https://doi.org/10.31449/inf.v45i7.3410 Informatica 45 (2021) 25–30 25
EveOut: an Event-centric News Dataset to Analyze an Outlet’s Event Selection
Patterns
Swati, Dunja Mladeni´ c and Tomaž Erjavec
Jožef Stefan Institute and Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
E-mail: swati@ijs.si, dunja.mladenic@ijs.si, tomaz.erjavec@ijs.si
Keywords: dataset, news event analysis, event selection bias, news coverage, gatekeeping bias, outlet prediction, digital
humanities
Received: January 10, 2021
Automation of computational models to study the structure of events and their value to news outlets is an
effective way to understand event-outlet relationships. However, the scarcity of publicly available, com-
prehensive event-centric news datasets restricts the implementation of such models. To overcome this bot-
tleneck, we collected seventeen months of event data using Event Registry to generate EveOut, a publicly
available event-centric news dataset. To conduct statistical analysis, we first select five English-language
and three in Slovenian-language news outlets. We then retrieved all the events covered by them and used
it to document the prevalence of geographical, temporal, categorical, and several other aspects of the event
selection bias by these outlets. We illustrate the significance of our dataset in the field of digital human-
ities by identifying a motivating use case. The dataset is publicly available from the dedicated website
http://cleopatra.ijs.si/EveOut/, which provides a detailed description of the fields, usage
information, and a link to the GitHub repository.
Povzetek: V prispevku predstavljamo EveOut, javno dostopno množico podatkov, zgrajeno na osnovi do-
godkov, o katerih poroˇ cajo mediji. EveOut je zasnovana za pomoˇ c pri analizi in razumevanju zapletenega
odnosa medijev do poroˇ canja o posameznem dogodku. Zgrajeno množico podatkov smo tudi uporabili za
raziskovanje geografskih, ˇ casovnih, vsebinskih in drugih vidikov pristranskosti nekaj izbranih poroˇ ceval-
cev pri izbiri dogodkov, o katerih poroˇ cajo.
1 Introduction
News outlets are constantly confronted with the task of se-
lecting events to be reported on. This selection is based on
the newsworthiness of an event which can be defined by
the presence or absence of several news values such as the
inclusion of the power elites, the relevance, and popularity
of the topic, etc. Determining the news value for an outlet
may result in a selection bias, also known as gatekeeping
bias [7]. A journalist, for instance, is more likely to re-
port on an event that includes fresh data on an existing and
trending event.
Gatekeeping bias can be significantly reduced by study-
ing and analyzing the correlation and impact of differ-
ent features on the selection of events by the outlets and
then using the knowledge to automate the event selection
process. Computational models for the study of complex
event-outlet relationships can help explore the strategies
for selecting publishable events and automating the event
selection process. However, to stimulate the development
of these models, the availability of data on news events and
their relevant details is necessary.
In this paper, we present EveOut, the first, large, publicly
available event dataset generated by leveraging the events
collected using EventRegistry [4]. The resulting dataset,
called “EveOut”, consists of81;562 news events in English
and Slovenian language, with a varied range of features re-
trieved for the period between January 2019 and May 2020.
We hope that EveOut will serve as a benchmark dataset
to study the event-outlet correlation and help mitigate the
impact of implicit bias present in the production and report-
ing process. We also hoped that it will encourage the pub-
lishers and others involved in the news production process
to develop tools to enhance digital journalism and facilitate
research in this field.
The contributions of this paper are as follows:
– We present EveOut, a publicly available novel event-
centric news dataset generated with a wide range of
features using the EventRegistry platform.
– We provide flexible dataset generation scripts that fa-
cilitate the generation of custom versions of EveOut
with the required features.
– We identify a potential use-case ‘outlet prediction
task’. For the task, we then illustrate how conditional
probabilistic models can be used to estimate the cor-
relation between outlets.
– We present a detailed statistical analysis with respect
to multiple event features to compare, contrast, and
infer the coverage pattern of the selected news outlets
publishing in English and Slovenian languages.

26 Informatica 45 (2021) 25–30 Swati et al.
2 Related work
A number of datasets are based on news articles but, to
the best of our knowledge, only a few datasets that ex-
plicitly focus on event-centric data have been proposed.
GDELT (Global Data on Events, Location, and Tone) [5]
is a CAMEO-coded [6], open, and large-scale news dataset
that tracks the news media around the world in multiple lan-
guages. The articles are then compiled into a list of events,
for rich and insightful event analytics. However, there is
no attribute in the dataset that would specify the outlet(s)
for the event. As a result, there is a lack of information
in GDELT that is crucial to the study of the event-outlet
relationship that forms the foundation of our dataset.
In terms of availability, publicly available event datasets
are scarce. There is some related research on event data [3,
1], but the datasets extracted/generated for the experiments
are not publicly available. Besides, the majority of the ex-
isting event datasets [2] are category-dependent (politics,
healthcare, disaster, etc.) which renders them useful for
specific research purposes only. EveOut addresses these
bottlenecks by introducing a generalized publicly available
event-centric news dataset.
Select Outlets Ex: Top 5 Global Newspapers Set Time Constraint Ex: 2019-01-01 to 2020-05-31 Generate Event List Ex: eng-4500343 Extract Event Info Ex: id, date, title, summary, ... Generate Outlet Label Ex: 0- Not Covered, 1- Covered EveOut - Event Outlet Figure 1: EveOut generation process, composed of user
selection of the outlets and the time period, automatic ex-
traction of the data from Event Registry and labeling of the
extracted data.
(a) English news outlets.
(b) Slovenian news outlets.
Figure 2: Distribution of event coverage by the outlets.
3 Data description
3.1 Raw data source
Event Registry
1
[4] monitors, collects, and delivers news
articles from news sources around the world in more than
30 languages. It extracts semantic information from the
articles and if the same event is described in multiple ar-
ticles, it aggregates them into clusters using several clus-
tering algorithms. These article clusters are referred to as
events. For instance, “Trump threatens to shut down so-
cial media firms” is an event recorded internationally in
more than 1,220 news articles. Each event is then anno-
tated with various metadata, such as a unique id to track
the coverage of the event, topic, categories to which it may
belong, geographical location, sentiments, etc. As a result,
its large-scale temporal coverage can be used effectively to
study the event-outlet relation.
3.2 Data generation process
For the data generation process, as depicted in Figure 1, we
first selected five English and three Slovenian news outlets
(for the sake of simplicity, we refer news outlets publishing
in English/Slovenian language as English/Slovenian news
outlets throughout the paper). We selected these outlets
following the work in [8] which is based on Alexa Global
Rankings of top news outlets.
We then used an explicit temporal query (Q
t
) to retrieve
all events in all news categories using Event Registry API.
Q
t
= fQ
text
; Q
time
g consists of the text component
Q
text
and the time component Q
time
. Next, we set the
time limit Q
time
= [Q
sd
;Q
ed
] for extracting events that
occurred within the specified time where, Q
sd
= ‘2019-
-01-01’ and Q
ed
= ‘2020-05-31’ signify the event’s start
date and end date. Since the outlet’s event selection policy
may change over time, we selected this time frame as re-
cent data tends to be more reliable in predicting event cov-
erage patterns. We then setQ
text
= fQ
out
; Q
lang
; Q
cat
g
where,Q
out
2
,Q
cat
3
, andQ
lang
4
represents the list of out-
1
https://eventregistry.org
2
https://eventregistry.org/documentation?tab=
suggSources
3
https://eventregistry.org/documentation?tab=
suggCategories
4
https://github.com/EventRegistry/

EveOut: an Event-centric News Dataset to. . . Informatica 45 (2021) 25–30 27
Attribute Description
uri a unique event identifier
title event title in the specified language
event_date date in yyyy-mm-dd format
sentiment event sentiment
categories event categories
loc_country country where the event occurred
loc_continent continent where the event occurred
total_article_count total number of articles published
article_count total number of articles published in the specified language
summary summary of the event
outlet_list list of outlets that reported the event
Table 1: Description of the dataset attributes.
P(O
1
jO
2
) Nytimes Indiatimes Washingtonpost Usatoday Chinadaily
Nytimes 1.00 0.09 0.28 0.24 0.19
Indiatimes 0.03 1.00 0.03 0.03 0.01
Washingtonpost 0.33 0.09 1.00 0.26 0.19
Usatoday 0.27 0.09 0.25 1.00 0.13
Chinadaily 0.10 0.01 0.08 0.06 1.00
(a) English news outlets.
P(O
1
jO
2
) Delo Dnevnik Veˇ cer
Delo 1.00 0.33 0.33
Dnevnik 0.51 1.00 0.49
Veˇ cer 0.30 0.29 1.00
(b) Slovenian news outlets.
Table 2: Conditional probability of an event to be covered by an outlet (in rows), provided it is covered by another outlet
(in columns).

28 Informatica 45 (2021) 25–30 Swati et al.
lets, categories, and languages respectively.
For English news outlets, we set Q
out
=
f‘nytimes:com’; ‘indiatimes:com’; ‘washington-
post:com’;‘usatoday:com’;‘chinadaily:com:cn’g and
Q
lang
= f‘eng’g and we setQ
out
= f‘delo:si’;
‘dnevnik:si’; ‘vecer:com’g and Q
lang
= f‘slv’g
for Slovenian news outlets. We fixed Q
cat
=
f‘news=Politics’; ‘news=Business’; ‘news=Spor-
ts’; ‘news=ArtsandEntertainment’; ‘news=Sci-
ence’; ‘news=Technology’; ‘news=Health’; ‘new-
s=Environment’g to represent the news categories. If an
event falls into more than one category, it is labeled with
multiple categories.
We first excluded events from the extracted event list that
weren’t covered by any of the selected outlets. We then ex-
tracted individual outlets from the event’s outlet list and
generated a column in each dataset to denote individual
outlets. We used a binary scalar value to indicate whether
the outlets covered the event or not. Table 1 describes the
attributes of the generated dataset. From Figure 2, it is ap-
parent that the event coverage by the outlets is not uniform.
4 Availability and reusability
For ease of discovery and preservation, EveOut is archived
as an online resource at https://doi.org/10.
5281/zenodo.3953878. It is well documented in ac-
cordance with the requirements of the FAIR Data princi-
ples
5
and is freely accessible under the Creative Commons
Attribution 4.0 International license to make it reusable for
nearly any purpose. For dataset regeneration, the GitHub
repository at https://github.com/Swati17293/
EveOut gives the source code of the collection process.
For an in-depth analysis, a separate web page with de-
tailed statistics and illustrations can be found at http:
//cleopatra.ijs.si/EveOut/.
The resource is currently being used in several studies
within a larger research project
6
. A major part of this
project aims to provide a temporal, cross-lingual analysis
of concepts around different events, exploring how lan-
guage impacts the mediatic narratives built by the media.
Since EveOut serves as the basis for the study and analy-
sis of events and their attributes, it is ideally suited to the
project needs.
5 Potential use case - outlet
prediction
Outlet Prediction is the task of estimating the probability
that an event will be covered by an outlet. In addition to
allowing the publishers of the outlets to evaluate the sig-
nificance of the event, this task is intended to benefit in-
event-registry-python/wiki/Supported-languages
5
http://www.nature.com/articles/sdata201618/
6
http://cleopatra-project.eu/
dependent editors who prefer to report on events covered
by mainstream outlets. It can be best assessed by calculat-
ing the conditional probabilityP of an event covered by an
outletO
1
given that it is already covered by another outlet
O
2
using the following equations.
P(O
1
jO
2
)=
P(O
1
\O
2
)
P(O
2
)
; ifP(O
2
)>0 (1)
=0; ifP(O
2
)=0 (2)
Table 2a shows that apart from ‘Indiatimes’ and ‘Chi-
nadaily’, rest of the outlets tends to overlap each other in
terms of event coverage. It is also interesting to note from
Table 2, that among the listed outlets, the likelihood of any
outlet to cover an event, given that it is already covered by
any other outlet is higher (higher P values) for Slovenian
outlets.
Unlike the selected English outlets which are suppos-
edly global, the selected Slovenian outlets are the major
outlets in Slovenia which is a small country. This differ-
ence influences the coverage pattern of the outlets, which
reveals how regional priorities affect the event selection
process. For instance, P(DnevnikjDelo) = 0:51 and
P(DnevnikjVeˇ cer) = 0:49 which is quite high as com-
pared to the others which indicates that if an event is cov-
ered by either ‘Delo’ or ‘Veˇ cer’ it is highly probable that it
will be covered by ‘Dnevnik’.
6 Statistics and analysis
The statistical analysis of our dataset with regard to the dis-
tribution of events between the outlets is summarized and
visualized in this section.
Figure 3a and 3b represents the distribution of event cat-
egories covered by the English and the Slovenian news
outlets. It is evident from the distribution that each En-
glish news outlet focuses on a different event category other
than ‘Politics’. For instance, ‘Indiatimes’ focuses more on
events related to ‘Arts and Entertainment’, whereas ‘Chi-
nadaily’ tends to cover more ‘Business’ related events. In
contrast to English outlets, the event coverage by Slove-
nian outlets is similar in addition to ‘Politics’ focusing on
‘Sports’ and to some extent on ‘Business’.
By plotting the proportion of event coverage over time,
as shown in Figure 4, the pattern of event coverage by the
outlets can be better visualized. In particular, ‘May 2020’
contrasts the percentage of event coverage by the English
and Slovenian news outlets. Moreover, unlike other news
outlets, coverage of events by ‘Usatoday’, ‘Washington-
post’, and ‘Veˇ cer’ is somewhat inconsistent. A substantial
decline in the coverage of ‘Washingtonpost’ in ‘May2020’
is also noteworthy in the graph. It is due to its event prefer-
ence which is evident from its radial graph in Figure 3a. Its
coverage is skewed towards ‘Politics’ and ‘Sports’ which
alone represents around50% of events in the dataset. How-
ever, this percentage dropped to40% in ‘May2020’, and as

EveOut: an Event-centric News Dataset to. . . Informatica 45 (2021) 25–30 29
(a) English news outlets. (b) Slovenian news outlets.
Figure 3: Category-wise distribution of event coverage by the outlets. (Category ‘others’ includes: environment, health,
science, and technology )
Figure 4: Distribution of the percentage of event coverage by the news outlets over time.
a result, its coverage declined substantially. In a nutshell, if
the outlet favors a certain category of events and, in a spe-
cific time frame, events of that category are higher/lower
than usual, it would be reflected in the outlet’s coverage
pattern.
Figure 5 reflects the inclination of the news outlets to-
wards geographical bias which indicates that they prefer to
cover events relevant to the geographical area in which they
are based.
7 Conclusions and future work
In this paper, we presented a novel event-centric dataset
EveOut for the study and analysis of complex event-outlet
relationships. We also provide flexible data generation
scripts, to speed up the development of future versions of
EveOut. We also mentioned a potential use case to illus-
trate how the dataset could be used to study the event cov-
erage patterns of the outlet and to estimate the correlation
between the outlets using conditional probabilistic models.
We also conducted a statistical study to compare and
contrast five English and three Slovenian outlets to exam-
ine their event selection patterns. We found that ‘Politics’ is
the most popular category, while ‘Environment’ is the least
popular category covered by the outlets. We also identi-
fied that news outlets, as expected, tend to cover geograph-
ically relevant events. In particular, we discovered that if
the outlet favors a certain category of events and, if in a
specific time frame, events collected of that category are
higher/lower than usual, then this is reflected in the outlet’s
coverage pattern.
Although several features, such as event description,
have not been analyzed in our study, it is expected that these
features will also help to identify the inherent bias present
in the event selection process. We hope that our dataset will
not only help to discover and interpret event selection bias
but will also help researchers to develop tools to enhance
digital journalism.
Different news outlets may have different policies for se-
lecting events. For example, some news outlets may want
to publish only the top events of the day, while others may
want to include exclusive global events. As part of our fu-
ture work, an automated solution could be developed using

30 Informatica 45 (2021) 25–30 Swati et al.
(a) English news outlets.
(b) Slovenian news outlets.
Figure 5: Distribution of country-wise coverage of events
by the outlets. Notice the higher coverage density in (a)
The USA, India, and China (b) Slovenia.
EveOut to provide an overview of the event and to visualize
the differences in coverage, as it is important for journalists
to know which event is worthy of publication and which
factors influence the selection process.
In the future, it would also be interesting to have a distri-
bution of articles with positive and negative sentiment for
specific events and outlets. This would reveal not only the
outlet’s political orientation but also the editorial’s overall
attitude.
Acknowledgement
This work was supported by the Slovenian Research
Agency under the project J2-1736 Causalify and co-
financed by the Republic of Slovenia and the European
Union’s H2020 research and innovation program under the
Marie Skłodowska-Curie grant agreement No 812997.
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