@article{mir_public_2022,
	title = {Public perception of {COVID}-19 vaccines from the digital footprints left on {Twitter}: analyzing \textit{positive} , \textit{neutral} and \textit{negative} sentiments of {Twitterati}},
	volume = {40},
	issn = {0737-8831},
	shorttitle = {Public perception of {COVID}-19 vaccines from the digital footprints left on {Twitter}},
	url = {https://www.emerald.com/insight/content/doi/10.1108/LHT-08-2021-0261/full/html},
	doi = {10.1108/LHT-08-2021-0261},
	abstract = {Purpose
              Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.
            
            
              Design/methodology/approach
              
                To fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “
                Twitter Application Programming Interface
                ” (API), using “
                Orange Software
                ”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “
                Orange Software
                ” were applied to the dataset, which include “
                transformation
                ”, “
                tokenization
                ” and “
                filtering
                ”. The “
                Valence Aware Dictionary for sEntiment Reasoning
                ”
                (VADER)
                tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.
              
            
            
              Findings
              A gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52\%) of tweets express positive sentiments, followed by neutral (7,948; 33.71\%) and negative sentiments (4,422; 18.75\%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91\%) and likes (84.62\%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.
            
            
              Research limitations/implications
              The main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.
            
            
              Practical implications
              The study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.
            
            
              Originality/value
              The paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.},
	language = {en},
	number = {2},
	urldate = {2023-05-03},
	journal = {Library Hi Tech},
	author = {Mir, Aasif Ahmad and Rathinam, Sevukan and Gul, Sumeer},
	month = mar,
	year = {2022},
	pages = {340--356},
}

Purpose Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles. Design/methodology/approach To fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “ Twitter Application Programming Interface ” (API), using “ Orange Software ”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “ Orange Software ” were applied to the dataset, which include “ transformation ”, “ tokenization ” and “ filtering ”. The “ Valence Aware Dictionary for sEntiment Reasoning ” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used. Findings A gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments. Research limitations/implications The main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied. Practical implications The study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines. Originality/value The paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

@article{nirenberg_dostoevsky_2022,
	title = {Dostoevsky and the {Word} “{Jew}”: {A} {Quantitative} {Analysis} of {F}.{M}. {Dostoevsky}’s {Greatest} {Novels}},
	volume = {72},
	issn = {0024-2667, 1865-8423},
	shorttitle = {Dostoevsky and the {Word} “{Jew}”},
	url = {https://www.degruyter.com/document/doi/10.1515/libri-2021-0011/html},
	doi = {10.1515/libri-2021-0011},
	abstract = {Abstract
            The relationship of F.M Dostoevsky with Jews attracted the attention of numerous scholars throughout the years, many of whom attempted to grapple with the views of the great writer and their origin. In this article we will attempt to show this relationship by analyzing six of Dostoevsky’s greatest novels, written through the entirety of his career. We are analyzing these novels using Distant Reading in conjunction with Close Reading, tools that are commonly used in the field of digital humanities, which enabled us to show visually the extent of F.M. Dostoevsky’s engagement with this topic. The study poses two research questions: 1. To what extent did the writer use the more denigrating term “Zhid”? 2. Can we see a correlation between the writer’s portrayal of Jews with the definition of Anti-Semitism as it was known during his era? The obtained results show that there is clearly a correlation between the definition of anti-Semitism as it was understood at the time of Dostoevsky and the “Jew” as depicted in his novels, as the financial motif is paramount in the depiction of Jews as this is the central topic in 49\% of the negative sentences in which the word “Jew” appears, with 59\% of these sentences classified as stereotypes. The negative financial stereotype constitutes 32\% of the entire corpus. In addition, we found the term “Zhid” is commonly used by the writer, a variation of which constitutes 75\% of the total terms used to depict Jews.},
	language = {en},
	number = {1},
	urldate = {2023-05-03},
	journal = {Libri},
	author = {Nirenberg, Yigal and Prebor, Gila},
	month = mar,
	year = {2022},
	pages = {83--96},
}

Abstract The relationship of F.M Dostoevsky with Jews attracted the attention of numerous scholars throughout the years, many of whom attempted to grapple with the views of the great writer and their origin. In this article we will attempt to show this relationship by analyzing six of Dostoevsky’s greatest novels, written through the entirety of his career. We are analyzing these novels using Distant Reading in conjunction with Close Reading, tools that are commonly used in the field of digital humanities, which enabled us to show visually the extent of F.M. Dostoevsky’s engagement with this topic. The study poses two research questions: 1. To what extent did the writer use the more denigrating term “Zhid”? 2. Can we see a correlation between the writer’s portrayal of Jews with the definition of Anti-Semitism as it was known during his era? The obtained results show that there is clearly a correlation between the definition of anti-Semitism as it was understood at the time of Dostoevsky and the “Jew” as depicted in his novels, as the financial motif is paramount in the depiction of Jews as this is the central topic in 49% of the negative sentences in which the word “Jew” appears, with 59% of these sentences classified as stereotypes. The negative financial stereotype constitutes 32% of the entire corpus. In addition, we found the term “Zhid” is commonly used by the writer, a variation of which constitutes 75% of the total terms used to depict Jews.

@article{storey_like_2020,
	title = {Like {Two} {Pis} in a {Pod}: {Author} {Similarity} {Across} {Time} in the {Ancient} {Greek} {Corpus}},
	issn = {2371-4549},
	shorttitle = {Like {Two} {Pis} in a {Pod}},
	url = {https://culturalanalytics.org/article/13680-like-two-pis-in-a-pod-author-similarity-across-time-in-the-ancient-greek-corpus},
	doi = {10.22148/001c.13680},
	language = {en},
	urldate = {2021-07-12},
	journal = {Journal of Cultural Analytics},
	author = {Storey, Grant and Mimno, David},
	month = jul,
	year = {2020},
}

@article{horvath_vershangzas_2020,
	title = {A vershangzás jellemzőinek automatikus feltárása {József} {Attila} verseiben},
	volume = {3},
	issn = {2630-9696},
	url = {http://ojs.elte.hu/digitalisbolcseszet/article/view/422},
	doi = {10.31400/dh-hun.2020.3.422},
	abstract = {A tanulmány József Attila versei alapján mutatja be a vershangzáshoz kapcsolódó tulajdonságok automatikus feltárásának egy módszerét. A tanulmány első fele ismerteti a vershangzás jellemzőinek automatikus annotálására létrehozott hunpoem\_analyzer-TEI program funkcióit, valamint az annotációs folyamat főbb lépéseit. A tanulmány második fele különböző, az elemzett korpuszból kinyert, a versek lexikai tulajdonságaihoz és a vershangzás jellemzőihez kapcsolódó gyakorisági listákat mutat be.},
	urldate = {2021-03-03},
	journal = {Digitális Bölcsészet},
	author = {Horváth, Péter},
	month = jul,
	year = {2020},
}

A tanulmány József Attila versei alapján mutatja be a vershangzáshoz kapcsolódó tulajdonságok automatikus feltárásának egy módszerét. A tanulmány első fele ismerteti a vershangzás jellemzőinek automatikus annotálására létrehozott hunpoem_analyzer-TEI program funkcióit, valamint az annotációs folyamat főbb lépéseit. A tanulmány második fele különböző, az elemzett korpuszból kinyert, a versek lexikai tulajdonságaihoz és a vershangzás jellemzőihez kapcsolódó gyakorisági listákat mutat be.

@book{berend_xvi_2020,
	address = {Szeged},
	title = {{XVI}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}: {MSZNY} : {Szeged}, 2020. január 23-24.},
	isbn = {978-963-306-719-2},
	shorttitle = {{XVI}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}},
	language = {Tanulmányok váltakozva magyar és angol nyelven},
	publisher = {Szegedi Tudományegyetem TTIK Informatikai Intézet},
	editor = {Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika and {Szegedi Tudományegyetem} and {Természettudományi és Informatikai Kar} and {Informatikai Intézet} and {Magyar Tudományos Akadémia (Budapest) Szegedi Tudományegyetem} and {Mesterséges Intelligencia Kutatócsoport} and {Magyar Számítógépes Nyelvészeti Konferencia}},
	year = {2020},
	note = {OCLC: 1227883790},
}

@inproceedings{qader_overview_2019,
	address = {Erbil, Iraq},
	title = {An {Overview} of {Bag} of {Words};{Importance}, {Implementation}, {Applications}, and {Challenges}},
	isbn = {978-1-72814-377-4},
	url = {https://ieeexplore.ieee.org/document/8950616/},
	doi = {10.1109/IEC47844.2019.8950616},
	urldate = {2023-06-06},
	booktitle = {2019 {International} {Engineering} {Conference} ({IEC})},
	publisher = {IEEE},
	author = {Qader, Wisam A. and Ameen, Musa M. and Ahmed, Bilal I.},
	month = jun,
	year = {2019},
	pages = {200--204},
}

@inproceedings{indig_one_2019,
	address = {Florence, Italy},
	title = {One format to rule them all – {The} emtsv pipeline for {Hungarian}},
	url = {https://www.aclweb.org/anthology/W19-4018},
	doi = {10.18653/v1/W19-4018},
	language = {en},
	urldate = {2020-08-05},
	booktitle = {Proceedings of the 13th {Linguistic} {Annotation} {Workshop}},
	publisher = {Association for Computational Linguistics},
	author = {Indig, Balázs and Sass, Bálint and Simon, Eszter and Mittelholcz, Iván and Vadász, Noémi and Makrai, Márton},
	year = {2019},
	pages = {155--165},
}

@article{kiss_stilometriai_2019,
	title = {Stilometriai elemzés lehetőségei magyar történeti szövegkorpuszon},
	volume = {2},
	issn = {2630-9696},
	url = {http://ojs.elte.hu/index.php/digitalisbolcseszet/article/view/336/303},
	journal = {DIGITÁLIS BÖLCSÉSZET},
	author = {Kiss, Margit},
	year = {2019},
	pages = {15--33},
}

@article{gomez_adorno_stylometry-based_2018,
	title = {Stylometry-based {Approach} for {Detecting} {Writing} {Style} {Changes} in {Literary} {Texts}},
	volume = {22},
	issn = {2007-9737, 1405-5546},
	url = {http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2882},
	doi = {10.13053/cys-22-1-2882},
	number = {1},
	urldate = {2021-07-12},
	journal = {Computación y Sistemas},
	author = {Gomez Adorno, Helena Montserrat and Rios, Germán and Posadas Durán, Juan Pablo and Sidorov, Grigori and Sierra, Gerardo},
	month = mar,
	year = {2018},
}

@article{gergely_az_2018,
	title = {Az olvasó gép},
	volume = {1},
	issn = {2630-9696},
	url = {http://ojs.elte.hu/index.php/digitalisbolcseszet/article/view/126},
	doi = {10.31400/dh-hun.2018.1.126},
	abstract = {Tanulmányom helyzetfelmérés és kísérlet. Az érdekel, a rendelkezésre álló magyar nyelvi számítógépes elemzőkkel és statisztikai programokkal lehetséges-e a Berzsenyi-versek csoportosításával kapcsolatos, a szoros olvasás révén felmerült hipotézist igazolni, illetve meg lehet-e válaszolni olyan, Berzsenyi hangzóelméleti- és -használati gyakorlatát illető kérdéseket, amelyeket szoros olvasással nyilvánvalóan nem lehetne, emellett bemutatok egy lehetséges elemzési módszert, amely egyes fogalmak felbukkanását, eloszlását és összefüggéseit kutatja. Olyan belső kapcsolatokat, struktúrákat tárhatunk fel, illetve olyan modelleket, törvényszerűségeket állapíthatunk meg, amelyek a szoros olvasás révén egyszerűen nem állnának rendelkezésünkre. Egy olyan szemléletmódról és módszertanról van tehát szó, amely korábban elképzelhetetlen mértékű kontextualizációt tesz lehetővé, amely az egyedi szövegek környezetének, az egyes szövegeket meghatározó irodalmi, társadalmi, kulturális trendeknek az azonosítását, elemzését ígéri: ez a történeti és stilisztikai kérdéseknél egyértelmű előnnyel rendelkezik a szoros olvasással szemben.},
	urldate = {2021-03-03},
	journal = {Digitális Bölcsészet},
	author = {Gergely, Labádi},
	month = jun,
	year = {2018},
	pages = {17},
}

Tanulmányom helyzetfelmérés és kísérlet. Az érdekel, a rendelkezésre álló magyar nyelvi számítógépes elemzőkkel és statisztikai programokkal lehetséges-e a Berzsenyi-versek csoportosításával kapcsolatos, a szoros olvasás révén felmerült hipotézist igazolni, illetve meg lehet-e válaszolni olyan, Berzsenyi hangzóelméleti- és -használati gyakorlatát illető kérdéseket, amelyeket szoros olvasással nyilvánvalóan nem lehetne, emellett bemutatok egy lehetséges elemzési módszert, amely egyes fogalmak felbukkanását, eloszlását és összefüggéseit kutatja. Olyan belső kapcsolatokat, struktúrákat tárhatunk fel, illetve olyan modelleket, törvényszerűségeket állapíthatunk meg, amelyek a szoros olvasás révén egyszerűen nem állnának rendelkezésünkre. Egy olyan szemléletmódról és módszertanról van tehát szó, amely korábban elképzelhetetlen mértékű kontextualizációt tesz lehetővé, amely az egyedi szövegek környezetének, az egyes szövegeket meghatározó irodalmi, társadalmi, kulturális trendeknek az azonosítását, elemzését ígéri: ez a történeti és stilisztikai kérdéseknél egyértelmű előnnyel rendelkezik a szoros olvasással szemben.

@inproceedings{dobi_shtylo_2018,
	title = {Shtylo: stilometriai elemzések webes támogatása},
	url = {http://acta.bibl.u-szeged.hu/59065/1/msznykonf_014_423-436.pdf},
	booktitle = {{XIV}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia} : {MSZNY} 2018},
	author = {Dobi, Jan Sándor and Mészáros, Tamás and Kiss, Margit},
	year = {2018},
	pages = {423--436},
}

@book{vincze_xiv_2018,
	address = {Szeged},
	title = {{XIV}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}: {MSZNY} 2018 : {Szeged}, 2018. január 18-19.},
	isbn = {978-963-306-578-5},
	shorttitle = {{XIV}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}},
	url = {https://rgai.inf.u-szeged.hu/sites/rgai.sed.hu/files/teljesB5.pdf},
	language = {A tanulmányok magyar és angol nyelven váltakozva},
	publisher = {Szegedi Tudományegyetem Informatikai Intézet},
	author = {Vincze, Veronika and {Szegedi Tudományegyetem} and {Természettudományi és Informatikai Kar} and {Informatikai Intézet} and {Magyar Számítógépes Nyelvészeti konferencia}},
	year = {2018},
	note = {OCLC: 1132614333},
}

@inproceedings{stanikknas_comparison_2017,
	title = {Comparison of distance and similarity measures for stylometric analysis of {Lithuanian} texts},
	author = {Stanikknas, Daumantas and Mandravickaite, J. and Krilavicius, Tomas},
	year = {2017},
}

@article{evert_understanding_2017,
	title = {Understanding and explaining {Delta} measures for authorship attribution},
	volume = {32},
	issn = {2055-7671, 2055-768X},
	url = {http://academic.oup.com/dsh/article/32/suppl_2/ii4/3865676},
	doi = {10.1093/llc/fqx023},
	language = {en},
	number = {suppl\_2},
	urldate = {2021-07-12},
	journal = {Digital Scholarship in the Humanities},
	author = {Evert, Stefan and Proisl, Thomas and Jannidis, Fotis and Reger, Isabella and Pielström, Steffen and Schöch, Christof and Vitt, Thorsten},
	month = dec,
	year = {2017},
	pages = {ii4--ii16},
}

@article{eder_open_2017,
	title = {An open stylometric system based on multilevel text analysis},
	issn = {2392-2397},
	url = {https://ispan.waw.pl/journals/index.php/cs-ec/article/view/cs.1430},
	doi = {10.11649/cs.1430},
	abstract = {An open stylometric system based on multilevel text analysisStylometric techniques are usually applied to a limited number of typical tasks, such as authorship attribution, genre analysis, or gender studies. However, they could be applied to several tasks beyond this canonical set, if only stylometric tools were more accessible to users from different areas of the humanities and social sciences. This paper presents a general idea, followed by a fully functional prototype of an open stylometric system that facilitates its wide use through to two aspects: technical and research flexibility. The system relies on a server installation combined with a web-based user interface. This frees the user from the necessity of installing any additional software. At the same time, the system offers a variety of ways in which the input texts can be analysed: they include not only the usual lexical level, but also deep-level linguistic features. This enables a range of possible applications, from typical stylometric tasks to the semantic analysis of text documents. The internal architecture of the system relies on several well-known software packages: a collection of language tools (for text pre-processing), Stylo (for stylometric analysis) and Cluto (for text clustering). The paper presents: (1) The idea behind the system from the user’s perspective. (2) The architecture of the system, with a focus on data processing. (3) Features for text description. (4) The use of analytical systems such as Stylo and Cluto. The presentation is illustrated with example applications. Otwarty system stylometryczny wykorzystujący wielopoziomową analizę języka Zastosowania metod stylometrycznych na ogół ograniczają się do kilku typowych problemów badawczych, takich jak atrybucja autorska, styl gatunków literackich czy studia nad zróżnicowaniem stylistycznym kobiet i mężczyzn. Z pewnością dałoby się je z powodzeniem zastosować również do wielu innych problemów klasyfikacji tekstów, gdyby tylko owe metody oraz odpowiednie narzędzia były bardziej dostępne dla uczonych reprezentujących różne dyscypliny nauk humanistycznych i społecznych. Artykuł niniejszy omawia założenia teoretyczne oraz w pełni funkcjonalny prototyp otwartego systemu stylometrycznego, którego szerokie zastosowanie umożliwią dwie jego cechy: elastyczność techniczna oraz dostosowywalność do różnych pytań badawczych. System opiera się na instalacji serwerowej sprzęgniętej z sieciowym interfejsem użytkownika. Uwalnia to użytkownika od konieczności instalowania jakichkolwiek dodatkowych programów. Jednocześnie system oferuje wiele sposobów analizowania tekstów nie tylko na poziomie leksykalnym, lecz także poprzez cechy językowe niskiego poziomu. Daje to możliwość stosowania systemu na wiele różnych sposobów, od typowych testów stylometrycznych do analizy semantycznej dokumentów. Wewnętrzna architektura systemu składa się z wielu elementów znanych ze swej funkcjonalności, w tym z pakietu Stylo przeznaczonego do analiz stylometrycznych oraz pakietu Cluto służącego do zaawansowanej analizy skupień. Artykuł omawia: (1) Koncepcję całego systemu, postrzeganą z punktu widzenia użytkownika, (2) Architekturę systemu oraz jego elementy odpowiedzialne za przetwarzanie tekstu, (3) Cechy językowe służące do opisu dokumentów, (4) Zastosowanie modułów analizy danych, takich jak Stylo czy Cluto. W artykule zostały też przedstawione przykładowe zastosowania systemu.},
	number = {17},
	urldate = {2020-10-20},
	journal = {Cognitive Studies {\textbar} Études cognitives},
	author = {Eder, Maciej and Piasecki, Maciej and Walkowiak, Tomasz},
	month = dec,
	year = {2017},
}

An open stylometric system based on multilevel text analysisStylometric techniques are usually applied to a limited number of typical tasks, such as authorship attribution, genre analysis, or gender studies. However, they could be applied to several tasks beyond this canonical set, if only stylometric tools were more accessible to users from different areas of the humanities and social sciences. This paper presents a general idea, followed by a fully functional prototype of an open stylometric system that facilitates its wide use through to two aspects: technical and research flexibility. The system relies on a server installation combined with a web-based user interface. This frees the user from the necessity of installing any additional software. At the same time, the system offers a variety of ways in which the input texts can be analysed: they include not only the usual lexical level, but also deep-level linguistic features. This enables a range of possible applications, from typical stylometric tasks to the semantic analysis of text documents. The internal architecture of the system relies on several well-known software packages: a collection of language tools (for text pre-processing), Stylo (for stylometric analysis) and Cluto (for text clustering). The paper presents: (1) The idea behind the system from the user’s perspective. (2) The architecture of the system, with a focus on data processing. (3) Features for text description. (4) The use of analytical systems such as Stylo and Cluto. The presentation is illustrated with example applications. Otwarty system stylometryczny wykorzystujący wielopoziomową analizę języka Zastosowania metod stylometrycznych na ogół ograniczają się do kilku typowych problemów badawczych, takich jak atrybucja autorska, styl gatunków literackich czy studia nad zróżnicowaniem stylistycznym kobiet i mężczyzn. Z pewnością dałoby się je z powodzeniem zastosować również do wielu innych problemów klasyfikacji tekstów, gdyby tylko owe metody oraz odpowiednie narzędzia były bardziej dostępne dla uczonych reprezentujących różne dyscypliny nauk humanistycznych i społecznych. Artykuł niniejszy omawia założenia teoretyczne oraz w pełni funkcjonalny prototyp otwartego systemu stylometrycznego, którego szerokie zastosowanie umożliwią dwie jego cechy: elastyczność techniczna oraz dostosowywalność do różnych pytań badawczych. System opiera się na instalacji serwerowej sprzęgniętej z sieciowym interfejsem użytkownika. Uwalnia to użytkownika od konieczności instalowania jakichkolwiek dodatkowych programów. Jednocześnie system oferuje wiele sposobów analizowania tekstów nie tylko na poziomie leksykalnym, lecz także poprzez cechy językowe niskiego poziomu. Daje to możliwość stosowania systemu na wiele różnych sposobów, od typowych testów stylometrycznych do analizy semantycznej dokumentów. Wewnętrzna architektura systemu składa się z wielu elementów znanych ze swej funkcjonalności, w tym z pakietu Stylo przeznaczonego do analiz stylometrycznych oraz pakietu Cluto służącego do zaawansowanej analizy skupień. Artykuł omawia: (1) Koncepcję całego systemu, postrzeganą z punktu widzenia użytkownika, (2) Architekturę systemu oraz jego elementy odpowiedzialne za przetwarzanie tekstu, (3) Cechy językowe służące do opisu dokumentów, (4) Zastosowanie modułów analizy danych, takich jak Stylo czy Cluto. W artykule zostały też przedstawione przykładowe zastosowania systemu.

@article{eder_visualization_2017,
	title = {Visualization in stylometry: {Cluster} analysis using networks},
	volume = {32},
	issn = {2055-7671, 2055-768X},
	shorttitle = {Visualization in stylometry},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqv061},
	doi = {10.1093/llc/fqv061},
	language = {en},
	number = {1},
	urldate = {2020-10-06},
	journal = {Digital Scholarship in the Humanities},
	author = {Eder, Maciej},
	month = apr,
	year = {2017},
	pages = {50--64},
}

@book{magyar_szamitogepes_nyelveszeti_konferencia_mszny_2016,
	address = {Szeged},
	title = {{MSZNY} 2016: {XII}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia} : {Szeged}, 2016. január 21-22.},
	isbn = {978-963-306-450-4},
	shorttitle = {{MSZNY} 2016},
	language = {Váltakozva magyar és angol nyelven},
	publisher = {JATEPress},
	editor = {{Magyar Számítógépes Nyelvészeti Konferencia} and Tanács, Attila and Varga, Viktor and Vincze, Veronika and {Szegedi Tudományegyetem} and {Természettudományi és Informatikai Kar} and {Informatikai Tanszékcsoport}},
	year = {2016},
	note = {OCLC: 1014670517},
}

@article{eder_stylometry_2016,
	title = {Stylometry with {R}: {A} {Package} for {Computational} {Text} {Analysis}},
	volume = {8},
	url = {https://doi.org/10.32614/RJ-2016-007},
	doi = {10.32614/RJ-2016-007},
	number = {1},
	journal = {The R Journal},
	author = {Eder, Maciej and Rybicki, Jan and Kestemont, Mike},
	year = {2016},
	pages = {107--121},
}

@inproceedings{dravucz_f_szabo_mk_vincze_v_szentiment-_2016,
	address = {Szeged},
	title = {Szentiment- és emóciószótárak eredményességének mérése emóció- és szentimentkorpuszokon},
	booktitle = {{XIII}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}},
	author = {Drávucz, F., Szabó M.K., Vincze V:},
	year = {2016},
	pages = {228--239},
}

@incollection{szabo_mk_vincze_v_morvay_g_magyar_2016,
	address = {Budapest},
	title = {Magyar nyelvű szövegek emócióelemzésének elméleti nyelvészeti és nyelvtechnológiai problémái},
	booktitle = {Távlatok a mai magyar alkalmazott nyelvészetben},
	publisher = {Tinta},
	author = {Szabó M.K., Vincze V., Morvay G.},
	year = {2016},
}

@book{moisl_cluster_2015,
	title = {Cluster {Analysis} for {Corpus} {Linguistics}:},
	isbn = {978-3-11-035025-8},
	shorttitle = {Cluster {Analysis} for {Corpus} {Linguistics}},
	url = {https://www.degruyter.com/document/doi/10.1515/9783110363814/html},
	urldate = {2021-07-30},
	publisher = {DE GRUYTER},
	author = {Moisl, Hermann},
	month = jan,
	year = {2015},
	doi = {10.1515/9783110363814},
}

@book{tanacs_xi_2015,
	address = {Szeged},
	title = {{XI}. {Magyar} számítógeopes nyelvészeti konferencia: {MSZNY} 2015},
	isbn = {978-963-306-359-0},
	shorttitle = {{XI}. {Magyar} számítógeopes nyelvészeti konferencia},
	publisher = {Szegedi Tudományegyetemen, Informatikai Tanszékcsoport},
	editor = {Tanács, Attila and Varga, Viktor and Vincze, Veronika},
	year = {2015},
}

@article{juola_rowling_2015,
	title = {The {Rowling} {Case}: {A} {Proposed} {Standard} {Analytic} {Protocol} for {Authorship} {Questions}},
	issn = {2055-7671, 2055-768X},
	shorttitle = {The {Rowling} {Case}},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqv040},
	doi = {10.1093/llc/fqv040},
	language = {en},
	urldate = {2020-10-06},
	journal = {Digital Scholarship in the Humanities},
	author = {Juola, Patrick},
	month = oct,
	year = {2015},
	pages = {fqv040},
}

@book{tanacs_x_2014,
	address = {Szeged},
	title = {X. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}, {MSZNY} 2014, {Szeged}, 2014. január 16 - 17},
	isbn = {978-963-306-246-3},
	language = {hun},
	publisher = {Szegedi Tudományegyetem, Informatikai Tanszékcsoport},
	editor = {Tanács, Attila},
	year = {2014},
	note = {Meeting Name: Magyar Számítógépes Nyelvészeti Konferencia
OCLC: 931621600},
}

@article{rybicki_collaborative_2014,
	title = {Collaborative authorship: {Conrad}, {Ford} and {Rolling} {Delta}},
	volume = {29},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Collaborative authorship},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqu016},
	doi = {10.1093/llc/fqu016},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Rybicki, J. and Hoover, D. and Kestemont, M.},
	month = sep,
	year = {2014},
	pages = {422--431},
}

@article{torruella_lexical_2013,
	title = {Lexical {Statistics} and {Tipological} {Structures}: {A} {Measure} of {Lexical} {Richness}},
	volume = {95},
	issn = {18770428},
	shorttitle = {Lexical {Statistics} and {Tipological} {Structures}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1877042813041888},
	doi = {10.1016/j.sbspro.2013.10.668},
	language = {en},
	urldate = {2021-03-03},
	journal = {Procedia - Social and Behavioral Sciences},
	author = {Torruella, Joan and Capsada, Ramon},
	month = oct,
	year = {2013},
	pages = {447--454},
}

@article{kestemont_cross-genre_2012,
	title = {Cross-{Genre} {Authorship} {Verification} {Using} {Unmasking}},
	volume = {93},
	issn = {0013-838X, 1744-4217},
	url = {http://www.tandfonline.com/doi/abs/10.1080/0013838X.2012.668793},
	doi = {10.1080/0013838X.2012.668793},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {English Studies},
	author = {Kestemont, Mike and Luyckx, Kim and Daelemans, Walter and Crombez, Thomas},
	month = may,
	year = {2012},
	pages = {340--356},
}

@book{tanacs_viii_2011,
	address = {Szeged},
	title = {{VIII}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}: {MSZNY} 2011},
	isbn = {978-963-306-121-3},
	shorttitle = {{VIII}. {Magyar} {Számítógépes} {Nyelvészeti} {Konferencia}},
	publisher = {JATEPress},
	editor = {Tanács, Attila and Vincze, Veronika},
	year = {2011},
}

@article{smith_improving_2011,
	title = {Improving {Authorship} {Attribution}: {Optimizing} {Burrows}' {Delta} {Method}*},
	volume = {18},
	issn = {0929-6174, 1744-5035},
	shorttitle = {Improving {Authorship} {Attribution}},
	url = {http://www.tandfonline.com/doi/abs/10.1080/09296174.2011.533591},
	doi = {10.1080/09296174.2011.533591},
	language = {en},
	number = {1},
	urldate = {2020-10-06},
	journal = {Journal of Quantitative Linguistics},
	author = {Smith, Peter W. H. and Aldridge, W.},
	month = feb,
	year = {2011},
	pages = {63--88},
}

@article{rybicki_deeper_2011,
	title = {Deeper {Delta} across genres and languages: do we really need the most frequent words?},
	volume = {26},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Deeper {Delta} across genres and languages},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqr031},
	doi = {10.1093/llc/fqr031},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Rybicki, J. and Eder, M.},
	month = sep,
	year = {2011},
	pages = {315--321},
}

@article{lancashire_vocabulary_2009,
	title = {Vocabulary {Changes} in {Agatha} {Christie}'s {Mysteries} as an {Indication} of {Dementia}: {A} {Case} {Study}},
	journal = {19Th Annual Rotman Research Institute Conference, Cognitive Aging: Research and Practice},
	author = {Lancashire, Ian and Hirst, Graeme},
	year = {2009},
}

@article{bagavandas_style_2008,
	title = {Style {Consistency} and {Authorship} {Attribution}: {A} {Statistical} {Investigation}*},
	volume = {15},
	issn = {0929-6174, 1744-5035},
	shorttitle = {Style {Consistency} and {Authorship} {Attribution}},
	url = {http://www.tandfonline.com/doi/abs/10.1080/09296170701803426},
	doi = {10.1080/09296170701803426},
	language = {en},
	number = {1},
	urldate = {2021-07-12},
	journal = {Journal of Quantitative Linguistics},
	author = {Bagavandas, M. and Manimannan, G.},
	month = feb,
	year = {2008},
	pages = {100--110},
}

@inproceedings{abbasi_framework_2007,
	title = {A {Framework} for {Stylometric} {Similarity} {Detection} in {Online} {Settings}.},
	author = {Abbasi, Ahmed and Chen, Hsiu-chin},
	year = {2007},
	pages = {127},
}

@article{van_dalen-oskam_delta_2007,
	title = {Delta for {Middle} {Dutch} {Author} and {Copyist} {Distinction} in {Walewein}},
	volume = {22},
	issn = {0268-1145, 1477-4615},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqm012},
	doi = {10.1093/llc/fqm012},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {van Dalen-Oskam, K. and van Zundert, J.},
	month = may,
	year = {2007},
	pages = {345--362},
}

@article{argamon_interpreting_2007,
	title = {Interpreting {Burrows}'s {Delta}: {Geometric} and {Probabilistic} {Foundations}},
	volume = {23},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Interpreting {Burrows}'s {Delta}},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqn003},
	doi = {10.1093/llc/fqn003},
	language = {en},
	number = {2},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Argamon, S.},
	month = oct,
	year = {2007},
	pages = {131--147},
}

@article{hirst_bigrams_2007,
	title = {Bigrams of {Syntactic} {Labels} for {Authorship} {Discrimination} of {Short} {Texts}},
	volume = {22},
	issn = {0268-1145, 1477-4615},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqm023},
	doi = {10.1093/llc/fqm023},
	language = {en},
	number = {4},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Hirst, G. and Feiguina, O.},
	month = sep,
	year = {2007},
	pages = {405--417},
}

@article{grieve_quantitative_2007,
	title = {Quantitative {Authorship} {Attribution}: {An} {Evaluation} of {Techniques}},
	volume = {22},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Quantitative {Authorship} {Attribution}},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqm020},
	doi = {10.1093/llc/fqm020},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Grieve, J.},
	month = may,
	year = {2007},
	pages = {251--270},
}

@article{stamou_stylochronometry_2007,
	title = {Stylochronometry: {Stylistic} {Development}, {Sequence} of {Composition}, and {Relative} {Dating}},
	volume = {23},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Stylochronometry},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/fqm029},
	doi = {10.1093/llc/fqm029},
	language = {en},
	number = {2},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Stamou, C.},
	month = oct,
	year = {2007},
	pages = {181--199},
}

@incollection{hutchison_visualizing_2006,
	address = {Berlin, Heidelberg},
	title = {Visualizing {Authorship} for {Identification}},
	volume = {3975},
	isbn = {978-3-540-34478-0 978-3-540-34479-7},
	url = {http://link.springer.com/10.1007/11760146_6},
	urldate = {2021-08-03},
	booktitle = {Intelligence and {Security} {Informatics}},
	publisher = {Springer Berlin Heidelberg},
	author = {Abbasi, Ahmed and Chen, Hsinchun},
	editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Dough and Vardi, Moshe Y. and Weikum, Gerhard and Mehrotra, Sharad and Zeng, Daniel D. and Chen, Hsinchun and Thuraisingham, Bhavani and Wang, Fei-Yue},
	year = {2006},
	doi = {10.1007/11760146_6},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {60--71},
}

@article{van_halteren_new_2005,
	title = {New {Machine} {Learning} {Methods} {Demonstrate} the {Existence} of a {Human} {Stylome}},
	volume = {12},
	issn = {0929-6174, 1744-5035},
	url = {http://www.tandfonline.com/doi/abs/10.1080/09296170500055350},
	doi = {10.1080/09296170500055350},
	language = {en},
	number = {1},
	urldate = {2020-10-06},
	journal = {Journal of Quantitative Linguistics},
	author = {van Halteren, Hans and Baayen, Harald and Tweedie, Fiona and Haverkort, Marco and Neijt, Anneke},
	month = apr,
	year = {2005},
	pages = {65--77},
}

@article{can_change_2004,
	title = {Change of {Writing} {Style} with {Time}},
	volume = {38},
	issn = {0010-4817},
	url = {http://link.springer.com/10.1023/B:CHUM.0000009225.28847.77},
	doi = {10.1023/B:CHUM.0000009225.28847.77},
	language = {en},
	number = {1},
	urldate = {2021-07-12},
	journal = {Computers and the Humanities},
	author = {Can, Fazli and Patton, Jon M.},
	month = feb,
	year = {2004},
	pages = {61--82},
}

@article{pennebaker_words_2003,
	title = {Words of wisdom: {Language} use over the life span.},
	volume = {85},
	issn = {1939-1315, 0022-3514},
	shorttitle = {Words of wisdom},
	url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0022-3514.85.2.291},
	doi = {10.1037/0022-3514.85.2.291},
	language = {en},
	number = {2},
	urldate = {2021-07-12},
	journal = {Journal of Personality and Social Psychology},
	author = {Pennebaker, James W. and Stone, Lori D.},
	year = {2003},
	pages = {291--301},
}

@article{stewart_charles_2003,
	title = {Charles {Brockden} {Brown}: {Quantitative} {Analysis} and {Literary} {Interpretation}},
	volume = {18},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Charles {Brockden} {Brown}},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/18.2.129},
	doi = {10.1093/llc/18.2.129},
	language = {en},
	number = {2},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Stewart, L. L.},
	month = jun,
	year = {2003},
	pages = {129--138},
}

@article{burrows_delta_2002,
	title = {'{Delta}': a {Measure} of {Stylistic} {Difference} and a {Guide} to {Likely} {Authorship}},
	volume = {17},
	issn = {0268-1145, 1477-4615},
	shorttitle = {'{Delta}'},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/17.3.267},
	doi = {10.1093/llc/17.3.267},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Burrows, J.},
	month = sep,
	year = {2002},
	pages = {267--287},
}

@article{forsyth_cicero_1999,
	title = {Cicero, {Sigonio}, and {Burrows}: investigating the authenticity of the {Consolatio}},
	volume = {14},
	issn = {2055-768X, 2055-7671},
	shorttitle = {Cicero, {Sigonio}, and {Burrows}},
	url = {http://academic.oup.com/dsh/article/14/3/375/936871},
	doi = {10.1093/llc/14.3.375},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Digital Scholarship in the Humanities},
	author = {Forsyth, Rs and Holmes, Di and Tse, Ek},
	month = sep,
	year = {1999},
	pages = {375--400},
}

@article{binongo_application_1999,
	title = {The application of principal component analysis to stylometry},
	volume = {14},
	issn = {0268-1145, 1477-4615},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/14.4.445},
	doi = {10.1093/llc/14.4.445},
	language = {en},
	number = {4},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Binongo, J. and Smith, M.},
	month = dec,
	year = {1999},
	pages = {445--466},
}

@article{holmes_evolution_1998,
	title = {The {Evolution} of {Stylometry} in {Humanities} {Scholarship}},
	volume = {13},
	issn = {0268-1145, 1477-4615},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/13.3.111},
	doi = {10.1093/llc/13.3.111},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Holmes, D. I.},
	month = sep,
	year = {1998},
	pages = {111--117},
}

@article{baayen_outside_1996,
	title = {Outside the cave of shadows: using syntactic annotation to enhance authorship attribution},
	volume = {11},
	issn = {0268-1145, 1477-4615},
	shorttitle = {Outside the cave of shadows},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/11.3.121},
	doi = {10.1093/llc/11.3.121},
	language = {en},
	number = {3},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Baayen, H. and van Halteren, H. and Tweedie, F.},
	month = sep,
	year = {1996},
	pages = {121--132},
}

@article{kjell_authorship_1995,
	title = {Authorship {Determination} {Using} {Letter} {Pair} {Frequency} {Features} with {Neural} {Network} {Classifiers}},
	volume = {9},
	issn = {0268-1145, 1477-4615},
	url = {https://academic.oup.com/dsh/article-lookup/doi/10.1093/llc/9.2.119},
	doi = {10.1093/llc/9.2.119},
	language = {en},
	number = {2},
	urldate = {2020-10-06},
	journal = {Literary and Linguistic Computing},
	author = {Kjell, B.},
	month = apr,
	year = {1995},
	pages = {119--124},
}

@article{kjell_discrimination_1994,
	title = {Discrimination of authorship using visualization},
	volume = {30},
	issn = {03064573},
	url = {https://linkinghub.elsevier.com/retrieve/pii/0306457394900299},
	doi = {10.1016/0306-4573(94)90029-9},
	language = {en},
	number = {1},
	urldate = {2021-08-03},
	journal = {Information Processing \& Management},
	author = {Kjell, Bradley and Woods, W.Addison and Frieder, Ophir},
	month = jan,
	year = {1994},
	pages = {141--150},
}

@article{katalin_egy_nodate,
	title = {Egy magyar nyelvű szentimentkorpusz létrehozásának tapasztalatai},
	language = {hu},
	author = {Katalin, Szabó Martina and Veronika, Vincze},
	pages = {8},
}

@article{szabo_summary_nodate,
	title = {{SUMMARY} {OF} {PHD} {THESIS}},
	language = {en},
	author = {Szabó, Martina Katalin},
	pages = {17},
}

@article{maciej_piasecki_open_nodate,
	title = {Open {Stylometric} {System} {WebSty}: {Integrated} {Language} {Processing}, {Analysis} and {Visualisation}},
	shorttitle = {Open {Stylometric} {System} {WebSty}},
	url = {http://cmst.eu/articles/open-stylometric-system-websty-integrated-language-processing-analysis-and-visualisation/},
	doi = {10.12921/CMST.2018.0000007},
	urldate = {2020-10-20},
	author = {Maciej Piasecki and Tomasz Walkowiak and Maciej Eder},
	note = {Publisher: ICHB PAS Poznan Supercomputing and Networking Center},
}

@article{stamatatos_survey_nodate,
	title = {A {Survey} of {Modern} {Authorship} {Attribution} {Methods}},
	volume = {60},
	issn = {15322882, 15322890},
	language = {en},
	number = {3},
	journal = {Journal of the American Society for Information Science and Technology},
	author = {Stamatatos, Efstathios},
}