Performing the self: Constructing Identity Through Stemfie Tweets

Annemarie Sint Jago 0205753

Final Essay Mobile Media Studies 30 Jan 2015

One of the key pillars of democracy is secret ballot (UN 2015). Voting booths ensure that people

can choose in freedom. No one, other than the person who votes, can see which box is filled.

Subsequently, when the voting paper is folded and put in the voting box, no one will ever be able

to find out what choice an individual has made. This is key to ensure that people can give their

vote free from external pressure. The voting booth, while standing in a public area, offers the

individual a private space that ensures that only the individual himself/herself knows what hap-

pened.

During the Dutch municipal elections of 2014, however, this private space of the voting

booth became public when individuals started to take pictures of themselves (selfies) (sometimes

showing their completed ballots) in the voting booth. A new phenomenon was born: the stemfie, a

combination of stem (Dutch for vote or ballot) and selfie. It is thus a selfie that is taken in

the voting booth, often with the intention to share it on Social Networking Sites (Van Dale

2014). Often, not only the vote itself is displayed, but people show themselves in these pictures,

thereby connecting their votes to themselves as a person, or vice versa. Hence, it seems that peo-

ple not only seek to express their specific vote, but they seem to feel a need to relate that to

themselves: they, as a specific individual, want to show parts of their selves, whether that being

their political preference or just a photographical representation of the self. In other words, an

aspect of the self (namely, political preferences) that formerly was safeguarded by physical means

(namely, the voting booth), suddenly transcended physical constraints and entered the public

domain. Of course, the stemfie offers many interesting angles for research in various disciplines,

such as implications concerning the secrecy of ballot (stemgeheim). Although it is certainly

worthwhile to research juridical implications of the advent of the stemfie, for instance, this pa-

per seeks to focus on the blurring between the private and the public, and how the self is con-

structed in this process of negotiating the private and the public. To be more specific, the main

question of this paper is:

What performative strategies are used by the on-line self to balance between the pub-

lic and the private in stemfie tweets?

What I expect is that the blurring of the boundaries of private and public are not that blurred. I

expect that although people share information of a private act, literally showing themselves, they

make deliberate choices of what information they do and do not share.

Before directly going into the analysis of stemfie tweets, three notions first need further

exploration, namely: the representation of the (on-line) self, the performance of identity, and the

blurring boundaries of the public and the private in the digital age, to answer questions such as:

what is self-presentation, is the on-line self different to the off-line self, to what extent is the stem-

fie tweet a performance of the self, and how do SNS blur the private and the public? Thereafter,

I will focus on how people construct their online self in stemfie tweets by analysing the tweets of

the day of the Dutch municipal elections of 2014. What parts of the self are shared in these

tweets and what information regarding the self is not shared? For this analysis, I will use mix

methods, combining quantitative and qualitative content analysis. Finally, I hope to come to an

understanding of how an analysis of stemfie tweets could help us understand how the individual

balances between the private and the public in the process of representing the self in stemfie

tweets.

Self-presentation, the private and the public, and performativity

The amalgamation of the public and private sphere, facilitated by the use of technologies that

surmount physical restrictionsin this case, the restrictions that the voting booth imposes is ne-

gotiated by the affordance of the mobile phoneis not a new phenomenon. To understand how

people use performative strategies to balance between the private and public when they con-

struct the on-line self, the construction of the offline self needs to be regarded firstly.

According to Goffman (1959), who developed the idea of identity-as-performance

(Pearson 2009), self-representation is a performance in which the individual presents himself to

others (Goffman 1959). Baumeister and Hutton, building forth on Goffman, distinguish between

two types of self-representational motivations, namely pleasing the audience (to match one's

self-presentation to the audiences expectations and preferences) and self-construction (to

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match one's self-presentation to ones ideal self ) (71). These self-representational motivations 1

are triggered by the need for status and for popularity (Hogan 1982 paraphrased by Baumeister

and Hutton 1987, 72). For self-representation, thus, an (implicit) audience is needed (which

could also be the self). The self consequently presents oneself, based on the way in which one

wishes to be perceived by one's audience.

In performing the self, Goffman distinguishes between the front and the back. The front

is

[t]hat part of individual's performance which regularly functions in a general and fixed fashion to define the situation for those who observe the performance.Front, then, is the expressive equipment of a standard kind intentionally or unwittingly employed by the individual during his performance. (Goffman 1959, 13)

Sharon Chan helps us interpret this challenging phrased definition: the front space is the region

where the actual presentation takes place (Chan 2000, 273). Thus, the front is that part of the

self that is visible to others. Part of this front stage is the setting, which includes props, location,

and decor, which also give information about the self (Goffman 1959, 13). The back is the place

where the self prepares the performance (Goffman 1959; Chan 2000).

Transposed into the online self, SNS could be understood as the front stage (Chan 2000,

271-2). The back is then, for example, the private space of the home (Chan 2000, 272-3). Chan

contends that the homepage is an example of a place where the public and the private space are

combined: it is a public space, a theatre, in which private lives are performed (Chan 2000, 272).

As Pearson astutely observes, online the front stage and back stage blur (Pearson 2009). More-

over, on the internet, not only the self is created, but the setting . . . in which these selves exist

is also staged (Pearson 2003), which implies that the self can be manipulated even more thor-

oughly. As Goffman explains, the setting can reveal information about the self (Goffman 1959).

Yet, if one can stage the setting, in the case of an homepage or by a profile on a SNS, this im-

plies that the presentation of the self can be more easily be constructed, independently of the

real self. If in an offline setting the self would be a person wearing ragged clothes, on the inter-

net, the self could use an avatar to represent oneself, or choose to not disclose specific informa-

tion concerning ones clothes. This online stage is, thus, somewhat different from the stage as

described by Goffman. Pearson uses the term the glass bedroom to illustrate how this stage is

not entirely private space, nor a true backstage space as Goffman articulated. . . . It is a bridge

that is partially private and public, constructed online through signs and language (Pearson

The first type is not limited to merely pleasing the audience, or showing the idealised self (as Goffman 1argues); the individual can also choose to present oneself in a negative way (as is pointed out by Jones and Pittman (1982) (Baumeister and Hutton 1987, 71-2).

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2009). In this glass bedroom, the border between the private and the public, is thus not very

clear, and the private can thus easily become public. According to Hangwoo Lee, the homepage

is such a space, in which people constantly negotiate the boundary between the private and the

public in on-line self presentations (Lee 2006, 18). Additionally, Van House notes that online

self-presentation is not limited to a profile (which is basically a intentionally constructed stage),

but the most direct and visible way of representing oneself online is via periodic postings (Van

House 2011, 425). Thus, the staged, online self, could be a representation of the self that one is

unaware of. For example, one could construct a Twitter profile which describes how adventurous

one is; however, if one posts links about recipes and images of kittens, the perceived identity will

probably be not that adventurous. Moreover, also posted images could present the self in a spe-

cific way: they can tell the viewer not only what the member looks like but where the member

has been, what theyve been doing, what they consider photo-worthy (Barthes quoted in Van

House 2011, 425). These posts and images could seem to display something about the online

self, in a similar fashion as offline props and setting communicated something about the offline

staged self. The construction of the self is thus a combination of intentional and unintentional

performances.

The selfie is such an image that conveys both intended and unintended information; it

acts as a window through which the the front stage and back stage could be seen at once. It is a

conscious representation of the self (that is, the photograph is staged, exactly because the self

chooses to make a picture, and has a certain idea in mind of what the picture should communi-

cate), while it also shows the setting in which the self is photographed, which could be a private,

stage in the back. The selfie is about individuality, and about sharing a representation of the self

with others, or, in Goffmans terms, about staging the self (or performing the self) for an audi-

ence. Thereby, the selfie is not only about individuality, but also about interaction.

Whereas the selfie can be taken anywhere, anytime, the stemfie is a specific type of self-

ie: it is a selfie that is taken in a polling booth. This means that the setting of the stemfie is, thus,

always private, whereas the selfie can be made in public places and therefore does not have to be

private. The stemfie itself is thus in itself a combination of the private and the public. So, how

does the self balances between the private and the public when taking and publishing a stemfie?

What information is kept back stage and which props are taken front stage? In order to under-

stand how people balance their performed self in this glass bedroom (to use Pearson's pic-

turesque term), an analysis was performed of tweets containing the hashtag stemfie.

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Methodology

A method that seemed logical was performing interviews, in which people were to be asked what

considerations were made when composing and posting tweets, whether they share private in-

formation, etc. However, if people post tweets, they think they are part of a collective and do

not consider the option that they could be tracked as an individually (Zimmerman 2010). Ap-

proaching individuals because they tweeted something could be unethical (Zimmerman 2010) or

even intimidating. Secondly, people could give socially desirable answers. Thirdly, they could not

be aware of their tweets being part of constructing the self, which would not produce any an-

swers at all. Therefore, it was chosen to use aggregated data to recognise patterns and categories

in the tweets. Firstly, conventional content analysis was done to understand the data that was

going to be dealt with, which is a form of analysis that is often performed when existing theory

or research on a phenomenon is limited (Hsieh and Shannon 2005, 1279). This type of re-

search allows categories to flow from the data, and is used as initial approach in many qualita-

tive methods (idem). Secondly, after defining the categories, the data was analysed using visual

and textual qualitative content analysis, respectively.

Cleaning and preparing the data

Firstly, before the analyses could be carried out, the data set needed to be cleaned. The primary

dataset initially consisted of 4132 tweets that were posted on Twitter on 19 March 2014, the day

of the municipal elections, containing the hashtag #stemfie. The data was collected by the com-

pany BuzzCapture, and consisted of both persons as companies tweets (such as NOS). Firstly, 2

the dataset was cleaned from companies and sources other than Twitter (such as Facebook

tweets that are automatically shared on Twitter). This reduced the data set to 3543 tweets. Of

these tweets, 1467 were retweets (tweets starting with RT). Since these were not the primary fo-

cus of this paper, they were also removed from the data set. After manually reading the remain3 -

ing 2075 tweets, 206 tweets were found that on closer inspection appeared to be retweets as well,

although without the RT indicator, including retweets with comments (such as :) [retweeted

text]). Additionally, also tweets from non-persons, such as news media, were manually removed

BuzzCapture uses an automated search query to get the tweets. This search query should retrieve all the 2tweets containing #stemfie (Kriens 2011). (In contrast, APIs just get a sample of the tweets (Twitter 2015)).

Retweets merely show that people are involved in the stemfie discussion, yet, no personal information is 3disclosed. Since this paper seeks to understand how people deal with the private and the public, it focuses on which parts of the self are disclosed.

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from the dataset, along with double tweets, resulting in a final dataset of 1510 tweets, containing

only tweets with the hashtag stemfie.

Next, the data needed to be prepared for the visual and textual qualitative analyses, re-

spectively, so the data was to be divided by whether it contained a picture or not. This was done

in Microsoft Excel by writing a formula that extracted the URL of the tweet. Subsequently,

these URLs were put into JDownloader, a program that can automatically retrieve pictures from

the web or the computer. After this step, of the 1510 tweets, 639 tweets contained pictures,

which were subsequently used for the visual analysis.

Conventional content analysis

For the visual analysis of the photos, a conventional content analysis was chosen as a first ex-

ploratory analysis to find out what kind of information is communicated in these photos. All the

photos were closely analysed manually, and notes were made about the contents of the photo

(e.g., did people show themselves, or did they only show the checkboxes that they filled on the

ballot paper?). It became apparent that the photos could be distinguished based on the amount

of personal information that was given in these photos, which resulted in the following cate-

gories: selfies with the ballot paper, photo of the ballot paper, selfies without the ballot paper,

picture taken by someone else, and other.

" Figure 1: initial categorisation of the tweets, showing the different categories as percentage of the whole data set.

By using these categories, it was assumed that it could be quantitatively analysed to what extent

people really gave private information about themselves. In the first category, people showed

both themselves and their ballot paper, thereby sharing the fact that they voted. Sometimes their

vote was visible, which could also be a bit of personal information. Conversely, the secondly cat-

egory consisted of only pictures that showed either a folded ballot paper (which basically only

expresses that someone voted, while that can even have be contested), or the picture concerned a

Category photos

Selfies with the ballot paper 176

Photo of the ballot paper 109

Selfie without the ballot paper 233

Picture taken by someone else 28

Other 91

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ballot paper with a clear, visible choice, which then would tell something about the voter. How-

ever, while reanalysing these pictures, it was realised that these categories were not adequate:

different photos in different categories could actually tell a similar story of a person. What this

analyses did add to this research was merely a notion of how many selfies were actually tweeted,

which were the photos in the first and third categories added together (which amounts to 409

photos).

Therefore, it was decided to perform a new analysis of the pictures, this time focusing

on the way in which people situate themselves on this day of elections. How did they represent

themselves, how did they construct a story? What did they share and how? Consequently, new

categories could be made to group the photos. The pictures could be grouped into the following

categories:

The selfies (no performance of the self) (339 photos) Performing the act of voting (either showing themselves or not) (98 photos) Pictures of ballot papers (102) Cues for the narrative of voting (22 photos) Other (nonsense photos, etc) (74 photos)

In the first category, the selfies, the self was shown statically: often these were pictures of the self

while showing in the background or foreground a ballot paper, but also selfies without anything

in the background were part of this category. The second category consisted of pictures in

which the self performed the act of voting (either taken as a selfie, or taken by another person).

The third category was constituted by photos of ballot papers, in which only folded or checked

ballots were shown. The fourth category existed of pictures in which the self and the ballot pa-

per were absent; however, in these photos narrative cues were shown, such as signs containing

stemlokaal. The last category contained other type of pictures, that seemed to be part of none

of the aforementioned categories, such as cartoons and icons.

"7

"Figure 2: Left: no selfies (no action). Right: selfies and photos taken by an other in which there is action taken.

" Figure 3: Left: pictures with ballots. Center: photos containing narrative cues. Right: Other pic-tures. such as cartoons, or screenshots.

"8

Table X: The distribution of the different type of photos

Textual analysis

After the visual content analysis of the stemfies, a qualitative textual content analysis of the

tweets that contained the hashtag stemfie (which was the cleaned dataset that consisted of 1510

tweets) was performed. The tweets were divided into two groups: the first group consisted of the

tweets that contained a URL, the second group were tweets that did not contain URLs. Initially,

the intention of this approach was to categorise the tweets, in the same way as the visual analy-

sis. However, after attempting to manually analyse the tweets and categorise them, it became

apparent that this was hardly possible within the appointed time frame: if someone was men-

tioned in a tweet, which was often the case, this tag could mean I have voted for you, or it

could be a sign of a need for communication. Hence, this analytical approach proved to be too

time-consuming. Therefore, it was chosen to perform an exploratory data analysis (or quantita-

tive analysis as it could also be called) to find out whether there were words that were used more

often than others, so that it could be determined which tweets needed closer analysis. To under-

stand interesting patterns that could be further explored, the texts of either group were put into

a word cloud, using Tagcrowd, resulting in the following visualisations: 4

1. The selfies (no performance) 339 photos

A) selfies without ballot 168 photos

B) selfies with ballot 173 photos

showing clearly the vote 49 photos

not showing clearly the vote 124 photos

2. Performing the act of vo?ng (either showing themselves or not) 98 photos

A) showing clearly the vote 1 photo

B) not clearly showing the vote 97 photos

3. Pictures of ballot papers 102 photos

A) showing the vote 71 photos

B) not showing the vote 31 photos

4. Cues for the narra?ve of vo?ng 22 photos

5. Other (nonsense photos, etc) 74 photos

Words that were excluded from the word count were: aan, al, als, bij, co, daar, dan, dat, de, deze, die, dit, 4dus, een, en, er, goed, het, hoor, http, in, is, maar, naar, nog, om, ook, op, rt, te, toch, van, voor, waar, was, wat, weer, words, zijn, zo.)

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" Figure 4: Word count visualisation for the group that included a picture in their tweets.

" Figure 5: Word count visualisation for the group that did not include pictures.

In these visualisations, a few words stood out, such as geen, niet, vergeten. Based on these

visualisations, the datasets were further analysed using qualitative content analysis to understand

how these words were used in the tweets. Did the latter group often express words such as

geen in relation to gemaakt, and niet in relation to gestemd, or was niet rather relat-

ed to gemaakt? The context and use of these words were analysed by selecting only the tweets

that included (one of) these words.

In group 1 niet was related to voting in 1 instance. In 6 cases (11% of the people who

used niet) the utterance was related to the stemfie (not being able to make a stemfie, for in-

stance). In group 2, in 2 instances of niet concerned the act of voting (both instances were cas-

es in which there were no elections in these city governments), while in 17 instances (14,5% of

niet), the sharing, making, or publishing of the stemfie was concerned (for instance, people say-

ing that they did vote, but refused to make a stemfie, or people saying that their phones battery

went dead so they could not make a stemfie).

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As expected, only a relatively small number of people of the first group (the group that

consisted of tweets with a URL) used the word geen in relation to stemfie. Only 29 people (3%)

said that they did not make a stemfie, however, 69% of them did take another picture (such as a

picture from the polling station or their ballot). In group 2 (the tweets without the URLS,) the

word geen was related to stemfie in 116 tweets (18%). In all of these 116 tweets, except for 5

one, it was explicitly stated that people did vote. Furthermore, in many of these tweets, is was

also mentioned for which political party or individual was voted.

There was no difference between the group in the percentage of people that used the

words stemmen or gestemd. In both groups, 16% used these words respectively in their

tweets.

Findings / Discussion:

What immediately emerges from the visual analysis is the main focus of the photos (regardless

of whether they are selfies or pictures taken by other people): it seems that the majority of twit-

terers does not show their specific choice of vote, but merely the fact that they got out and voted.

Of the tweets containing pictures, only 19 percent showed the actual vote (of which most were

undecipherable due to the bad quality of the pictures). Instead, people tended to show that they

were in the polling booth, had filled out their ballots (or were filling them at that specific mo-

ment), pretended to be thinking about whom to vote for. The action and contextualising of the

self, namely the self as participating being in society, acting out his/her moral duty in democra-

cy, appeared to be more important than the final vote.

This focus on the performance and contextualising of the vote was also found in the

textual analysis. Regardless of whether people included an actual stemfie picture in the stemfie

tweet or not, most people stressed the fact that they voted, or expressed their intention to vote. A

smaller number of people also expressed their specific vote. Interestingly, there were 660 tweets 6

containing the hashtag stemfie without containing a stemfie picture. Approximately 20% explic-

itly mentioned not making a stemfie, yet they all (but one) stated that they got out and voted.

The other 80% included tweets of people stating that they had just voted, were comments on

others stemfies or were comments on the phenomenon of the stemfie itself. Apparently, the

stemfie became a categorisation that was not limited to tweets containing a proper stemfie, but it

became a label that was used to show engagement with the municipal elections. The stemfie 7

Other instances of geen were not used in relation to voting or stemfies.5

However, as mentioned earlier, it was difficult to analyse the percentage of tweets that explicitly stated 6the specific choice of vote.

3% mentioned duty (burgerplicht, plicht), 1 % mentioned democracy. 7

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became a label through which people could connect to others and show their engagement with

society. As a matter of fact, it is exactly through descriptions and tags that one (implicitly) situ-

ates oneself in a group (Van House 2011, 4250). Only a relatively small number focuses on per-

formance (15%) while the rest of the photos contains markers (either the self, a ballot, or signs)

that situates the tweet and thereby, implicitly, the self. By situating oneself, even by tagging a lo-

cation (such as #Utrecht), the self is in a way represented and constructed, using words as a

means to contextualise the self (i.e. I live in Utrecht).

On the one hand, the self is put on the front stage: one tries to become as visible as pos-

sible, literally and figuratively. Photos of the self are added, the actual vote is photographed, and

hashtags such as #stemfie, #selfie, #GR, etc are added, functioning as labels, which, according

to Ruth Page, promote the visibility of the tweets (Page 2012, 181). However, on the other hand,

private information (back stage information) seems to be sparse. Be refraining from disclosing

personal preferences and personal motivations, people do not really share private information:

few expressed whom they voted for, virtually none told why one chose for a particular person

(this was only mentioned by one or two), and the decision process that had preceded the vote

was absent. The most explicit private information was the fact that by tweeting about the elec-

tions, ones engagement with the elections is showed, which is something that is rather part of

ones situatedness in a community (by expressing yourself, youre trying to belong to a larger

group), than something that is exclusively reserved to the self.

Yet,identity and the performance of identity is not only about what people show or tell,

but it is also constituted exactly by what is not said. Also, as already noted in the introduction,

the most direct and visible way to represent oneself online is via periodic postings (Van House

2011. 425). Moreover, [i]mages can tell the viewer not only what the member looks like but

where the member has been, what theyve been doing, what they consider photo-

worth (Barthes quoted in Van House 2011, 525). While this could certainly be true, focusing on

whether posts and images would tell something about the individual is not the focus of this pa-

per. Rather, in the analysis of the tweets, it becomes clear that the on-line self that is constructed

by the majority, is not one of uniqueness or distinction (look who I am!), but rather one of inclu-

sion (Look, I am part of). People (implicitly) categorised themselves by using hashtags, but also

by mentioning locations and political preferences, thereby showing that they are part of some-

thing bigger.

Yet, the tweets could not simply be considered as ingredients of one big soup. In the

attempt to be part of a group, whether this is the twitter community of #stemfie hashtags or

something else, people simultaneously try to stand out. The moving back-and-forth between

public and private, individual and collective, has already been observed by Papacharissi

(parafrasing Schechner 2003): Performance enable[s] individuals to traverse from private to

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public, but also, potentially, from the personal to the political, or from the individual to the col-

lective, and back (Papacharissi 2012). Especially the latter can be observed: people show their

own uniqueness (e.g., stemfies of people locking red pencils between their noses and upper lips,

trying to stand out), while simultaneously using categorisation by labelling their tweets with

stemfie. Interestingly, 3% of the tweets that not contained pictures (compared to none of the

tweets containing pictures) consisted of criticising notions of stemvee, implying that people

behaved like cattle by all taking and posting stemfies. While these observations were meant as

critique, are astute observations of the notion that people do in fact try to be at once an individ-

ual and part of a larger group.

Limitations / Reflection

The cleaned data set consisted of 1510 tweets. Of these, therewere 639 tweets found which con-

tained a picture. However, 850 tweets contained a link. Thus, there are 211 tweets that cannot

accounted for, due to methodological limitations in this research (the pictures could not be con-

nected with the data table). It is thought that these 211 tweets are primarily tweets that contain

links to articles that discuss the stemfie. These 211 were not included in the analyses, which

could influence the overall findings. Sharing links is an example of how people try to socially

interact, and including these tweets could therefore expose interesting patterns in the data.

Furthermore, the tools that were used to analyse the tweets did not allow to link the pic-

tures to the users and the texts. Analysing the connection between text and image could reveal

different patterns.

Moreover, the notion of most twitterers balancing between the collective and the indi-

vidual could be a result from the approach of this study, which was based on a data set that is

constructed on basis of a hashtag (#stemfie), which is a data set in which the people have al-

ready categorised themselves. These tweets were thus much more likely to show an attitude to-

wards the collective rather than the individual.

Moreover, this research lacked a focus on the networked self: the self in interaction with

others, while SNS are explicitly designed on the notion of social connections as networks (Van

House 2011, 424). This means that the individual cannot be regarded, without also encapsulat-

ing the interactive aspect of Twitter. While my object of study was part of SNS, I have not taken

into account how interaction is involved in self-presentation and the construction of identity.

This could have been done by taking into account the retweets, comments and replies. Taking

interactivity into account in the research of tweets may be worthwhile, since many scholars hold

that identity is in part constituted by interaction (Miller 1995; Pearson 2009; Papacharissi 2012).

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Another limitation is the fact that the data was collected by BuzzCapture which offered

limited access to the exact method of data collection. I have carried out my own data collection

by searching Twitter, exporting the HTML file, writing a code to extract the tweets and URLS,

but this resulted in a little less tweets than BuzzCaptures data set (My own data set consisted of

3921 tweets). Therefore, it cannot be ascertained that the data set that was used is valid or repre-

sentative. The reason did I chose to use BuzzCapture's data set, because this data set was deliv-

ered in a spreadsheat, while my data set was a text file, which was harder to analyse.

Lastly, the primary drawback is the fact that the data set only contained tweets with the

hastag #stemfie. Possibly, many tweets were send with the hastag #selfie that were essentially

stemfies too, although not labeled as stemfie. This means that the results of my research, namely

that the majority of people tried to show that they were engaged with the elections, may be

wrong.

Conclusion

Although the popular media spoke about a trend, only 409 tweets actually contained stemfies. It

is questionable whether this is really a trend or not. In fact, there were more tweets in which the

phenomenon of the stemfie itself was discussed and tweets that encouraged people to post a

stemfie, than there were actual stemfies.

In these stemfies, people tended to show their engagement with the elections, rather

than disclose private information such as the party they voted for. They did publish photos of

themselves on the internet, but these do not seem different than other selfies, except for the fact

that they are situated in voting booths. If tweets of selected individual were to be analysed, the

stemfie would probably be another narrative cue for the construction of an individuals online

self. However, when only looking at one tweet of one individual, there does not seem enough

information to come to un understanding of the staged self.

It could be interesting to research multiple posts of an individual to understand how on-

line selfs constructs their online identity through narration via tweets, and whether people tend

to use the same narrative mechanism. However, this does raise ethical questions.

From the findings of this research, it seems that most people stage their online self with-

out really using private information, but by rather categorising themselves, but labelling their

tweets with hashtags, such as #stemfie or #utrecht. Even the people who did show their vote

rather tell something about the group that they feel they belong to (the SP has a different elec-

torate than the VVD), than that they disclose really personal information.

"14

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