A Text Similarity Study: Understanding How Differently Greek News

Media Describe News Events

Nikos Kapellas

and Sarantos Kapidakis

Department of Archival, Library and Information Studies, University of West Attica,

Ag. Spyridonos 28 12243, Athens, Greece

Keywords:

Greek News Media, News Events, News Articles, Similarity Analysis, Cosine Similarity.

Abstract:

Online news media usually cover the same news events. It would be interesting to understand, how similar are

the descriptions of these events. In other words to explore how a distinct news event, is described by different

news media. Can we safely conclude when two or more event descriptions refer to the same event? This

research aims to investigate similarities in Greek news articles and provide new data on the ﬁeld, as there is no

relevant research. To do so, news article’s text is extracted, processed and analyzed with automated methods.

To establish an understanding of similarity, three similarity settings are explored and special focus is given in

examining the degree of similarity between news articles that cover the same event. In some cases results are

inconclusive, while in others results show that groups of news media share the exact same articles with little or

no modiﬁcations at all. The similarity analysis of news articles is a complex task and relies on many different

factors that need to be addressed.

1 INTRODUCTION

In the era of social media and online news one ma-

jor issue is the degrading quality of the information

that is being shared. More than once fake news have

gone viral and biased authors, unintentionally spread

misinformation. News media controlled by different

interests, write articles aiming to convince individuals

on a certain topic.

Additionally, a phenomenon that is observed on a

daily basis, is that of duplicate or near duplicate ar-

ticles that circulate, among online newspapers. And

while it is easy to address this duplication task via

automated methods, it is difﬁcult for readers to un-

derstand how similar speciﬁc news articles are. Often

there are minor transformations to an original article:

information is added or removed, the title is altered,

a different source is credited, the structure is shufﬂed,

and different images are posted.

Initially we are not interested in proposing a novel

methodology for near duplicate detection, as the goal

of this study is to put similarity in context. Thus,

we construct three different controlled similarity set-

tings to explore. We aim in analyzing news articles in

Greek, as there are no relevant studies. Also, Greek

https://orcid.org/0000-0002-2767-3956

https://orcid.org/0000-0002-8723-0276

can be considered a low-resource language, in terms

of datasets and processing tools. This is part of the

motivation to research local news articles, instead of

using an English dataset for example. Moreover, the

monetization paradigm in this particular news media

landscape, relies primarily on ads. This fact, disables

the involved parties in producing original journalistic

material, as they struggle to keep up with competi-

tion; It is practically impossible for many online news

media to cover every national and global news event.

Thus, editors prefer to copy or plagiarise news articles

and make minor modiﬁcations (or no modiﬁcations at

all), and post them to their channels.

We assume that this can be problematic, since the

information contained in these articles is not always

cross-checked or validated. This publication prac-

tice allows many forms of misinformation to mani-

fest and to be shared with ease and in an incredible

speed. Readers on the other hand, are presented with

the same information, rather than different views and

details of a speciﬁc news event. By exploring the de-

gree of similarity between different news events, and

focusing on examining whether we can safely con-

clude if two or more event descriptions refer to the

same event, we aim in understanding how text simi-

larity is propagated.

In section 2, we are going to present selected re-

Kapellas, N. and Kapidakis, S.

A Text Similarity Study: Understanding How Differently Greek News Media Describe News Events.

DOI: 10.5220/0011589700003335

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD, pages 245-252

ISBN: 978-989-758-614-9; ISSN: 2184-3228

245

search that touches on the various subjects of our

topic and provide helpful terminology regarding our

study. In section 3, we explain the methodology fol-

lowed in creating three small news articles datasets

and the initial processing of this text corpus. In sec-

tion 4, we present the results obtained from the simi-

larity analysis and we present some additional infor-

mation from the clustering algorithms. In section 5,

we provide some thoughts and conclusion regarding

the overall workﬂow and future developments.

2 RELATED WORK

In literature an event is something that happened at

a speciﬁc time, in a speciﬁc place. There is a differ-

ence though between an event and a generic theme.

For example, the plane crash of the Ukraine Inter-

national Airlines Flight 752 is considered an event,

while “plane crashes” is a theme. It is observed that

“bursts” of news articles follow each event in the me-

dia (Yang et al., 1999).

At the same time, a big event may comprise

smaller ones. For example, the national elections in

Greece cover a broad range of other events or themes,

such as election campaigns, opinion polls, turnout of

voters, results and so on. In general, events are more

precise than themes since the factor of time is rather

crucial. Say, the different invasions of Afghanistan

by the Soviets and the US, are different events that

repeated over the years (Po et al., 2017).

Regarding the trustworthiness of online news, it is

proven that they are often biased, something that can

lead users to adopt biased opinions as well (Spinde

et al., 2021), (Hamborg et al., 2019). Another concern

is populism or the audience agenda; prior to online

news, editors had only a general idea regarding their

reading audience. Nowadays, editors have detailed

data, regarding the popularity of published articles.

This may lead to a journalistic culture where impor-

tant but non-appealing stories make space for news

that grab attention more easily (Bright and Nicholls,

2014).

In (Nicholls and Bright, 2018) authors propose

“news stories”, a second observational level for news

events. News stories are deﬁned as a collection of

articles that approach a speciﬁc theme from different

perspectives, or a collection of articles that contains

news articles on an event of continuous interest, the

initial news article, and its updates.

Another concerning phenomenon of the online

news industry, is the fact that if a news event makes

a headline to a big or international news media, it

is more likely to become a headline to smaller, local

news media as well. These smaller media will effec-

tively change their content to match the coverage of-

fered by prominent news media (Papadopoulou et al.,

2021).

New age journalism shifts focus from the old fash-

ioned editors-readers dynamic, to promoting the in-

volvement of people in the ﬁeld, ﬁnding new pub-

lishing procedures. Technology, ﬁnancial crisis and

distrust in news media, created circumstances for the

so-called “citizen journalism”. Since people are not

obliged to follow standard journalistic practices, it is

claimed, yet uncertain, that citizen journalism gave a

helping hand to the propagation of rumors, fake and

unveriﬁed news (Antonopoulos et al., 2020a), (Rubin

et al., 2015).

Greece’s online news industry is also steadily

changing. In the last 10 years, more than 50 news me-

dia, 9 radio stations and 3 TV channels have closed,

while 4 news groups have been sold or closed as well.

According to data, average circulation of newspapers

has dropped from around 220.000 in 2016 to 98.000

in 2015 (Leandros and Papadopoulou, 2020). It is

also noted that 46% of international media follows a

subscription model for giving access to their content,

while in Greece only 9% follows a similar practice

(Antonopoulos et al., 2020b).

In journalism, there is a wide range of practices:

transformations in prototype text, summaries, shrink-

ing and reinforcement, reconstruction of pieces of in-

formation to write a concise news article (Tenenboim-

Weinblatt and Baden, 2018).

News articles can be described by a set of terms

that represent the main event or theme within the doc-

ument. It is reasonable for documents that describe

relevant, similar or the same events, to contain com-

mon terms. Thus, to identify news events useful key

terms must be extracted from the text. However, as it

happens key terms lists are often dominated by non-

representative words, such as stop-words or corpus-

speciﬁc terms (Fan et al., 2017).

A basic attribute of news pages, is that they use a

common structure (template) to present content that

can potentially aid in automatically extracting text

(Varlamis et al., 2014). However in many occasions,

authors fail to declare the appropriate information in-

side the corresponding HTML tags, making the pro-

cess of identifying useful content harder (Hu et al.,

2005), (Mohammadzadeh et al., 2012).

There are two major practices in extracting text

from news pages: the ﬁrst approach uses sets of rules,

while the second uses parser that try to make an ed-

ucated guess regarding the location of a page’s con-

tent, extracting only speciﬁc parts (Ibrahim et al., ).

As (Yi et al., ) mentions noise on the web falls un-

KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development

246

der two categories, depending on the level of detail:

global noises a website, such as ads, navigation bars,

GDPR and copyright statements, of the web, that in-

clude duplicate or obsolete web pages and local noise,

that concern various parts of links to social media. As

mentioned, templates may prove useful for the extrac-

tion process. On the other hand, some argue that the

wide usage of templates is problematic.They have a

negative impact on the efﬁciency of tools related to

web pages processing and they represent 40-50% of

data on the web and this value increases by 6% each

year (Vieira et al., 2006).

For text, determining the similarity degree be-

tween words is the starting point for phrase, para-

graph, and document similarity. A similarity measure

based on a text corpus, quantiﬁes the similarity be-

tween words according to information obtained by big

corpora. A similarity measure based on knowledge,

quantiﬁes the degree of that similarity between words,

using information from semantic networks (H.Gomaa

and A. Fahmy, 2013), (Dhyani et al., 2002). This

came as a solution in the ever-growing volume of data

and information overload. The amount of information

users can handle given a speciﬁc time frame is lim-

ited, making it more difﬁcult to retrieve and access

information within their interest (Ahmed and Hanif,

2020).

There numerous applications were similarity anal-

ysis lies on their core. Recommendation systems are

helping users locate, ﬁlter information, and make de-

cisions, according to their needs, when there is no

prior knowledge to evaluate a product or a piece of in-

formation (Feng et al., 2020). Automatic summariza-

tion is also a research ﬁeld relevant to similarity anal-

ysis. The idea is that units of text, such as sentences,

can represent whole documents (Llewellyn et al., ).

Paraphrase identiﬁcation has similarity in its core as

well. Paraphrase identiﬁcation studies in which ways

systems can identify alternative linguistic expressions

of the same meaning, in different texts and levels

(documents, paragraphs, sentences) (AL-Smadi et al.,

2017). Classiﬁcation applications contain the elimi-

nation of duplicates from databases, spam email ﬁlter-

ing, plagiarism detection, among other (Soloshenko

et al., 2015). Classiﬁcation may be supervised or

unsupervised. In the ﬁrst case, prior knowledge is

needed on the data, while in unsupervised there is no

such need (Prasad et al., 2018). In general, deﬁning a

pair of similar items is not always clear. In news arti-

cles, research is often focused on classifying the con-

tent into categories such as politics, economy, sports

(Huang, 2008). Speciﬁcally for text, since the num-

ber and composition of the elements involved are not

well deﬁned, documents are considered unstructured

data. Therefore, a structure is applied by using a text

representation method (Mozer et al., 2020).

2.1 Terminology

To avoid confusion, some basic terminology is pro-

vided in the context of this research. An event, that is

a news event, can be of the following three categories:

same event (news describing the same event), related

event (news describing events that have something in

common, ex. a common theme), and unrelated event

(news describing different events).

Also, regarding the similarity of news news arti-

cles, there are 4 distinct categories: articles can be

identical, similar, dissimilar or discrete. Moreover,

articles can be similar, if they are near-duplicates, re-

duced (containing less information) and modiﬁed (ad-

ditional changes in text and structure). discrete arti-

cles, concern only related event and unrelated event

news, while the rest categories (identical, similar and

dissimilar) concern same event news.

The term news media is referring to news sites,

primarily online newspapers. Lastly, in some cases,

the terms documents and news articles are used to de-

note the same thing.

In the following section, the rationale of formulat-

ing the methodology is explained.

3 METHODOLOGY

3.1 Creating the News Articles Datasets

To better describe the similarity between articles,

three different data sets are formed by extracting text

from news media sites. Each set contains 24 news ar-

ticles from 24 different news media; one news article

from each news media website. All of the articles in-

cluded in the three mentioned sets were selected and

checked manually. The ﬁrst set contains unrelated

event news articles. These belong to various cate-

gories (sports, politics, economy) and each presents

an event that has no connections with any other event

within the set. The second set contains related event

news articles that are retrieved based on the common

theme “accident”, but report on different events; that

is, articles that present accidents that happened in dif-

ferent places or time, involving different actors. The

third set contains same event news articles, all report-

ing on a speciﬁc event that was published by different

media.

This distinct event is related to the economy of

Greece. Speciﬁcally, the story is about how the Eu-

ropean leaders agreed to support Greece by providing

A Text Similarity Study: Understanding How Differently Greek News Media Describe News Events

247

ﬁnancial aid, due to the economic recession caused

by the pandemic of COVID-19. In total, the en-

tire dataset numbers 72 news documents. The arti-

cle selection process was done manually, by visiting

the news media websites and inspecting their content.

Later, the article’s text was extracted and stored into

separate documents, one for each article.

The objective here is threefold: First, to estab-

lish a quantiﬁed meaning of similarity, by exploring

similarity limits for each set. Second, to compare

similarity observations among sets, to better under-

stand how similarity changes according to the differ-

ent event categories. Third, to speciﬁcally examine

the range of similarities between articles of the third

set, containing news articles of the same event and

evaluate those ﬁndings.

The underlying goal is to understand how differ-

ently the media describe the news events. It is outside

of the scope of this study to provide a competitive

methodology for text extraction or a novel duplicate

detection algorithm. To analyze the similarity news

articles, that describe the same event, is a low starting

point for a task as such.

In the following section text processing is de-

scribed.

3.2 How to Perceive Similarity

There are several ways to represent a text document.

Here the three distinct sets of documents are trans-

formed into TF-IDF matrices. Each word represents

a dimension in the resulting space and each document

becomes a vector. Term frequency is used as a weight,

meaning that the terms that appear more frequently

are more descriptive for the document. In practice

though, this is not always true. Words such as ar-

ticles, stop-words, and function words, are not that

important for the document subject. With documents

as vectors, the degree of similarity between two doc-

uments is measured as the correlation between their

assigned vectors. This can be quantiﬁed as the cosine

of the angle between those vectors, as it can be seen

in 1.

Figure 1: Angle between documents.

To remove stop-words from the corpus, the text

was tokenized into words, annotated with part-of-

speech (POS) information, and lemmatized. This way

each document is transformed into a list of triplets.

Each row contains the initial term, its POS tag based

on its grammatical category, and its word stem. For

example the triplet: banks NoCmFePlNm bank. In

some cases, words were miss-tokenized, and their

lemma was marked as unknown. These words were

removed from the corpus as well, since they would

map into one another, falsely increasing similarity re-

sults. Three grammatical categories were qualiﬁed

as valuable for the scope of our research. These

are nouns, adjectives, and verbs. Each of the three

categories, has several subcategories specifying their

type, such as proper nouns, superlative adjectives,

past tense verbs and so on. Words with POS tags other

than (No) for nouns, (Aj) for adjectives, and (Vb) for

verbs, were removed from each document. To fur-

ther improve accuracy, too infrequent words were re-

moved from the corpus. Terms that appeared in a

few documents for each set, were ignored by setting a

minimum threshold to term document frequency.

To further understand how similar the news ar-

ticles of the third set are, k-means and dendrogram

clustering algorithms are applied. Both have a com-

mon application in this case, since they provide a view

of the data at different level of abstraction and gran-

ularity. For k-means clustering, to determine an ap-

propriate number of clusters, the Elbow method was

used. As Fig. 2 shows, the distortion value de-

creases at a steady rate for our data, creating a rather

smooth curve, signifying that this method is inconclu-

sive. Since the elbow-point could not be identiﬁed,

after some trial and error attempts, the k was empiri-

cally set to k=4.

Results for all three similarity settings are pre-

sented in the following section, along with the eval-

uation of these results and the clustering algorithms

outputs.

Figure 2: The elbow method showing the optimal k.

4 RESULTS

4.1 Unrelated and Related News

Articles Similarity Limits

The similarity analysis between news articles of the

ﬁrst set, that of unrelated event news articles, revealed

KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development

248

that there are 48 article pairs with similarity score

above 0.384 (similarity threshold), while 2 observa-

tions are equal to 0.03 (minimum) and another 2 are

equal to 0.61 (maximum).

For the second set, that contains related event

news, drawn from the common news theme ”acci-

dent”, there are 67 observations above 0.384 and the

lowest similarity observed is equal to 0.04. On the

other hand, there are 16 observations between 0.5-

0.56 (maximum).

In contrast, in the third set, that of same event

news, there are 20 pairings of high similarity scores,

ranging from 0.69 to 0.91, 87 similarity values from

0.50 to 0.69, whereas the rest pairs have scores from

0.49 to 0.15 (lowest).

These results indicate that for the ﬁrst two sets, the

similarity minimum and maximum are approximately

the same, even though the number of news articles

above the similarity threshold differs.

The similarity minimum of these two sets is of

different order, compared to the minimum value of

the third set. Accordingly, the maximum value of the

third set, is signiﬁcantly higher than the maximum of

the two other sets.

Also, the similarity limits of the ﬁrst two sets,

were expected to increase or decrease more gradu-

ally. The similarity maximum of the unrelated event

news set (0.61), was expected to be lower. Likewise,

the similarity minimum of the related event news set

(0.04), was expected to be higher.

In the third set, that of same event news articles,

the similarity limits differ substantially, observing a

higher minimum and maximum, while many values

range well-above the similarity threshold.

Based on the above, a context of similarity is be-

ginning to emerge, even though not all observations

follow reasonable patterns. By measuring similar-

ity, an understanding of what similar means can be

formed, to evaluate the descriptions for the different

categories of news events.

4.2 Same Event News Articles

Similarity Analysis

Special focus is given on the same event news set.

In it, some news articles appear identical or similar

to one another, while others seem to be dissimilar,

which is reasonable even though they describe the

same event.

In Fig. 3 we can see the document similarity ma-

trix of the same event news set, with a color scale on

the right, indicating the degree of that similarity, with

1 being similar (close to yellow) and 0 being differ-

ent (close to blue). In both x, y axis there are labels

for each one of the 24 documents. Each document

is paired with every other document of the dataset,

forming a matrix of 576 squares (288 pairings). The

observations running the matrix diagonally, represent

the same document pairings.

Figure 3: Pair-wise similarity matrix.

Table 1 shows the highest similarity score val-

ues. Columns Document A and Document B repre-

sent pairs of documents and cosine similarity is rep-

resented as the similarity score. Based on the results,

document 0 and document 3 could be classiﬁed as

identical with a score of 0.91, the pairs of documents

0, 15 and 15, 3 are near duplicates with scores of 0.82

and 0.78 accordingly, while document 14 is 0.72 re-

duced comparing to document 2, and so on and so

forth.

Table 1 indicates that there are two groups of iden-

tical or similar documents, within the set of articles.

The ﬁrst consists of documents 0, 3, 15, whereas

the second consists of documents 14, 2, 7, 9, 16,

18. However, not all documents of the second group

are equally similar to each other. For example, even

though the pair of documents 7, 16 and 16, 18 are

highly similar, their intersection pair of documents 7

and 18, has a lower similarity score of 0.65. More-

over, out of the 276 similarity pairings (excluding the

24 same document pairings), 20 pairings have high

similarity scores, from 0.69 to 0.91 (highest), 87 have

similarity scores from 0.50 to 0.69, whereas the rest

pairs have scores from 0.49 to 0.15 (lowest). Inter-

estingly enough, document 19, taken from the for-

eign news agency Reuters.com and was automatically

translated from English to Greek, is similar to a de-

gree of 0.50 or greater, with documents 2, 7, 9, 14, 16

and 18.

A Text Similarity Study: Understanding How Differently Greek News Media Describe News Events

249

Table 1: Highest similarity score pairings.

Document A Document B Similarity score

0 3 0.91

0 15 0.82

15 3 0.78

14 2 0.72

7 9 0.72

7 14 0.76

7 16 0.70

9 16 0.76

14 9 0.76

14 18 0.76

16 18 0.73

4.3 Evaluation and Clustering

Regarding the evaluation of the methodology fol-

lowed, the observed similarity scores were matched

against a similarity threshold. That threshold was pro-

duced based on the average over all similarities ob-

served. This cut-off value expresses the degree of

similarity that two documents must have to be con-

sidered as similar. The formula for calculating that a

threshold derives from the physical properties of its

parts:

T = avg(sim) + a · s(sim)

where a is a parameter and s is the standard devi-

ation of the similarity values.

The avg(sim) = 0.246, is the average over all sim-

ilarity pairs within the dataset and to distance the

T value from that average, parameter a was set to,

a = 0.75 that is equal to 1.5 standard deviations. The

calculated value T = 0.384, seems reasonable when

examining the pairwise similarity scores. In other

words, document pairs with similarity equal or greater

to 0.384 qualify as identical or similar, while pairs

with a score below 0.384 appear to be dissimilar or

discrete, depending on the set. The similarity ﬁndings

reveal 122 observations with a score over 0.384.

Fig. 4 shows how documents of the set are clus-

tered by the k-means algorithm. Each point on the

diagram represents a different document. Colors are

used to indicate groups and grey circles represent the

center of each cluster. The clustering reveals 4 sepa-

rate clusters. Documents 0, 3, 15 form the ﬁrst cluster,

documents 5, 10, 13, 20, 21, 22 form a second clus-

ter, documents 4, 6, 8, 11, 12, 23 form a third cluster,

whereas documents 1, 2, 7, 9, 14, 16, 17, 18, 19 form

the biggest cluster of similar documents.

Fig. 5 shows how the dendrogram algorithm clus-

tered the news articles. Horizontally the 24 docu-

ments and their clusters are found, colored accord-

Figure 4: Scatter plot of k-means clustering.

Figure 5: Dendrogram of the hierarchical clustering.

ingly and vertically the distance or dissimilarity be-

tween those clusters is shown. Each joining of the

two clusters is represented on the graph by the split

of one vertical line in two. The position of the split

is shown by the horizontal bar, which also gives the

distance between the two clusters. As expected, doc-

uments 0, 3, 15 form a cluster. Documents 1, 2, 7, 9,

14, 17, 18, 19 form a second cluster. Documents 4, 6,

11, 23 form a third cluster and documents 5, 10, 20,

21, 22 form a fourth one.

An overview of the results suggest that there is

a certain pattern that follows the publication of the

same event news. Even though it is reasonable to ex-

pect high similarity between text presenting a speciﬁc

event, it can be safely assumed that certain news me-

dia shared the exact same text. These small groups

of media may have based their articles in information

drawn from the same sources. Surely, one of them

posted the article ﬁrst and the others followed, by sim-

ply copying the article. It is not certain regarding who

copied from who, or if the credited sources are the

true sources of the articles text.

The observations represent only an instance of a

practice, that it is believed to be widely followed by

many news media. Considering the impact this prac-

tice might have on quality news, the misinformation

KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development

250

pitfall here is twofold: some pieces of information can

be circulated intact to avoid miscommunicating them

to readers. But what happens if the source of the arti-

cle failed to validate facts or if the author of the arti-

cle is biased? Surely the news media would not want

their brand name to be harmed, by transferring mis-

informative articles. Readers on the other hand, can

easily fall for propaganda or different forms of fake

news, something that can greatly affect social coher-

ence.

5 DISCUSSION AND FUTURE

WORK

In this research the degree of similarity between news

articles, published by different news media, was ex-

plored. In other words, we tried to inspect how dif-

ferently online news media describe news events. To

establish a quantiﬁed understanding of that similarity,

three different datasets were created. The ﬁrst con-

tains unrelated event news, the second related event

news, while the third, same event news articles.

The underlying goal of this task, is to deﬁne sim-

ilarity limits for each set, to be able to put ﬁndings

in context and ﬁnally compare them. Special focus

is given in examining the similarity, between same

events news articles. Even though results are diverse,

in some cases they support our argument; high simi-

larity scores reveal that many news articles, are iden-

tical and/or similar.

This fact suggests that news media fail to inves-

tigate events and describe them in an original way.

Instead, groups of media are formed that circulate

nearly identical piece of information to their readers.

The methodology followed can surely be im-

proved. Even though tﬁdf and cosine similarity per-

form well, there are numerous representations and

measurement algorithms to consider for feature tasks.

Also, we would like expand this study by collecting

more data, and including additional factors such as

topic models, named entities, and comparison with

other languages. This research contributes in current

literature as there is no relevant research, that exam-

ines the similarities of news articles in Greek, which

is a low-resource language.

ACKNOWLEDGEMENTS

This research is supported by the University of West

Attica.

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