can be more politically expressive due to a greater proportion of
fellow minority voters in an electoral district (Fraga, 2015), a greater number of candidates
of the same ethnicity standing (Rocha et al., 2010) in a general election, or if a particular
political party is making efforts to reach out to them (Phelps, 2006). More internal causes
for greater expressiveness include greater consciousness of ethnicity (Verba and Nie, 1972)
and of ethnic group’s minority status (Olsen, 1970). Conversely, minorities can be less
expressive if they feel alienation from, prejudice (Verba and Nie, 1972) and political mistrust
towards the current government (Shingles, 1981).
Finally, variation of SNS expressiveness based on ethnicity initially presents a puzzle. The
literature is lacking in this area because ethnic information is often unavailable for practical,
legal, or political reasons (Chang et al., 2010). However, SNS proliferation means that there
is slight (Hargittai, 2007) or no (Mislove et al., 2011) ethnic bias regarding SNS, and it can be
reasoned that Facebook has the least bias as it’s the most widely used platform regardless
of ethnicity (Duggan et al., 2015). Political expressiveness on SNS by ethnicity seems to be
mainly determined by general factors of ethnic political expressiveness.

Data Set
User data will be gathered from the from the timeframe of 26th May 2016 to the 22nd June
2016 and the official Facebook pages of all candidates who did not lose their electoral
deposit in the United Kingdom (UK) General Election 2015. The data set will be extracted
using the R software, accessed via the FB Graph API and stored on Github rather than
interviewing. Although we lose the ability to gain in-depth insights by asking specific
questions, the data science methodology obtains larger and richer behavioural data sets
directly from the platform thereby reducing variance and increasing result accuracy.
Moreover, it does not need to deal with potential inaccuracies caused by experimental bias,
subject bias (Hogg and Vaughan, 2011), misinformative phrasing, unintentional question
priming, bad respondent memory or question answering fatigue.
The SNS platforms of MP candidates are chosen in order to examine specifically how
political representativeness is affected from a politician’s perspective. High volumes of
measurable political interactions are expected given the incentives of citizens to express
preferences to politicians through this online medium as a way to indirectly shape policies in
parliament. These politicians have the incentive to generate content that invites such
political interactions if they wish to gain a better understanding of their electorate’s political
preferences and to engage them in order to be successful in elections. The focus is on
official FB pages because these were the most public platform that representatives control.
They are theoretically the most effective nodal point for the most frequent and diverse
amount of political interaction. Data will be gathered from all MP candidates because the
incentives hold true even if they lost the election. Moreover, excluding these losers
excluded interactions generated by minority political preferences on platforms of minority
party candidates that stood a low chance of being elected under the UK’s majoritarian “First
Past the Post” (FPTP) system (http://www.electoral-reform.org.uk, 2010). Lastly, candidates
will be excluded if they attract minimal political engagement as they will likely have less
politically relevant SNS interactions. Using the electoral deposit criteria as benchmark,
candidates with less than 5% of the vote share were excluded (http://www.parliament.uk,
2010).

The dataset will be restricted solely to the UK. The analysis is restricted to a single country
to better identify any possible confounding factors in the analysis and to allow user
accounts to be more easily categorised into the different independent variable categories.
The UK, in particular, is chosen given its high democratic rankings
(http://democracyranking.org/, 2015). Despite the majoritarian constraints of the FPTP
system (BBC, 2016), its systems have a wide political diversity of representatives and parties
and have the incentives to both citizens and MPs to give and invite political interactions
without fear of political persecution.
The data collection timeframe is set 4 weeks before the “Brexit” Referendum starting from
the day before the actual poll on 23rd June 2016. The timeframe is set with reference to a
single-issue referendum, rather than a general election, over a longer period of time, or
after the referendum, in order to control the issues engaging the voters. “Brexit” was picked
as the issue because it is of national, broad and decisive importance. This allows for
significant variation in levels of political engagement to be expressed across all segments of
the UK electorate. Finally, this allows a timeframe when most politicians have adopted the
use of Facebook pages and gives us the richest possible dataset.

Independent Variables
Political expressiveness on SNS is conceptualised as interactions performed by a single user
who can be classified based on the information they reveal on their account into categories.
For the 1st IV, “Gender”, users will be classified as either “male”, “female” or “custom”. The
3rd category accommodates all non-heterosexual orientations. For the 2nd IV, “Ethnicity”,
users will be classified as either “Majority” or “Minority”. Those of “White” ethnicity, the
majority ethnicity in the UK (http://www.irr.org.uk/, 2016), will be classified in the former
category. The latter category accommodates all other minority ethnicities including those of
mixed race of any composition.
Gender will be inferred based on the “Gender” field of the user’s account and, failing that,
the stated name in the “Name” field. Ethnicity will be inferred from only the stated names in
the “Name” field. Inference of gender and ethnicity from the “Name” field is done by
training a name classifier on the R platform similar to the approach that is taken in
(Betebenner, 2015). A codebook to infer gender and ethnicity can be obtained by using the
database of first names and surnames in (Treeratpituk and Giles, 2012) that was compiled
from Wikipedia profiles. Accounts that cannot be classified by gender or ethnicity with more
than 95% probability are excluded from the data set. Accounts without any listed “friends”,
“followers” or “following” or with cities from outside the UK being listed in the “current
location” or “hometown” field are also excluded to mitigate the impact of auto-like bots and
foreigners from the data. These exclusions inevitably affect members of certain
demographics, such as citizens of mixed ethnicity or the UK diaspora respectively, but it is a
necessary trade-off to maintain the overall integrity of the data set.

Dependent Variable
The average number of interactions by users on the different pages for each user category
will be used as the DV to measure political expressiveness. Interactions will be defined as
likes and reactions towards the original post and user generated comments and replies.
Each interaction will count once towards different categories based on the attributes of the
user. Once all interactions within the time frame have been processed in this manner, the
total volume of interactions attributed to each category will be divided by the total number
of users in each category that have interacted in the timeframe to get the mean number of
interactions made by users in each category.
There are limits to modelling political preferences in this manner. Although the ability to
perform multiple interactions allows users with stronger or more mixed preferences to
perform more interactions than in traditional forms of political preference expression that
follow the principle of equally weighted votes, (Lovett, 2009) some partisan users have the
incentive to increase perceived popularity and content reach by interacting without
processing the political content. However, this still reflects how citizens make political
judgements expressions based on a variety of reasons and this still contributes to our
understanding of how political expressive SNS users are.
To model political preferences more proportionally “Comments” and “Replies” as forms of
interaction will be excluded to ensure that expressiveness is quantified by interactions that
require the same cost to perform. Only metrics that require a single click or tap will be used
as the amount of effort for other interactions takes longer and also varies based on the
length and thought put into crafting them. “Shares” will be excluded even if they satisfy the
above consideration because privacy settings often prevent identification of attributes of
users behind each interaction. This limits their ability to contribute to the research in a
meaningful way. All interactions made by the original page will be excluded and steps will be
taken to identify and exclude suspected automated accounts that have been programmed
to automatically to increase perceived popularity and content reach. This is to ensure that
only citizens’ political expressiveness are measured.

Conclusion
On a broader level, the research proposed has limitations which open the door for future
research. Firstly, empirical data will validate mathematically the relative extent the different
social classes are represented on Facebook. However, the research will be unable to assess
the causal weightage of the different reasons in the theoretical literature that contribute to
these cumulative results as well as the confounding effect of other social classes mentioned
previously besides gender and ethnicity. Secondly, the use of a large-scale quantitative
approach to collecting empirical data (Cavalli et. al., 2013) (Mitchell and Weisel, 2014)
prevents a qualitative understanding of why the categories of users interact differently or to
generate new reasons why not identified in the literature. Finally, this research proposal
focuses exclusively on the UK. The results may not be externally valid internationally.
The research proposed will join other research in giving a better understanding of political
representativeness on SNS pages (Wang et al., 2012) (Choy et al., 2011) (Bakliwal et al.,
2013) which enable more accurate deductions of the electorate’s collective political
preference. This is done by providing a quantitative, empirical understanding of how well
represented different social classes are on Facebook that allows compensation for particular
social classes exerting disproportional and more accurately deduce voter preferences in
other cases. Moreover, this understanding validates the theoretical literature that the
research outlines on the nature of political engagement on SNS and gives the basis for
designing ways to encourage more minority representation on SNS. Overall, this, in turn,
improves the democratic process normatively, by being more responsive, and for the better
design, proposal and evaluation of policies (KPMG LLP, 2014).

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