=Paper=
{{Paper
|id=Vol-2699/paper44
|storemode=property
|title=Data Privacy in Journalistic Knowledge Platforms
|pdfUrl=https://ceur-ws.org/Vol-2699/paper44.pdf
|volume=Vol-2699
|authors=Marc Gallofré Ocaña,Tareq Al-Moslmi,Andreas L. Opdahl
|dblpUrl=https://dblp.org/rec/conf/cikm/OcanaAO20
}}
==Data Privacy in Journalistic Knowledge Platforms==
https://ceur-ws.org/Vol-2699/paper44.pdf
Data Privacy in Journalistic Knowledge Platforms
Marc Gallofré Ocañaa , Tareq Al-Moslmia and Andreas L. Opdahla
a University of Bergen, Fosswinckelsgt. 6, Postboks 7802, 5020 Bergen, Norway
Abstract
Journalistic knowledge platforms (JKPs) leverage data from the news, social media and other sources. They collect large
amounts of data and attempt to extract potentially news-relevant information for news production. At the same time, by
harvesting and recombining big data, they can challenge data privacy ethically and legally. Knowledge graphs offer new
possibilities for representing information in JKPs, but their power also amplifies long-standing privacy concerns. This paper
studies the implications of data privacy policies for JKPs. To do so, we have reviewed the GDPR and identified different areas
where it potentially conflicts with JKPs.
Keywords
Privacy, Personal data, Journalistic Knowledge Platforms, GDPR
1. Introduction interest is not crystal clear.
Data privacy has become a central topic of discus-
Journalistic Knowledge Platforms (JKPs) are an emerg- sion for organisations and projects from private com-
ing generation of platforms which combine state- panies and governments to research activities in uni-
of-the-art artificial intelligence (AI) techniques, like versities around the globe. Whereas there is no general
knowledge graphs and natural-language processing solution to privacy for everyone and specific solutions
(NLP) [1, 2] for transforming newsrooms and leverag- vary between different countries, cultures and organi-
ing information technologies to increase the quality sations, privacy is a common concern, which has been
and lower the cost of news production. JKPs exploit discussed from the ethical and philosophical points of
and combine news, social media and other informa- view by many different authors [6, 7] and organisa-
tion sources, using linked open data (LOD), digital tions like the European Commission [8, 9]. The EU
encyclopaedic sources and news archives to construct has established the General Data Protection Regula-
knowledge graphs and provide fresh and unexpected tion (GDPR) which sets up a framework for governing
information to journalists, helping them dive more the usage, processing, privacy and security of personal
deeply into information, events and story-lines [3]. data, granting individuals power over their data and
JKPs of various kinds are becoming increasingly im- making organisations responsible for data collection
portant in leading news agencies like BBC [4] and and usage practices.
Thomson Reuters [5]. Our group have been developing News Hunter [10,
However, obtaining and representing knowledge 11, 12], a series of JKP architectures and prototypes.
leads to data privacy concerns when personal data The current News Hunter platform is big-data ready
from different sources is neither collected directly and designed to continually harvest and monitor real-
from the subject nor with the subject’s consent, al- time news feeds (e.g., RSS or web-sites) and social
though some countries have exemptions that loosen media (e.g., Twitter and Facebook). It aims to analyse
privacy requirements for journalistic research that is and represent news content semantically in knowl-
in the public interest or does not identify individuals edge graphs in order to provide better background
directly. This exemption becomes even more complex information for journalists and to suggest news an-
when the national privacy policies that apply to the gles [13, 14, 15, 16, 17].
data sources and the JKP are distinct or the public As part of our News Hunter effort, this paper inves-
tigates the implications of the GDPR on JKPs. To do so,
Proceedings of the CIKM 2020 Workshops, we asked ourselves which data privacy conflicts can
October 19-20, Galway, Ireland. arise when JKPs when are used in journalistic work,
email: Marc.Gallofre@uib.no (M. Gallofré Ocaña);
Tareq.Al-Moslmi@uib.no (T. Al-Moslmi); Andreas.Opdahl@uib.no
in particular when that work may be exempted from
(A.L. Opdahl) some privacy regulations because it is in the public in-
orcid: 0000-0001-7637-3303 (M. Gallofré Ocaña); terest. To the best of our knowledge, there is no previ-
0000-0002-5296-2709 (T. Al-Moslmi); 0000-0002-3141-1385 (A.L. ous work discussing the possible data privacy conflicts
Opdahl)
© 2020 Copyright for this paper by its authors. Use permitted under Creative in JKPs. Our contributions are: (1) we review different
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org) journalistic scenarios and personal data sources that
can conflict with GDPR policies, and (2) we introduce an autonomous person that states must protect to pre-
a personal data matrix framework to classify personal serve a democratic society, the concept of privacy in
data conflicts and discuss the possible uses of this ma- the United States of America is understood primarily
trix. as a physical notion that implies the “private space”
This paper is organised as follows: section 2 defines (e.g., bedroom, bathroom or the entire home) [6].
the main privacy concepts, section 3 discusses poten- These differences are reflected in the data privacy reg-
tial data privacy conflicts in JKPs, section 4 introduces ulations of the EU and the USA. The EU states in the
the personal data matrix framework, section 5 sum- General Data Protection Regulation (GDPR) [29] that
marises the conclusions, and section 6 presents open individuals must be notified and have the right to con-
questions and future work. sent when their personal data is collected from either
inside or outside EU legislation. In contrast, the US
only regulates privacy issues regarding health matters
2. Background and some financial information, leaving the rest to
individual states or businesses which do not need to
2.1. Journalistic knowledge platforms ask for individuals consent and give the possibility
Journalistic Knowledge Platforms (JKPs) leverage and to individuals to resign if they have any reservations
combine news, multimedia content (e.g., TV news about what is being collected from them. On a global
channels and podcast) social media (e.g., Twitter and scale beyond EU and the USA, differences in how pri-
Facebook), web-blogs and information over the net, vacy is viewed are even bigger, making it even more
using linked open data (LOD), digital encyclopaedic challenging to handle privacy regulations when JKPs
sources (e.g., Wikipedia and Wikidata) and news are used in fully international news organisations that
archives to provide fresh and unexpected information operate across cultural and legal domains.
to journalists. Projects like Neptuno [18], Event Reg-
istry [19], NEWS [20], NewsReader [21], SUMMA [22] 2.3. GDPR
and News Angler [11, 16, 12] have presented examples
of JKPs. All actions using or processing personal data of data
A typical JKP comprises a knowledge graph [23, 24, subjects who are in the European Union shall obey
25] along with AI, NLP pipelines, and semantic tech- the General Data Protection Regulation (GDPR) [29].
nology components. In a JKP, the Knowledge graph The GDPR is an extensive regulation which sets the
is filled with potentially news-related histories, in- basis for dealing with personal data in the EU or using
formation and current and archival news to support personal data from the EU. This section highlights the
journalists in creating newsworthy stories, finding most general concepts that restrict what and how to
relevant information, events and story-lines, and val- process personal data in JKPs.
idating and verifying news. The information in the The GDPR defines the concepts of personal data and
knowledge graph is represented using standard iden- processing (Chapter I, Article 4). Personal data is any in-
tifiers and semantic knowledge representations with formation that can be employed to identify directly or
reasoning capabilities. The usage of standard identi- indirectly a natural person (e.g., name, an identifica-
tion number or online identifier) or sensitive data like
fiers facilitates data integration, which is the process
of joining and merging different data sets or public health, biometric, genetic, economic, cultural factors
data sources like Semantic Web [26, 27], linked open or political opinions of a natural person. Data that has
data (LOD) [28], including Wikidata and DBpedia, and been de-identified, encrypted or pseudonymised but
Wikipedia. Data integration together with reasoning can be used to re-identify a person is considered as
allows drawing new insights from information from personal data too. By processing, the GDPR means pro-
across the data that would be impossible before with cesses such as collection, structuring, storage, alter-
isolated datasets. This inherent ability of drawing ation, consultation, use, disclosure, combination, re-
new insights implies that new personal data may be striction, erasure or destruction of personal data.
derived and exposed in the knowledge graph. Moreover, the GDPR establishes a set of principles
for processing personal data (Chapter II ) which define
how data have to be processed, stored and maintained.
2.2. Privacy These set of principles establish that data shall be pro-
Privacy is a historically and culturally situated con- cessed within the initial purposes and purposes com-
cept. For example, whereas privacy in Europe is tra- patible with them (purpose limitation), only what is
ditionally considered as an inalienable basic right of necessary to the purpose (data minimisation), personal
data shall be accurate and kept up to date (accuracy), not present a problem with the GDPR. However, the
and stored for no longer periods than the necessary for data can be made it publicly accessible by the subject
the purpose (storage limitation). It also defines the law- itself in social media networks (e.g., posts like tweets
fulness of processing which determines when personal in Twitter or forums and groups like Facebook groups)
data can be processed, e.g., when data subject gives or in the subject’s verifiable social media accounts and
the consent or for a task carried out in public interest. personal web sites without providing explicit consent
Under the GDPR, some research by journalist and aca- for its collection to the JKP. In that case, apart from
demics is understood as public interest. Likewise, the having to follow the source’s data policies, it raises
GDPR limits the processing of sensitive data which is the ethical questions whether the consent is implicit
prohibited in general terms but with some exceptions, because it is publicly available, when we should con-
e.g., when data subject gives explicit consent, it is nec- sider that it is publicly available and under which
essary for reasons of substantial public interest, or the conditions.
data subject has manifestly made it public.
The GDPR also details when and which information 3.2. Personal data from third parties
have to be provided to the data subjects (Chapter III ).
In the case of personal data that is not obtained di- When the data is not collected directly from the sub-
rectly from the subject, it determines which data have ject, instead, it has been made accessible by a third
to be provided, e.g., the source of the personal data party and subject may ignore its existence, we have
and whether it came from publicly accessible sources to consider two possible scenarios:
or the categories of personal data. Nevertheless, it also The first scenario, when news-related information
establishes some exemptions, e.g., when the provision is gathered from the web (e.g., online news, RSS, web-
of such information proves to be impossible or is likely sites or social media), JKPs can extract personal data
to harm the objectives of the processing objective. from the content to represent and combine it in the
knowledge graphs. E.g., from “We know the classic 7-
layer dip, made with Bush’s Beans, is a fan favorite for
3. Privacy conflicts in JKPs game day snacking celebrations, Kate Rafferty, the con-
sumer experience manager for Bush’s, told Fox News.” 1 ,
When discussing which scenarios in JKPs can cause a we can extract information like “Kate Rafferty is a per-
conflict with the GDPR we must consider the source son who works as consumer experience manager at
of the personal data, distinguishing between the data Bush’s Beens company at Knoxville, Tennessee” which
gathered directly from the subject, the data harvested can be considered as personal data, as it can be used to
from other sources like news or social media and the identify a natural person. According to the GDPR, the
inferred data. subject should be notified, and the JKP has to provide
In the context of GDPR, some data processing by a mechanism for the subject to protest. Even though,
journalists is exempted when it is conducted in the on a large scale, two issues arise: the number of notifi-
public interest. However, this exemption exclusively cations that famous people will get and how to contact
applies to journalistically relevant (newsworthy) per- subjects if the content information is missing.
sonal data, not to any personal data processed in the The second scenario, when personal data is gath-
JKP, and sensitive data may be less exempted or not ex- ered from publicly available sources or open sources
empted at all. Therefore, we must also consider how like Wikipedia, Wikidata or telephone/address books,
relevant the personal data is for the public interest it is clear that the personal data is already public. How-
from a news perspective. This includes the assess- ever, it may not be released with subject’s consent. In
ment of newsworthiness [30] along with the type of that case, it opens the question about: why should the
news. E.g., a corruption scandal and a private event in JKP not be allowed to store copies of personal data
the life of a famous person may both be highly news- which is already public?
worthy, but corruption is most likely more important
for the public interest.
3.3. Inferred personal data
3.1. Personal data from the subject When personal data is not gathered from any source,
instead, it is inferred using the actual data (either from
When personal data comes directly from a subject and the data subject, collected from news or gathered from
is collected with the subject’s explicit consent (e.g.,
1 Drew Schwartz, VICE: This 70-Layer Bean Dip Is the Most Vile
personal data collected during an interviewed), it does
Thing I’ve Ever Seen (https://t.co/qKyyNevpBh)
public sources) and reasoning techniques. E.g., from Table 1
the text “The European Court of Justice (ECJ) said that Personal data matrix
Oriol Junqueras had become an MEP the moment he Consented Collected Inferred
was elected in May, despite being on trial for sedition.” 2 , Impersonal Data ✔ ✔ ✔
we can represent the person “Oriol Junqueras” as the Personal Data ✔ ! !
entity Q116812 from Wikidata, from which we can Sensitive Data ✔ !! !!
derive that he is a member of a political party (P102)
and the political party is “the Republican Left of Cat-
alonia” (Q150068). With this information is it possible 4. Personal data matrix
to infer the subject’s political ideology (P1142) from
the political party information such as “republican- After reviewing the previous scenarios, we classi-
ism” (Q877848) and “Catalan pro-independence move- fied the different situations that can cause a conflict
ment” (Q893331). In this scenario there is not a direct with the GDPR into a two-dimension matrix (figure 1)
source of subject’s personal data or political opinions, framework. The personal data matrix aims to help
instead, there is a source of related information used journalists and JKP developers to classify the personal
for inferring knowledge which can be either in the data in JKPs and its possible issues with privacy poli-
same knowledge graph or from external sources. cies.
The personal data matrix (figure 1) classifies per-
sonal data based on the privacy level and the data
3.4. Possible solutions source. The first dimension (privacy level) classifies
To comply with the GDPR’s Chapter III, in any of the the data whenever it does not represent personal data
previously discussed scenarios it is important to iden- (impersonal data), it represents personal data or it rep-
tify the data source and personal data category (e.g., resents “sensitive data”. There is an explicit distinction
name, ID number, online identifier, health data, po- between personal and “sensitive data” because in the
litical opinion). Thus, it will be possible to identify GDPR “sensitive data” have much more restricted lim-
both the source and data and take actions accordingly. itations. The other dimension, the data source dimen-
Although the main responsible of complying with the sion, classifies data based on the data collected with
GDRP in the first place is the data provider (i.e., news the subject’s consent (consented), the data directly
website, social media platform or telephone/address collected from the content and the inferred data. Only
books), JKPs should follow the GDPR to safeguard the when data is either explicitly consented or it is not
subjects of privacy and consider the policies and re- personal data its treatment is straightforward. Oth-
strictions established by the data provider. The JKP erwise, as discussed in the previous section, each of
must always take independent responsibility for pri- these combinations has its issues and open questions
vacy, and it cannot trust its sources to safeguard pri- regarding the application of the GDPR and its origin.
vacy. In a truly international and global set-up, where The data matrix can be also regarded as a cube,
different privacy policies apply, JKPs may have to be where the public interest represents the third dimen-
designed with different knowledge graphs for differ- sion. This third dimension determines to what extent
ent legal domains or geographical regions, each graph the GDRP exemption to data processing for journalis-
only being accessible from its own privacy domain. tic purposes in the public interest applies, taking into
When this is infeasible, the most restrictive policies to consideration the newsworthiness component of the
guarantee personal data privacy must be adopted. data.
Moreover, JKPs should also implement automatic The proposed matrix can be used by JKP researchers
mechanisms to notify subjects with both the personal and developers to ensure – as automatically as possi-
data and the sources when this information is iden- ble, but in practice aided by human data privacy stew-
tified, a process that can be done by email. It is also ards – that privacy regulations are never violated. The
possible to set up an automatic system for subjects to matrix should be used in the design of JKPs to ensure
protest, complain, request or ask about personal data. that personal data is protected by default. E.g., devel-
opers of JKPs can use the matrix to evaluate the sys-
tem and identify which processes or collected data can
lead to privacy conflicts; implement the matrix as part
of the news creation workflow so that journalists can
2 BBC: Jailed Catalan leader ’should have had immunity’, rules
automatically check data privacy compliance before
EU court (https://www.bbc.com/news/world-europe-50808766)
collecting, re-combining or using any personal data;
it can be utilized as metadata for each piece of data in Acknowledgments
the knowledge graph to automatise its recognition and
privacy assurance; and the matrix can be used when Supported by the Norwegian Research Council IKT-
dealing with data under different regulations to find PLUSS project 275872 News Angler, which is a collab-
divergences between them. oration with Wolftech AB, Bergen, Norway.
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