Which Side Are You On?

A New Panopticon vs. Privacy

Miltiadis Kandias, Lilian Mitrou, Vasilis Stavrou and Dimitris Gritzalis

Information Security & Critical Infrastructure Protection Research Laboratory,

Dept. of Informatics, Athens University of Economics & Business, 76 Patission Ave., GR-10434, Athens, Greece

Keywords: Awareness, Panopticon, Privacy, Social Media, Surveillance, User Profiling, YouTube.

Abstract: Social media and Web 2.0 have enabled internet users to contribute online content, which may be crawled

and utilized for a variety of reasons, from personalized advertising to behaviour prediction/profiling. One

negative case scenario is the political affiliation profiling. Our hypothesis is that this scenario is nowadays

realistic, applicable to social media, and violates civil rights, privacy and freedom. To demonstrate this, we

developed a horror story, i.e., a Panopticon method, in order to reveal this threat and contribute in raising

the social awareness over it. The Panopticon relies on data/opinion mining techniques; hence it classifies

comments, videos and playlists, collected from the popular social medium YouTube. Afterwards, it aggre-

gates these classifications in order to decide over the users’ political affiliation. The experimental test case

of the Panopticon is an extensive Greek community of YouTube users. In order to demonstrate our case, we

performed an extensive graph theoretical and content analysis of the collected dataset and show how and

what kind of personal data (e.g. political attitude) can be derived via data mining on publicly available

YouTube data. Then, we provide the reader with an analysis of the legal means that are available today, to a

citizen or a society as a whole, so as to effectively be prevented from such a threat.

1 INTRODUCTION

The exponential growth of Information and Commu-

nication Technologies (ICT) and the rapid explosion

of social media have contributed to substantive

changes in the social dimensions of information sha-

ring and (mis)use. However, several inherent featu-

res of Internet (and especially Web 2.0) supported

technologies and platforms (e.g., digitization, avail-

ability, recordability and persistency of information,

public or semi-public nature of profiles and messa-

ges, etc.) encourage not only new forms of interacti-

on but also surveillance behaviours and tendencies

(Tokunaga, 2011). ICT have often been accused for

facilitating surveillance via CCTV or even the Inter-

net (Brignall, 2002).

The common conception of surveillance is this of

a hierarchical system of power between the observer

and the observed, represented in metaphors, such as

the “Panopticon” of J. Bentham, i.e. a theoretical

prison structure, an “ideal” prison building designed

in a way that allows observing of the prisoners from

a central location at all times. The observed subject

is never sure of whether or not she is under surveil-

lance. Foucault, who elaborated extensively on the

modern implications of the Panopticon, emphasized

that the conscious and permanent visibility assures

the automatic functioning of power (Foucault,

1975). The Panopticon creates “a consciousness of

permanent visibility as a form of power, where no

bars, chains and heavy locks are necessary for domi-

nation, anymore” (Almer, 2012).

Is the Internet surveillant in the way of a Panop-

ticon? The metaphor of Panopticon offers perhaps

the ultimate example of unilateral and vertical sur-

veillance, while social networks and media indicate

and incorporate the shift to interpersonal, horizontal,

and mutual information aggregation and surveillan-

ce. However, despite the lack of centralized control

over the Internet, its platforms and applications al-

low multilevel and latent surveillance, thus pose new

risks for the rights of the individuals by forming new

power relations and asymmetries. Surveillance and

surveillors remain invisible: The technology hides

both the possibility of surveillance and the signs of

what/who is monitored (Uteck, 2009); (Fuchs,

2011), although persons living in future ubiquitous

computing environments can - in antithesis to the

Kandias M., Mitrou L., Stavrou V. and Gritzalis D..

Which Side Are You On? - A New Panopticon vs. Privacy.

DOI: 10.5220/0004516500980110

In Proceedings of the 10th International Conference on Security and Cryptography (SECRYPT-2013), pages 98-110

ISBN: 978-989-8565-73-0

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

classical “Panopticon” - assume (or even accept, if

not wish) that they will be monitored.

May Web 2.0 become an Omniopticon, in which

“the many watch the many”? (Jurgenson, 2010). Is

the “social” and “participatory network” (Beyer et

al., 2008) the ideal “topos” for “social surveillance”

(Tokunaga, 2011); (Marwick, 2012) and “participa-

tory panopticism” (Whitaker, 1999) By being

subject of communication and engaging in social

networking activities the users are becoming objects

of a lateral surveillance (Fuchs, 2011). In social

media users monitor each other. Moreover, such si-

tes and interaction platforms are, by design, destined

for users to continually digital traces left by their

“friends” or persons they interact with - often by

simply consuming or commenting user-generated

content.

Nowadays, several often rely on user generated

content in social media, which is the most popular

type of information transmitted through internet, as

users are able to express themselves, inform others,

republish/redistribute news and opinions of others,

thus they form their personal identity in the digital

world. All these activities produce user generated in-

formation flows. These flows may be - in fact have

been - utilized for purposes ranging from profiling

for targeted advertising (on the basis of analysing

online features and behaviour of the users), to perso-

nality profiling and behaviour prediction.

Along with consumer and configuration based

offerings, exploitation/use of user generated data has

contributed to the shaping of the “Open Source In-

telligence” (Gibson, 2004). This data may be used

for both, the social good/in the interest of the society

(e.g. Forensics), or in a way that infringes funda-

mental rights and liberties or private interests (e.g.

social engineering, discriminations, cyber bullying,

etc.) (Gritzalis, 2001); (Lambrinoudakis, 2003);

(Marias, 2007); (Mitrou et al., 2003); (Spinellis et

al., 1999).

The voluntary exposure of personal information

to an indefinite audience gives rise both to the tradi-

tional and social panopticism (Nevrla, 2010). Sur-

veillance of user generated content and information

flows takes place between organisational entities and

individuals and between individual users (Marwick,

2012). Governments’ interest on information gained

through data aggregation and mining of social media

is easily understandable, as law enforcement and cri-

me prevention may require “connecting the dots”

and combining information about political beliefs

and every-day activities.

However, such information aggregation and pro-

filing of political beliefs and affiliations may result

to a “nightmare” for a democratic State, especially in

the case that such practices concern a large thus dis-

proportional) number of citizens/netizens. Users’ po-

litical profiling implicates the right to decisional and

informational privacy and may have a chilling effect

on the exercise of freedom of expression.

In order to prove our hypothesis we have develo-

ped a proof-of-concept panopticon and applied it on

real-life data. We crawled the YouTube social medi-

um and created a dataset that consists solely of Gre-

ek users, hence a Greek YouTube community. We e-

xamined the data (comments, uploads, playlists, fa-

vourites, and subscriptions) using text classification

techniques via comments classification. The panop-

ticon can predict the political affiliation of a video

and then predict the political affiliation expressed in

a list of videos. The method applies the above on u-

sers’ comments, uploaded videos, favourite videos

and playlists, so as to aggregate the results and ex-

tract a conclusion over the users’ political affiliation.

We have decided to define three categories of

broad political affiliations, namely Radical, Neutral

and Conservative. These assumptions are context-

dependent, given that our experimental test case is a

real-life Greek community. Thus, in order to reflect

the recent political/historical context in Greece, we

define the following pairing: Radical political affili-

ation refers to centre-left, left, and far-left political

beliefs, Neutral political affiliation refers to non-po-

litical content, whereas Conservative political affili-

ation refers to centre-right, right and far-right politi-

cal beliefs. The definition of the above categories

has no impact on the results of the analysis.

The paper is organized as follows: In section 2

we review the existing literature. In section 3 we

describe the panopticon methodology and the testing

environment. In section 4 we demonstrate when and

how a political affiliation of a YouTube user can be

revealed. In section 5 we provide a detailed statisti-

cal results evaluation and analysis. In section 6 we

highlight the social threats that can emerge from a

malevolent exploitation of a panopticon, along with

an analysis of the legal means that are available to-

day to a citizen or a society in order to avoid such a

case. Finally, in Section 7 we conclude and refer to

our plans for future work.

2 RELATED WORK

The advent of Web 2.0 has contributed in the trans-

formation of the average user from a passive reader

into a content contributor. Web 2.0 and social media

have, in particular, become a valuable source of per-

WhichSideAreYouOn?-ANewPanopticonvs.Privacy

sonal data, which are available for crawling and pro-

cessing without the user’s consent. The rise of social

media usage has challenged and directed researchers

towards opinion mining and sentiment analysis

(Pang and Lee, 2008).

Opinion mining and sentiment analysis constitute

computational techniques in social computing (King

et al, 2009). As presented by King et al., social com-

puting is a computing paradigm that involves multi-

disciplinary approach in analysing and modelling so-

cial behaviour on different media and platforms to

produce intelligence and interactive platform results.

One may collect and process the available data, so as

to draw conclusions about a user mood (Choudhury

and Counts, 2012). Choudhury and Counts present

and explore ways that expressions of human moods

can be measured, inferred and expressed from social

media activity. As a result, user and usage profiling

and conclusion extraction from content processing

are, today, more feasible and valuable than ever.

Several methods have been utilized in order to

process online data and materialize the above menti-

oned threat. These methods include user behaviour

characterization in online social networks (Beneve-

nuto et al., 2008), as well as analysis of the relation-

ship between users’ gratifications and offline politi-

cal/civic participation (Park et al., 2009). Park et al.

examine the aforementioned relationship through

Facebook Groups and their research indicated the

four needs for using Facebook groups. The analysis

of the relationship between users' needs and civic

and political participation indicated that informatio-

nal uses were more correlated to civic and political

action than to recreational uses.

Users often appear not to be aware of the fact

that their data are being processed for various rea-

sons, such as consumer behaviour analysis, persona-

lized advertisement, opinion mining, user and usage

profiling, etc. Automated user profiling (Balduzzi et

al., 2010) and opinion mining may be used for male-

volent purposes, in order to extract conclusions over

a crowd of users. Balduzzi et al. utilized the ability

of querying a social network for registered e-mail

addresses in order to highlight it as a threat rather

than a feature. Furthermore, they identified more

than 1.2 million user profiles associated with the col-

lected addresses in eight social networks, such as Fa-

cebook, MySpace and Twitter. They also proposed a

number of mitigation techniques to protect the user’s

privacy. Such techniques include CAPTCHA, limit-

ing information exposure, rate-limiting queries to

prohibit automated crawling and raising user aware-

ness. In order to raise awareness the attack was ap-

plied on a realistic environment, consisting of a soci-

al networks group. Graph theoretic analysis has, al-

so, been utilized in order to examine narcissistic be-

haviour of Twitter users (Kandias et al., 2013).

Jakobsson et al., as well as Ratkiewicz (Jakobs-

son et al., 2008); (Jakobsson and Ratkiewicz, 2006),

have also conducted research on a realistic social

media environment regarding online fraud

experiments, such as phishing, with respect to users’

privacy. Such realistic approaches are proposed as a

reliable way to estimate the success rate of an attack

in the real-world and a means of raising user

awareness over the potential threat.

3 METHODOLOGY

In this paper we have experimented with an extensi-

ve Greek community of YouTube. We present a pa-

nopticon political affiliation detection method, in

order to raise user awareness over political profiling

via social media. Furthermore, we present our find-

ings related to political profiling as a proof-of-con-

cept. The twofold purpose of this research is to (a)

raise users’ awareness over political profiling, and

(b) highlight the social threat of processing users’

online available data for discriminative purposes.

3.1 Data Crawling

In order to collect our dataset, we crawled YouTube

using its REST-based API, which simplifies and ac-

celerates the procedure. Each API request includes

parameters, such as the number of results and the

developer key. We have chosen to use a developer

key, as the crawler is less likely to be flagged as ma-

levolent for quota violations. Thus, we managed to

send more API requests and accelerate the process

of data collection. When quota violation emerges,

YouTube rejects all API calls for 10 minutes to “re-

set” quota. Regarding the number of results, YouTu-

be poses a limit of 1000 results/request. However,

the limit turned out to be much lower (50 results/re-

quest); otherwise, YouTube API kept returning error

codes. During the process of data crawling we col-

lected only publicly available data and respected qu-

ote limitations posed by YouTube, so as not to cause

even the mildest harm to YouTube’s infrastructure.

Crawling was initiated by a set of a few Greek

users. In order to crawl more users, we ran a breadth

-first search on user subscribers and on the users

who have commented on the crawled videos. During

the data collection process a user was added to the

crawling queue only if she had added a Greek locati-

on to her profile or had a profile description written

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in Greek.

The gathered data were classified into three ca-

tegories: (a) user-related information, e.g., her profi-

le, uploaded videos, subscriptions, favorite videos,

and playlists, (b) video-related information, e.g., vi-

deo’s license, the number of likes and dislikes it has

received, its category and tags, and (c) comment-re-

lated information, e.g., the content of the comment

and the number of likes and dislikes received. The

collected data include: (a) 12.964 users, (b) 207.377

videos, and (c) 2.043.362 comments. The time span

of the collected data covered 7 years (Nov. 2005 -

Oct. 2012).

We added to the collected data an anonymisation

layer. In specific, usernames have been replaced

with MD5 hashes, so as to eliminate possible con-

nections between collected data and real life users.

Each user is processed as a hash value, so it hardly

feasible for the results to be reversed. Thus, single

real life users cannot be detected.

It is in principle possible, though, to reverse this

process by using indirect means, such as searching

for specific comments in search engines (e.g. Google

hacking), or by utilizing Open Source Intelligence

techniques.

3.2 Graph-theoretic Approach

The forms of interactions in YouTube are easily no-

ticed. Each user can subscribe to other users, so as to

receive notifications about the recently generated

content. Furthermore, users are able to comment on

videos. These types of interaction are considered re-

lationships between users and represent ties in the

network graph of the collected dataset. In this secti-

on we present a graph analysis, so as to identify cha-

racteristics of users’ behaviour in YouTube.

Figure 1: Small world phenomenon.

Figure 2: Indegree distribution.

The main conclusions from this analysis are:

(a). The small world phenomenon does apply to the

collected Greek community. This is depicted in

Fig. 1, where we calculated the effective

diameter of the graph, i.e. every user of the

community is 6 hops away from everyone else

(Watts and Strogatz, 1998).

(b). A small group of users have the most subscri-

bers, while the rest of the users have

considerably fewer subscribers (Fig. 2). Most

nodes (users) have a low number of ingoing ties.

A small fraction of the nodes have a big number

of ingoing ones. Also, a small number of nodes

(users) have a lot of outgoing ties. The rest of the

nodes have fewer outgoing ties (Fig. 3). Higher

outdegree means more subscribers to the user, or

intense comment activity on a video.

Figure 3: Outdegree distribution.

User indegree value indicates the number of

users who subscribe to the user or comment to her

uploaded videos, while outdegree value is the num-

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ber of users to whom she subscribes or comments.

Both indegree and outdegree distributions tend to

have heavier tails (Costa et al., 2007), (Barabasi,

2005) for the biggest values of indegree and outdeg-

ree values, respectively. Fig. 2.shows the indegree

distribution of the graph, while Fig. 3 shows the out-

degree distribution.

(c). Users join YouTube to participate. Fig. 4 repre-

sents the group of nodes formed in the graph.

There is one large and strongly connected com-

ponent consisting of approximately 175.000

users, 2 small connected components with

approximately 20 users, and 3.795 consisting of

one user. Thus, most nodes in the graph have an

outgoing tie to another node. Only a considerably

small number of nodes have no outgoing ties and

is inactive.

Figure 4: Group of nodes.

3.3 Tag Cloud Description

For better observing the axis of content of the collec-

ted data, we visualized the results in the form of a

tag cloud. This is demonstrated in Fig. 5.

Figure 5: Tag cloud of the dataset.

Tags “Greece” and “greek” appear frequently in

the dataset because the experimentation focuses on a

Greek community of YouTube. The majority of the

tag cloud tags are Greek words written in Latin (i.e.

“greeklish). We have transformed the Greek tags to

greeklish, in order to deal with duplicates of a word

(one in Greek and one in greeklish).

The majority of videos are related to music and

entertainment. The next topic that can be found on

the collected YouTube video tags is sports. Several

tags containing Greek sports teams’ names are also

shown in the tag cloud. One may also notice politi-

cal content in the tag cloud (i.e., tags with the names

of the Greek major political parties).

3.4 Panopticon and Youtube

Our experimentation was carried out in a real envi-

ronment (YouTube). We have followed this appro-

ach so as to offer a real-life proof-of-concept of a

panopticon and contribute in the international debate

over the issue. We exploited the results, so as to en-

hance user privacy and raise user awareness, without

disrespecting users’ permissions of profile access.

According to official statistics depicted in Fig. 6

(www.youtube.com/yt/press/statistics.html) YouTu-

be is a popular social medium. Furthermore, it grows

exponentially along with user generated content that

it hosts. YouTube is characterized by emotional-

driven responses in its comments because of videos’

emotional content. Audio-visual stimuli, combined

with the anonymity offered by usernames, appear to

enable users to express their feelings and opinions,

regarding the content of a video. Also, YouTube

users are able to interact with each other through

video comments or subscriptions. Even though it is

not essential for the users to have formed a real life

bond, they can interact on the basis of common inte-

rests, views, hobbies, or political affiliations.

Our observations indicate that users tend to parti-

cipate in the medium and generate personalized con-

tent. YouTube videos and comments contain politi-

cal characteristics as presented in the tag cloud.

Thus, we formed the hypothesis that political affilia-

tion may be extracted via content analysis. Based on

our observations, we consider that:

(a) YouTube often contains political content.

(b) Users often feel free to express their opinions,

especially when it comes to politics (because of

the anonymity they assume and the emotional

content of the medium).

can reasonably expect that they will reveal, inter

alia, their personal data.

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Figure 6: YouTube penetration.

3.5 Drawing Conclusions

We demonstrate that one can identify the political

affiliation of a user via the political beliefs expressed

within the comments of her videos. The reason why

videos are examined is because video is YouTube’s

basic module. Since we cannot process the video it-

self, we draw a conclusion for the video through its

comments. We detect the political affiliation expres-

sed in a comment by performing text classification

into three main categories: (a) category R, which

contains expressions related to radical affiliation, (b)

category C, which contains expressions related to

conservative affiliation and (c) category N, which

contains all the comments that hold a neutral politi-

cal stance or have no political content.

Text classification uses machine learning techni-

ques to classify a comment in the appropriate cate-

gory. Assigning a comment into one of the categori-

es is equivalent to the fact that the comment contains

the respective political affiliation its category de-

picts. An alternative would be to create a vocabulary

including words of each category and scan each

comment to detect specific words. Machine learning

leads to a more reliable result than a simple word e-

xistence check. Also, text classification performs

better than scanning lists of words in a vocabulary.

Comment classification enables to extract conc-

lusions for a video’s political affiliation. The conclu-

sion drawn helps us to classify any video into one of

the defined categories of political affiliations. So, by

assigning a comment into a category implies that the

conclusion drawn for the comment is the political af-

filiation expressed in the category. The same applies

to a list of videos, such as favorite videos and play-

lists. Having the category in which a video falls into,

a conclusion can be drawn for the political affiliation

expressed in the list. Being able to classify user’s

content, we may extract conclusions for user’s com-

ments, uploaded videos, favourite videos and play-

lists. This way we can draw a final conclusion about

user’s political affiliation.

4 PANOPTICON

We store the crawled data in a relational database for

further analysis. The first step of the process is to

train a classifier that will be used to classify com-

ments into one of the three categories of political af-

filiation (radical, neutral, conservative). Comment

classification is performed as text classification (Se-

bastiani, 2002), which uses machine learning techni-

ques to train the system and decide in which catego-

ry a text falls into. The machine is trained by having

as input text examples and the category the exam-

ples belong to. Label assignment requires the assis-

tance of an expert, who can distinguish and justify

the categories each text belongs to.

We formed a training set so as to perform com-

ment classification. By studying the collected com-

ments, we noticed that a significant percentage of

the comments are written in the Greek language. An-

other characteristic of the Greek YouTube commu-

nity is that users write Greek words using Latin al-

phabet in their communication (“greeklish”). This is

the dominant way of writing in Greek YouTube (the

51% of our dataset’s comments are written in greek-

lish). Most users prefer to use greeklish, instead of

Greek, because they do not care about correct spel-

ling of their writings.

The appearance of those two different types of

writing in comments has led us to pick two different

approaches in comment classification, i.e., analyze

them as two different languages. Another issue is

due to the use of both greeklish and Greek. In order

to mitigate this problem we have chosen to merge

these training sets into one and train only one classi-

fier. Forming Greek and greeklish training sets requ-

ires the selection of comments from the database and

proper label assignment for each one of them, based

on the category it belongs to. We consulted a domai-

n expert (i.e., Sociologist), who could assign and ju-

stify the chosen labels on the training sets. Thus we

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103

created a reliable classification mechanism. We cho-

se 300 comments from each category (R,C,N) of the

training set for each language. The expert contribu-

ted by assigning a category label to each comment.

Apart from the training set, we also created a tes-

ting set, which is required to evaluate the efficiency

of the resulting classifier. The testing set contains

pre-labeled data that are fed to the machine to check

if the initial assigned label of each comment is equal

to the one predicted by the machine. The testing set

labels were also assigned by the domain expert.

We performed comment classification using: (a)

Naïve Bayes Mutlinomial (McCallum and Nigam,

1998) (NBM), (b) Support Vector Machines (Joac-

hims, 1998) (SVM), and (c) Multinomial Logistic

Regression (Anderson, 1982) (MLR), so as to com-

pare the results and pick the most efficient classifier.

We compared each classifier’s efficiency based on

the metrics of precision, recall, f-measure and accu-

racy (Manning et al., 2008).

Accuracy measures the number of correct classi-

fications performed by the classifier. Precision mea-

sures the classifier’s exactness. Higher and lower

precision means less and more false positive classifi-

cations (the comment is said to be related to the ca-

tegory incorrectly) respectively. Recall measures the

classifier’s completeness. Higher and lower recall

means less and more false negative classifications

(the comment is not assigned as related to a catego-

ry, but it should be) respectively. Precision and re-

call are increased at the expense of each other.

That’s the reason why they are combined to produce

f-score metric which is the weighted harmonic mean

of both metrics.

Table 1 presents each classifier’s efficiency, bas-

ed on accuracy, precision, recall, and f-score met-

rics. Multinomial Logistic Regression and Support

Vector Machines achieve the highest accuracy. The

accuracy metric is high due to the dominant number

of politically neutral comments. Precision and recall

are proper metrics to evaluate each classifier (Man-

ning et al., 2008).

Table 1: Metrics comparison of classification algorithms.

Metrics

Classifier NBM SVM MLR

Classes

R N C R N C R N C

Precision

65 93 55 75 91 74 83 91 77

Recall

83 56 85 80 89 73 77 93 78

F-Score

73 70 60 76 89 73 80 92 77

Accuracy

68 84 87

Multinomial Logistic Regression achieves better

precision value and SVM better recall value. Multi-

nomial Logistic Regression achieves a slightly better

f-score assesment. Support Vector Machines and

Multinomial Logistic Regression achieve similar re-

sults regarding both recall and precision metrics. As

a result, we chose Multinomial Logistic Regression

because of the better f-score value achieved for each

one of the categories.

4.1 Video Classification

Regarding the extraction of the political affiliation

expressed in a video, we studied each video, based

on its comments, classified to one of the three cate-

gories. Also, we know the number of likes/dislikes

each comment has received. Likes and dislikes re-

present the acceptability a comment has from the au-

dience, so it may be an indication of the comment’s

importance to the overall video’s result. Thus, a

comment that receives a significant number of likes

should be treated differently than a comment with no

likes, as the first one is acknowledged as important

by more users. This assumption has been confirmed

by the data mining process. Subsequently, in order

to extract a conclusion for the video, we take into

consideration only comments that belong either to

category R or C. Neutral comments are ignored.

Each comment importance is measured via its

number of likes and dislikes. In order to come to a

video overall result we utilize two sums, one for ca-

tegory R and one for C. For every comment that be-

longs to categories R or C we add the following

quantity to the respective aggregation:

1   _

⁄





 _

⁄



The quantity added to each sum shows that a com-

ment that has received more likes than dislikes

should affect the overall score more than a comment

with more dislikes than likes. Finally, the category

with the larger sum is the category that represents vi-

deo’s political affiliation.Table 2 illustrates the pro-

cedure described above.

Table 2: Example of video classification decision.

Video “Example”

Comment Political affiliation Likes Dislikes

#1 R 90 10

#2 C 15 20

#3 R 30 5

#4 N 5 2

#5 R 10 3

Total 150 40

Sum R equals to:



1  90 150

⁄

10 40

⁄





1  30 150

⁄

5 40

⁄





1  10 150

⁄

3 40

⁄

4.1,

whereas Sum C equals to



1  15 150

⁄

20 40

⁄



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0.6. Sum R≥ C, so video “Example” is classified to

category R and expresses radical political affiliation.

4.2 List Classification

The procedure followed to extract a conclusion a-

bout a list of videos is similar to the above mention-

ed video method. The only difference is that we uti-

lize videos instead of comments. The two sums are

also applied, one for category R and one for C. In

this case, instead of likes and dislikes we used the

video ones. In the end, the category with the greater

sum is the result for the list’s political affiliation.

This procedure is applied to the “favourite videos”

list, as well as to the other playlists that the user may

have created.

4.3 User Classification

A user political affiliation can be identified based on

the category she is assigned to. The procedure, as

shown in Fig. 7, takes into account a user’s com-

ments, her uploaded videos, her favourite videos,

and her playlists. A user is able to: (a) write a com-

ment to express her feelings or her opinion, (b) up-

load a video (the content may have a distinctive me-

aning for her), (c) add a video to her favourites list

(it may have an emotional or intellectual meaning

for her), and (d) create a playlist and add videos to it.

Figure 7: User classification process.

Based on these observations one may look for in-

dications of political beliefs within the user genera-

ted content. For the needs of our experimentation,

we have defined ad-hoc weights for each of the ca-

ses we examine (Table 3). Each phase of the process

generates a result on the category each user belongs

to. In comment classification, the result is based on

the number of political comments that exhibit the

highest aggregation. If the comments classified in

category R are more than those classified in category

C, then the result is that user tends to be Radical.

The results on uploaded videos, favourite videos,

and playlists are extracted as described in the list

result extraction process. User comments are the

most important factor to decide of a user’s political

affiliation (Table 3).

Table 3: Ad-hoc weights of each element.

Comment Upload Favourite Playlist

Weight

3 2 2 1

Regarding the aggregation of the results, we uti-

lize two sums, one for category R and the other for

C. Comments, videos, and lists classified as neutral

do not contribute to the aggregation. The sub-results

are appropriately weighted and added to the final

sums, in order to extract the final result. An example

of this procedure appears in Table 4.

Table 4: User classification example.

User “Example”

Political beliefs Weight

Comments R 3

Uploaded videos N 2

Favorite videos R 2

Playlists C 1

Sum R equals to 3 + 2 = 5, while Sum C equals

to 1. Sum R ≥ Sum C, which implies that the user

belongs to category R. A user is classified to catego-

ries R, or C, if there is at least one political comment

or political video detected to her content. A user

may not express a political stance via her comments

or uploaded videos; however, she may have added a

video with political content to her playlists. The re-

sult for the user will be that she belongs either to ca-

tegory R or C, depending on the political thesis ex-

pressed in the video. The weights are defined on an

ad-hoc basis. The weight of each result could be bet-

ter determined after a meta-training process.

5 STATISTICAL ANALYSIS

Based on the analysis, the 2% of the collected com-

ments exhibit a clear political affiliation (0.7% was

classified as R, while 1.3% was classified as C). On

the contrary, 7% of the videos were classified to one

of these categories, 2% as R and 5% as C. On the ot-

her hand, 50% of the users have been found to clear-

ly express, at least once, their political affiliation.

Out of them, the 12% of the dataset has been found

to express Radical affiliation and 40% Conservative.

Regarding users classified as radicals, we found

that - on average - the 20% of their comments has a

political position expressed. Also, they tend to prefer

the Greek alphabet (i.e., 54% of their comments are

WhichSideAreYouOn?-ANewPanopticonvs.Privacy

105

written in Greek, 33% in greeklish, and 13% use

both Greek and Latin alphabet).

On the other hand, users classified as conservati-

ves tend to prefer the greeklish way of expression,

namely 55% of the comments are in greeklish, 35%

written using the Greek alphabet and 10% use both

Greek and Latin alphabet. In table 5 the average

number of characters that a comment consists of is

depicted.

Comments written in greeklish tend to be shorter

and more aggressive. On the contrary, comments

written in Greek tend to be larger, more explanatory,

and polite. Another finding is that the more aggressi-

ve a comment, the more misspelled it is.

Table 5: Average number of characters in a comment.

Alphabet

Average no. of

characters (R)

Average no. of

characters (C)

Greek

294 179

Greeklish 245 344

Both 329 227

Regarding the license assigned to each video

(Typical YouTube or Creative Commons), the 7% of

the videos are published under the Creative Com-

mons license. A 55% of these videos were uploaded

by users classified as Radicals, 10% by Conservati-

ves and 35% by Neutrals.

Radicals tend to massively comment on the same

videos. We found that these videos have unequivo-

cal political content, namely political events, music,

incidents of police brutality, etc. Moreover, the vi-

deos that radicals tend to add to their favourites are

mainly documentaries and political music clips.

Conservatives tend to share mainly conspiracy-based

videos, as well ones with a nationalistic content.

6 PANOPTICON AND THE LAW

The use of this kind of methods may result to prob-

lems that are actually inherent in every kind of profi-

ling. In brief, these methods may be regarded as a

kind of (behavioural) profiling on the Internet, in the

meaning of collecting data (recording, storing, track-

ing) and searching it for identifying patterns (Castel-

luccia et al., 2011). Such profiling methods interfere

with the right to informational privacy and are asso-

ciated with discrimination risks.

The observation of the behaviour and characteri-

stics of individuals through mining of large quan-

tities of data may infringe fundamental rights, let

alone the determination of correlation between cha-

racteristics and patterns and the respective classifica-

tion of individuals. A major threat for privacy rights

derives from the fact that profiling methods can ge-

nerate sensitive information “out of seemingly trivial

and/or even anonymous data” (Hildebrandt, 2009).

By studying user’s uploads it is possible to ex-

tract information related to the content, especially

when it refers to areas such as political affiliation.

Furthermore, a user is possible to have a private pro-

file, however her comments could be collected from

crawling random videos. Thus, a limited profile can

be build based on those comments. The predominant

rationales for acquiring knowledge about the politi-

cal opinions and the relative sentiments seems to be

either (political) research purposes or the goal of re-

ducing risks both in the private and the public sector.

However, personal data that are, by their nature, par-

ticularly sensitive and vulnerable to abuse, deserve

specific protection.

Collecting and processing data about political be-

liefs is regarded by law as a highly exceptional situ-

ation. Many international and national laws prohibit

explicitly the processing of personal data revealing

political opinions (e.g. Art. 8 of the European Data

Protection Directive and Art. 6 of the Convention

108 of the Council of Europe). Derogating from the

prohibition on processing this “sensitive category”

of data is allowed if done by a law that lays down

the specific purposes and subject to suitable sa-

feguards. Such derogations rely on a manifest public

interest or the explicit, informed and written consent

of the person concerned.

However, in European data protection law dero-

gation is sometimes allowed also in the cases that

“the processing relates to data which are manifestly

made public by the data subject” (Art. 8, §2e of the

European Data Protection Directive), which is the

case if people generate content or comment on other

users’ content in social networks or media using the-

ir real identity and aiming at expressing their opini-

ons publicly. According to the American theory and

jurisprudence there is no “reasonable expectation of

privacy if data is voluntarily revealed to others” (So-

love, 2006). It is “apparent”, according to this theo-

ry, that one cannot retain a reasonable expectation of

privacy in the case of YouTube, videos, likes, and

comments left open to the public (Henderson, 2012).

By generating content in social media users are

generating information flows and aggregations. Pro-

viders and Online Social Networks encourage - also

through the default settings - “producers” (Bruns ,

2006) to publish personal information and enable

anyone accessing this information thus actively con-

tributing to shaping social media as an attractive

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product (Ziegele and Quiring, 2011). Does self-ex-

posure in social media amount to freely and consci-

ously chosen privacy abandonment?

YouTube offers several privacy options to users.

These privacy options encompass: (a) creation of

private channel/profile, which disables access to u-

ser’s channel where her profile is available, (b) crea-

tion of private videos, which enables users to share

them with a limited number of viewers (up to 50

persons) after inviting them, (c) creation of private

video lists, which applies to favourite videos and

playlists and disable a playlist from being publicly a-

vailable, and (d) potentially disclose user’s activity,

e.g., comments, subscriptions or favourite videos.

These options may protect user’s actions from being

tracked in order to get information about the video

she likes, comments on, or the users she is subscrib-

ed to. However, we should take into consideration

individual’s general inertia toward default terms

(Mitrou, 2009); (Mitrou, 2003); (Lambrinoudakis et

al., 2003). Moreover, it seems that the majority of

users choose to disclose their personal data to as ma-

ny users as possible, although average users do not

have a clear idea about the actual reach of informa-

tion they reveal or they underestimate the possible

reach of their profiles visibility (Ziegele and Qui-

ring, 2011).

Users are losing control over their data and the

use thereof, as they are becoming detectable and

“correlatable”. The combination of all this informati-

on provides a powerful tool for the accurate profil-

ing of users. Moreover, it is quite simple to identify

a particular person, even after her key attributes (na-

me, affiliation, address) have been removed, based

on her web history (Castelluccia et al., 2011).

However, even if individuals are profiled in a

pseudonimised way they may be adversely influenc-

ed (Schermer, 2011); (Spinellis, 1999).

Informational privacy protects individuals against

practices that erode individual freedom, their

capacity for self-determination, and their autonomy

to engage in relationships and foster social

appearance. If individuals fear that information

pertaining to them might lead to false incrimination,

reprisals or manipulation of their data, they would

probably hesitate to engage in communication and

participatory activities (Mitrou, 2010). The autono-

my fostered by informational privacy generates col-

lective benefits because it promotes “reasoned parti-

cipation in the governance of the community” (Co-

hen, 2000).

Risks of misuse and errors arising out of the ag-

gregation and data mining of a large amount of data

made public for other purposes are manifest. Accor-

ding to the German Federal Constitutional Court the

“cataloguing” of the personality through the connec-

tion of personal data for the purpose of creating pro-

files and patterns is not permitted (Judgment of the

Bundesverfassungsgericht, 4 April 2006, 1 BvR 518/

02, 23.05.2006). A mass profiling of persons on the

base of their views expressed in social media could

have intimidation effects with further impacts on

their behaviour, the conception of their identity and

the exercise of fundamental rights and freedoms

such as the freedom of speech (Cas, 2011). Fear of

discrimination and prejudice may result to self-cen-

sorship and self-oppression (Fazekas, 2004). Indeed,

while profiling risks are usually conceived as threats

to informational privacy we should point out the - e-

ventually more - significant and actual risk of discri-

mination (Gutwirth and Hert, 2008). The safeguards

relating to the use of personal information aim -

among others. if not principally - at preventing dis-

crimination against persons because of their opini-

ons, beliefs, health or social status. Studies conveyed

how profiling and the widespread collection and ag-

gregation of personal information increase social in-

justice and generate even further discrimination a-

gainst political or ethnical minorities or traditionally

disadvantaged groups (Mitrou, 2010).

Individuals may be confronted with major pro-

blems both in their workplace and in their social en-

vironment. Employers or rigid micro-societies could

demonstrate marginalizing behaviour against per-

sons because of their deviating political affiliation.

There are a lot of historical examples of people who

have been side-lined by the hegemonic attitude of

society. One should not look for numerous examples

in order to evaluate this thesis: Victor Hugo’s “Les

Miserables” (Section X: The Bishop in the presence

of an unknown light) is the most representative evi-

dence towards this result.

If we may generalize the above mentioned consi-

deration to a macro environment, consequences to

the deviating from the average political affiliation

could lead to mass social exclusion, prejudice and

discriminations. Such minorities may even be consi-

dered de facto delinquent and face a social stigma.

In the context of a totalitarian/authoritarian regime,

implementation of such methods could lead to mas-

sive violation of civil and human rights or even

threat the life of specific individuals.

7 CONCLUSIONS AND

RESEARCH

In this paper we dealt with the possibility of a social

WhichSideAreYouOn?-ANewPanopticonvs.Privacy

107

threat that is based on user generated content exploi-

tation and leads to political affiliation profiling; na-

mely a new panopticon of the digital era. Political

beliefs and affiliation have been a cause for social

marginalization, prejudice, and discrimination, espe-

cially in totalitarian and authoritarian regimes. Thus,

we bring this issue to the fore and contribute to the

debate and awareness raising. A user might want to

protect personal information other than political affi-

liation, namely information related to sexual orienta-

tion, racial discrimination or even the health conditi-

on of the user regardless of the national scope. Such

an improper information disclosure could be easily

conducted via expantion of the Panopticon methodo-

logy on the condition that domain experts of each

case are available to interp-ret the collected data and

train an appropriate model.

In order to prove and highlight the above mentio-

ned we developed a panopticon methodology that is

able to materialize this threat. During our research

we collected a number of 12.964 users, 207.377

videos and 2.043.362 comments from YouTube. Af-

terwards, we conducted content and graph theoretic

analysis of the dataset in order to verify that it is

possible to extract conclusions over users’ political

affiliation. Our results confirmed the initial hypothe-

sis that YouTube is a social medium that can support

the study of users’ political affiliation, namely au-

dio-visual stimuli along with the feeling of anonymi-

ty enables users to express their political beliefs, e-

ven the most extreme ones.

The panopticon needs the contribution of a field

specialist in order to assign category labels (Radical,

Neutral or Conservative) to each comment of the

training set. Then, a machine is trained in classifying

YouTube comments to these categories. We experi-

mented with three algorithms, i.e., Naïve Bayes

Multinomial, Support Vector Machines, and Multi-

nomial Logistic Regression. Comparison of each

classifier’s efficiency was based on the metrics of

precision, recall, f-score and accuracy and indicated

that the MLR algorithm is the most appropriate be-

cause of the better f-score value achieved for each of

the categories. F-score is a combination of recall and

precision metrics. Classifying comments to these ca-

tegories enables the panopticon to classify playlists,

lists of favourites and uploads, thus it manages to

classify users to the above mentioned categories.

Furthermore, we carried out a series of statistics

regarding our dataset. In specific we quoted charac-

teristics and demographics of the radical and conser-

vative users that we located in our data. Alongside

with these characteristics, we highlighted possible

consequences of an alleged implementation of the

described panopticon method. Regardless of the sco-

pe of the implementation, the resulting threats in-

clude working place discriminations, social prejudi-

ce or even stigma and marginalization of the victims.

These phenomena could be identified even in a de-

mocratic and stable society, not to mention the th-

reats one could face in a military or totalitarian regi-

me. Thus, we adopted a pro-privacy attitude and in-

cluded a legal point of view in our analysis, along

with the emergence of the demand for raising social

awareness over this threat and the necessity for insti-

tutionalization of digital rights.

For future work we plan on further studying the

panopticon and recognize more aspects of this social

threat. We intend on spreading our research on other

social media and study the phenomenon under the

prism of different tools and methodologies along

with optimization of our weight factors. Finally, we

plan on proposing optimized methods for online pri-

vacy and anonymity with particular interest on the

field of social media.

ACKNOWLEDGEMENTS

Authors would like to thank N. Bosovic for his help

in the process of data mining, K. Galbogini for her

contribution in the graph theoretic analysis of the da-

taset and V. Rongas for his help with Sociology and

political affiliation analysis.

This work was supported in part by the Solo (56

NEW_B_2012) project, funded by the Hellenic Ge-

neral Secretariat for Research & Technology.

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