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).

http://www.electoral-reform.org.uk/

http://www.parliament.uk/


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.

http://democracyranking.org/


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).


Bibliography

Abramson, P.R. and Aldrich, J.H. (1982) ‘The decline of electoral participation in America’,

The American Political Science Review, 76(3), p. 502. doi: 10.2307/1963728.

Acquisti, A. and Gross, R. (2006) ‘Imagined Communities: Awareness, Information Sharing,

and Privacy on the Facebook’, Lecture Notes in Computer Science, 4258, pp. 36–58.

Alexa.com (no date) Alexa top 500 global sites. Available at: http://www.alexa.com/topsites

(Accessed: 20 March 2016).

Andersen, K. (1975) ‘Working women and political participation, 1952-1972’, American

Journal of Political Science, 19(3), p. 439. doi: 10.2307/2110538.

Anderson, M. (2015) ‘Men catch up with women on overall social media use’, Pew Research

Center, 28 August. Available at: http://www.pewresearch.org/fact-tank/2015/08/28/men-

catch-up-with-women-on-overall-social-media-use/ (Accessed: 20 March 2016).

Andreessen, M. (2011) Why software is eating the world. Available at:

http://www.wsj.com/articles/SB10001424053111903480904576512250915629460


Anduiza, E., Cristancho, C. and Sabucedo, J.M. (2013) ‘Mobilization through online social

networks: The political protest of the indignados in Spain’, Information, Communication &

Society, 17(6), pp. 750–764. doi: 10.1080/1369118x.2013.808360.

Asur, S. and Huberman, B.A. (2010) ‘Predicting the Future With Social Media’, arXiv.org,

arXiv:1003.5699.

BBC (2016) Results of the 2015 general election - election 2015. Available at:

http://www.bbc.co.uk/news/election/2015/results (Accessed: 20 March 2016).

Bakliwal, A., Foster, J., Van Der Puil, J., O’brien, R., Tounsi, L. and Hughes, M. (2013)

‘Sentiment analysis of political Tweets: Towards an accurate Classifier’, Atlanta, Georgia:

Association for Computational Linguistics. Available at:

http://www.aclweb.org/anthology/W13-1106 (Accessed: 20 March 2016).


Barbera, P. and Rivero, G. (2014) ‘Understanding the political Representativeness of Twitter

users’, Social Science Computer Review, 33(6), pp. 712–729. doi:

10.1177/0894439314558836.

Bimber, B. (2000) ‘Measuring the Gender Gap on the Internet’, Social Science Quarterly,

81(3), pp. 868–876.

Bird, K. (2003) The Political Representation of Women and Ethnic Minorities in Established

Democracies: A Framework for Comparative Research. Available at:

https://www.hks.harvard.edu/fs/pnorris/Acrobat/stm103%20articles/Karen%20Bird%20ami

dpaper.pdf (Accessed: 20 March 2016).

Bond, R.M., Fariss, C.J., Jones, J.J., Kramer, A.D.I., Marlow, C., Settle, J.E. and Fowler, J.H.

(2012) ‘A 61-million-person experiment in social influence and political mobilization’,

Nature, 489(7415), pp. 295–298. doi: 10.1038/nature11421.

Boulianne, S. (2015) ‘Online news, civic awareness, and engagement in civic and political

life’, New Media & Society, . doi: 10.1177/1461444815616222.

Boyd, D. and Ellison, N. (2010) ‘Social network sites: Definition, history, and scholarship’,

IEEE Engineering Management Review, 38(3), pp. 16–31. doi: 10.1109/emr.2010.5559139.

Brandtzaeg, P.B. (2015) ‘Facebook is no “great equalizer”: A big data approach to gender

differences in civic engagement across countries’, Social Science Computer Review, . doi:

10.1177/0894439315605806.

CAWP (2015) Gender Differences in Voter Turnout. Available at:

http://www.cawp.rutgers.edu/ (Accessed: 20 March 2016).

Cavalli, N., Costa, E.I., Ferri, P., Mangiatordi, A., Micheli, M., Pozzali, A., Scenini, F. and

Serenelli, F. (2011) ‘Facebook influence on university students’ media habits: qualitative

results from a field research’, Massachusetts Institute of Technology: Presented at: Media in

Transition - unstable platforms: the promise and peril of transition. .


Chang, J., Rosenn, I., Backstrom, L. and Marlow, C. (2010) EPluribus: Ethnicity on social

networks. Available at: http://cameronmarlow.com/media/chang-ethnicity-on-social-

networks_0.pdf (Accessed: 20 March 2016).

Choy, M.J., Cheong, M.L.F., Ma, N.L. and Koo, P.S. (2012) ‘A sentiment analysis of Singapore

presidential election 2011 using Twi’ by Murphy Junyu CHOY, Michelle Lee Fong CHEONG et

al. Available at: http://ink.library.smu.edu.sg/sis_research/1436/ (Accessed: 20 March

2016).

Clark, C. and Clark, J. (1986) ‘Models of gender and political participation in the United

States’, Journal of Women, Politics & Policy, 6(1), pp. 5–25. doi:

10.1080/1554477x.1986.9970440.

Conover, M.D., Gonc¸alves, B., Ratkiewicz, J., Flammini, A. and Menczer, F. (2011) ‘Predicting

the Political Alignment of Twitter Users’, Proceedings of the 3rd IEEE International

Conference on Social Computing: IEEE. pp. 192–199.

Cummings, D., Oh, H. and Wang, N. (2011) Who needs polls? Gauging public opinion from

Twitter data. Available at: http://nlp.stanford.edu/courses/cs224n/2011/reports/nwang6-

davidjc-harukioh.pdf (Accessed: 20 March 2016).

Dalton, R.J. (1985) ‘Political parties and political representation: Party supporters and party

elites in Nine nations’, Comparative Political Studies, 18(3), pp. 267–299. doi:

10.1177/0010414085018003001.

Dalton, R.J. (1985) ‘Political parties and political representation: Party supporters and party

elites in Nine nations’, Comparative Political Studies, 18(3), pp. 267–299. doi:

10.1177/0010414085018003001.

Duggan, M., Ellison, N.B., Lampe, C., Lenhart, A. and Madden, M. (2015) Demographics of

key social networking platforms. Available at:

http://www.pewinternet.org/2015/01/09/demographics-of-key-social-networking-

platforms-2/ (Accessed: 20 March 2016).


Duggan, M. and Posts (2013) ‘It’s a woman's (social media) world’, 12 September. Available

at: http://www.pewresearch.org/fact-tank/2013/09/12/its-a-womans-social-media-world/


Effing, R., van Hillegersberg, J. and Huibers, T. (2011) ‘Social Media and Political

Participation: Are Facebook, Twitter and YouTube Democratizing Our Political Systems?’,

Delft, The Netherlands: IFIP International Federation for Information Processing 2011. pp.

25–31.

Engeli, I., Ballmer-Cao, T.-H. and Giugni, M. (2006) ‘Gender gap and turnout in the 2003

federal elections’, Swiss Political Science Review, 12(4), pp. 217–242. doi: 10.1002/j.1662-

6370.2006.tb00066.x.

Everett, S.P., Greene, K.K., Byrne, M.D., Wallach°, D.S., Derr°, K., Sandler°, D. and Torous°, T.

(2008) ‘Electronic voting machines versus traditional methods: Improved preference, similar

performance’, CHI 2008 Proceedings: . Available at:

http://chil.rice.edu/research/pdf/EverettGreeneBWDST_08.pdf (Accessed: 20 March 2016).

Firebaugh, G. and Chen, K. (1995) ‘Vote turnout of nineteenth amendment women: The

enduring effect of disenfranchisement’, American Journal of Sociology, 100(4), pp. 972–996.

doi: 10.2307/2782157.

Fraga, B.L. (2015) ‘Candidates or districts? Reevaluating the role of race in voter turnout’,

American Journal of Political Science, 60(1), pp. 97–122. doi: 10.1111/ajps.12172.

Gaventa, J. (2004) Representation, community leadership and participation: Citizen

involvement in neighbourhood renewal and local governance prepared for the

neighbourhood renewal unit office of deputy prime minister. Available at:

http://www.participatorymethods.org/sites/participatorymethods.org/files/Representation,

%20community%20leadership%20and%20participation_Gaventa.pdf (Accessed: 20 March

2016).

Gayo-Avello, D. (2012) ‘A Balanced Survey on Election Prediction using Twitter Data’,

http://arxiv.org/, arXiv:1204.6441.


Gladwell, M. (2010) ‘Small change: Why the revolution will not be tweeted.’, Annals of

Innovation, 4 October. Available at:

http://www.newyorker.com/magazine/2010/10/04/small-change-malcolm-gladwell


Hammond, J. (2014) ‘Ten reasons why women should vote’, Groupthink, 19 September.

Available at: http://groupthink.kinja.com/ten-reasons-why-women-should-vote-

1636713294 (Accessed: 20 March 2016).

Hargittai, E. (2007) ‘Whose space? Differences among users and non-users of social network

sites’, Journal of Computer-Mediated Communication, 13(1), pp. 276–297. doi:

10.1111/j.1083-6101.2007.00396.x.

Helsper, E.J. (2010) ‘Gendered Internet use across generations and life stages’,

Communication Research, 37(3), pp. 352–374. doi: 10.1177/0093650209356439.

Hogg, M.A. and Vaughan, G.M. (2010) Social psychology with MyPsychLab. 6th edn. Harlow,

England: Prentice Hall.

Ipsos Mori (2016) Voter Turnout Amongst Black And Minority Ethnic Voters. Available at:

https://www.ipsos-mori.com/researchpublications/researcharchive/454/Voter-Turnout-

Amongst-Black-And-Minority-Ethnic-Voters.aspx (Accessed: 20 March 2016).

KPMG LLP (2014) Shifting gears: Reprogramming government for the digital era. Available

at: https://www.kpmg.com/Ca/en/IssuesAndInsights/ArticlesPublications/Documents/100-

reprogramming-government-for-the-digital-era.pdf (Accessed: 20 March 2016).

Lenhart, A. (2009) Adults and social network Websites. Available at:

http://www.pewinternet.org/Reports/2009/Adults-and-Social-Network-Websites.aspx/


Lien, P. (1994) ‘Ethnicity and political participation: A comparison between Asian and

Mexican Americans’, Political Behavior, 16(2), pp. 237–264. doi: 10.1007/bf01498879.


Lovett, J. (2009) Weighted voting models for the HathiTrust constitutional convention.

Available at: https://www.hathitrust.org/documents/VotingModels.pdf (Accessed: 20

March 2016).

Manza, J. and Brooks, C. (1998) ‘The gender gap in U.S. Presidential elections: When? Why?

Implications?1’, American Journal of Sociology, 103(5), pp. 1235–1266. doi:

10.1086/231352.

McDonagh, E.L. (1982) ‘To work or not to work: The differential impact of achieved and

derived status upon the political participation of women, 1956-1976’, American Journal of

Political Science, 26(2), p. 280. doi: 10.2307/2111040.

Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P. and Rosenquist, J.N. (2012) Association for

the Advancement of Artificial Intelligence. Available at:

http://www.ccs.neu.edu/home/amislove/publications/Twitter-ICWSM.pdf .

Mitchell, A. and Weisel, R. (2014) Political polarization &Media habits. Available at:

http://www.journalism.org/files/2014/10/Political-Polarization-and-Media-Habits-FINAL-

REPORT-10-21-2014.pdf (Accessed: 20 March 2016).

Morozov, E. (2009) Iran: Downside to the ‘Twitter Revolution’.

Mueller, C. (1987) ‘American women and political participation: The impacts of work,

generation, and feminism. Karen Beckwith’, American Journal of Sociology, 93(3), pp. 746–

748. doi: 10.1086/228813.

Neuman, W.R., Guggenheim, L., Mo Jang, S. and Bae, S.Y. (2014) ‘The dynamics of public

attention: Agenda-setting theory meets big data’, Journal of Communication, 64(2), pp. 193–

214. doi: 10.1111/jcom.12088.

Norris, P. (1997) ‘Choosing electoral systems: Proportional, majoritarian and mixed

systems’, International Political Science Review, 18(3), pp. 297–312. doi:

10.1177/019251297018003005.

Norris, P., Inglehart, R.F. and Ronald, I. (2003) Rising tide: Gender equality and cultural

change around the world. New York: Cambridge University Press.


Norris, P. and Lovenduski, J. (1994) Political recruitment: Gender, race, and class in the

British parliament. Cambridge: Cambridge University Press.

Olsen, M.E. (1970) ‘Social and political participation of blacks’, American Sociological

Review, 35(4), p. 682. doi: 10.2307/2093944.

Phelps, E. (2006) Young adults and electoral turnout in Britain: Towards a generational

model of political participation. Available at:

https://www.sussex.ac.uk/webteam/gateway/file.php?name=sei-working-paper-no-

92.pdf&site=266 (Accessed: 20 March 2016).

Raacke, J. and Bonds-Raacke, J. (2008) ‘MySpace and Facebook: Applying the uses and

Gratifications theory to exploring friend-networking sites’, CyberPsychology & Behavior,

11(2), pp. 169–174. doi: 10.1089/cpb.2007.0056.

Rainie, L. and Smith, A. (2012) Politics on social networking sites. Available at:

http://www.pewinternet.org/files/old-

media/Files/Reports/2012/PIP_PoliticalLifeonSocialNetworkingSites.pdf (Accessed: 20

March 2016).

Rocha, R.R., Tolbert, C.J., Bowen, D.C. and Clark, C.J. (2010) ‘Race and turnout: Does

descriptive representation in state legislatures increase minority voting?’, Political Research

Quarterly, 63(4), pp. 890–907. doi: 10.1177/1065912910376388.

Saggar, S. (2000) Race and representation: Electoral politics and ethnic pluralism in Britain.

Manchester: Manchester University Press.

Shapiro, I., Stokes, S.C., Wood, E.J. and Kirshner, A.S. (eds.) (2010) Political representation.

United Kingdom: CAMBRIDGE UNIVERSITY PRESS, United Kingdom.

Shingles, R.D. (1981) ‘Black consciousness and political participation: The missing link’, The

American Political Science Review, 75(1), p. 76. doi: 10.2307/1962160.

Tang, G. and Lee, F.L.F. (2013) ‘Facebook use and political participation: The impact of

exposure to shared political information, connections with public political actors, and


network structural heterogeneity’, Social Science Computer Review, 31(6), pp. 763–773. doi:

10.1177/0894439313490625.

Tannen, D. (1990) You just don’t understand: Women and men in conversation. New York,

NY: Morrow, c1990.

The Electoral Commission (2004) Gender and political participation. Available at:

http://www.electoralcommission.org.uk/__data/assets/electoral_commission_pdf_file/001

9/16129/Final_report_270404_12488-9470__E__N__S__W__.pdf (Accessed: 20 March

2016).

Tumasjan, A., Sprenger, T.O., Sandner, P.G. and Welpe, I.M. (2010) ‘Predicting Elections with

Twitter: What 140 Characters Reveal about Political Sentiment’, Munich, Germany:

Technische Universität München Lehrstuhl für Betriebswirtschaftslehre Strategie und

Organisation. Available at:

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1441/1852 (Accessed:

20 March 2016).

UN Women (2013) ‘Women’s Leadership and Political Participation’, .

Uhlaner, C.J., Cain, B.E. and Kiewiet, D.R. (1989) ‘Political participation of ethnic minorities in

the 1980s’, Political Behavior, 11(3), pp. 195–231. doi: 10.1007/bf00992297.

Valenzuela, S., Park, N. and Kee, K.F. (2009) ‘Is there social capital in a social network site?

Facebook use and college students’ life satisfaction, trust, and participation’, Journal of

Computer-Mediated Communication, 14(4), pp. 875–901. doi: 10.1111/j.1083-

6101.2009.01474.x.

Verba, S. and Nie, N.H. (1972) Participation in America: Political democracy and social

equality: Political democracy and social equality. New York: Harper & Row.

Verba, S. and Nie, N.H. (1972) Participation in America: Political democracy and social

equality: Political democracy and social equality. New York: Harper & Row.

Wang*, H., Can**, D., Kazemzadeh**, A., Bar*, F. and Narayanan**, S. (2012) ‘A System for

Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle’, Jeju, Republic


of Korea: 50th Annual Meeting of the Association for Computational Linguistics. pp. 115–

120.

Welch, S. (1977) ‘Women as political animals? A test of some explanations for male-female

political participation differences’, American Journal of Political Science, 21(4), p. 711. doi:

10.2307/2110733.

cdn2.business2community.com (2013) Http://cdn2.business2community.com. Available at:

http://cdn2.business2community.com/wp-content/uploads/2013/03/Social_Platform.jpg


http://democracyranking.org/ (2015) Democracy ranking 2015. Available at:

http://democracyranking.org/wordpress/rank/democracy-ranking-2015/ (Accessed: 20

March 2016).

http://www.electoral-reform.org.uk (2010) In this section. Available at:

http://www.electoral-reform.org.uk/first-past-the-post (Accessed: 20 March 2016).

http://www.parliament.uk (2010) Who can stand as an MP?. Available at:

http://www.parliament.uk/about/mps-and-lords/members/electing-mps/candidates/
