Classification of fake news in Ukraine and abroad

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Classification of fake news in Ukraine and abroad

M. Kitsa

Candidate of Sciences in Social Communications

Associate Professor

Associate Professor of Journalism and Mass Media

Lviv Polytechnic National University Lviv

Abstract

The aim of the work is to propose a broad classification of fake news based on the generalization of Ukrainian and international research.

Research methodology. Both theoretical and empirical research methods were used in the research process. The research methodology consisted of several stages. The first is data collection. This method was used to build a dataset of fake news articles from various sources. These sources included known purveyors of fake news, such as clickbait sites or biased blogs, as well as reputable news sources that have published fake news. The next stage was extraction of fake news features. After collecting a dataset of desinformation materials, we extract relevant functions that can be used as keywords for searching in Google. These data include word frequencies, grammatical structures, or other linguistic features that are known to be associated with fake news.

Results. Western researchers distinguish ten types of «fake news» [7]. Each of the ten forms of deceptive or illusory content carries a different level of threat, impact, and intent. The focus should be on identifying the types of content that are malicious and pose a threat of panic and confusion. Foreign researchers distinguish the following types of fakes: fake news, manipulation, deep fakes, puppet news, phishing, spreading rumors, bots, disinformation, clickbait, satire and parody. The above classification is quite narrow, as it covers specific examples of fake media publications. Considering that the media market and the Internet as a platform are dynamic, changing and reacting to external factors, a broader classification was proposed that would work in the longer term and that would also be able to adapt to dynamic changes in the genre.

Novelty. The novelty of this work is the proposed broad classification of fake news in media outlets on the basis of theoretical and empirical research.

Practical meaning. The obtained information can be used in further monitoring and research of fake news in Ukrainian and international media outlets. By accurately classifying fake news, the audience and journalists can identify the sources of misinformation and track the spread of false information. By developing different tools to classify fake news, other researchers can help educate the public on how to spot false information online and avoid being misled, which is an important aspect of media literacy.

Key words: fake news, disinformation, media, audience, clickbait.

Кіца М.О.

Класифікація фейкових новин в Україні та за кордоном

Анотація

fake news article panic

Мета дослідження - на основі узагальнення українських та міжнародних досліджень запропонувати широку класифікацію фейкових новин.

Методологія дослідження. У процесі дослідження використані як теоретичні, так і емпіричні методи. Дослідження передбачає кілька етапів. По-перше, це збір даних для створення набору даних фейкових новинних статей з різних джерел. Ці джерела включали відомих розповсюджувачів фейкових новин, як-от сайти-клікбейти або заангажовані блоги, а також авторитетні джерела новин, які, проте, публікували фейкову інформацію. Наступний етап - виділення ознак фейкових новин. Після збору набору даних дезінформаційних матеріалів виділено релевантні функції, які можна використовувати як ключові слова для пошуку в Google. Ці дані включали частоту вживання слів, граматичні структури чи інші лінгвістичні особливості, які, як відомо, пов'язані з фейковими новинами.

Результати. Західні дослідники виділяють десять видів «фейкових новин». Кожна з десяти форм оманливого або ілюзорного вмісту має різний рівень загрози, впливу та наміру. Варто зосередитися на виявленні типів шкідливого вмісту, який створює загрозу паніки та плутанини. Зарубіжні дослідники виділяють такі типи фейків: фейкові новини, маніпуляції, глибинні фейки, маріонеткові новини, фішинг, поширення чуток, боти, дезінформацію, клікбейт, сатиру та пародію. Наведена вище класифікація є досить вузькою, оскільки охоплює конкретні приклади фейкових публікацій у ЗМІ. З огляду на те, що медіаринок та інтернет як платформа є динамічними, змінними та такими, що реагують на зовнішні чинники, запропоновано ширшу класифікацію, яка працюватиме в довгостроковій перспективі та зможе бути адаптованою до динамічних змін у цій сфері.

Новизна. Запропоновано широку класифікацію фейкових новин у ЗМІ на основі теоретичних та емпіричних досліджень.

Практичне значення. Отримана інформація може бути використана для подальшого моніторингу та дослідження фейкових новин в українських і міжнародних ЗМІ. Правильно класифікуючи фейкові новини, аудиторія та журналісти можуть визначити джерела дезінформації та відстежити поширення неправдивої інформації. Розробляючи різні інструменти для класифікації фейкових новин, можна навчити громадськість виявляти неправдиву інформацію в інтернеті та уникнути введення в оману, що є важливим аспектом медіаграмотності.

Ключові слова: фейкові новини, дезінформація, ЗМІ, аудиторія, клікбейт.

Kitsa M.

Klasyfikacja fake newsow w Ukrainie i za granicq

Cel badania - zaproponowac szerokq klasyfikacja fake newsow na podstawie uogolnienia badan ukrainskich i miqdzynarodowych

Adnotacja

Metodologia badania. W procesie badania zostafy wykorzystane zarowno teoretyczne, jak i empiryczne metody. Badanie obejmuje kilka etapow. Przede wszystkim, jest to gromadzenie danych w celu stworzenia zestawu danych fafszywych artykufow z roznych zrodef. Te zrodfa obejmowafy sfynnych rozpowszechniaczy fafszywych wiadomosci, jak na przykfad strony-clickbaity lub zaangazowane blogi, a takze miarodajne zrodfa wiadomosci, ktore jednak publikowafy fafszywe informacje. Kolejnym etapem jest wyroznienie cech fafszywych wiadomosci. Po zebraniu zestawu danych materiafow dezinformacyjnych zostafy wyodrqbnione relewantne funkcje, ktore mogq byc wykorzystywane jako sfowa kluczowe w wyszukiwarkach Google. Te dane obejmowafy czqstotliwosc uzywania sfow, struktury gramatyczne lub inne osobliwosci lingwistyczne, ktore, jak wiadomo, sq zwiqzane z fafszywymi wiadomosciami.

Wyniki. Zachodni badacze wyrozniajq dziesiqc rodzajow „fake newsow”. Kazda z dziesiqciu form o zfudnej lub iluzorycznej tresci posiada rozny stopien zagrozenia, wpfywu oraz zamiaru. Nalezy siq skoncentrowac na identyfikacji rodzajow zfosliwej tresci, ktora stwarza zagrozenie paniki i zamieszania. Zagraniczni badacze wyrozniajq nastqpujqce rodzaje fejkow: fafszywe wiadomosci, manipulacje, deep fakes, newsy marionetkowe, phishing, rozpowszechnianie plotek, boty, dezinformacja, clickbait, satyra i parodia. Powyzsza klasyfikacja jest dosc wqska, poniewaz obejmuje konkretne przykfady fafszywych publikacji w srodkach masowego przekazu. Biorqc pod uwagq to, ze rynek medialny i Internet sq platformami dynamicznymi, zmiennymi i reagujq na czynniki zewnqtrzne, zaproponowano szerszq klasyfikacjq, ktora siq sprawdzi w dfuzszej perspektywie i bqdzie mogfa byc dostosowana do dynamicznych zmian w tym obszarze.

Nowosc. Zaproponowano szerokq klasyfikacjq fake newsow w srodkach masowego przekazu na podstawie badan teoretycznych i empirycznych.

Znaczenie praktyczne. Uzyskane informacje mogq byc wykorzystane w celu monitoringu oraz badania fake newsow w ukrainskich i miqdzynarodowych srodkach masowego przekazu. Dziqki prawidfowej klasyfikacji fafszywych wiadomosci, audytorium i dziennikarze mogq identyfikowac zrodfa dezinformacji oraz sledzic rozpowszechnianie fafszywych informacji. Opracowujqc rozne narzqdzia dla klasyfikacji fake newsow, mozna nauczyc spofeczenstwo wykrywac nieprawidfowe informacje w Internecie, a takze unikac wprowadzenia w bfqd, co jest waznym aspektem alfabetyzmu medialnego.

Stowa kluczowe: fake news/fafszywe wiadomosci, dezinformacja, srodki masowego przekazu, audytorium, clickbait.

Introduction

Distorted news material, the content of which is far from reality, is not a new phenomenon in journalism. False information in mass media is a component of journalism that has accompanied the history of media development since the invention of the printing press. Since posting such information helps sell tabloids, this factor makes fakes a very attractive way for publishers to increase sales. The practice of posting false information has also spread rapidly on the Internet - such materials are often posted with «clickbait» headlines - that is, those that attract the attention of readers, encourage them to click on the headline and click on the link.

In today's world where content is created and shared instantly, the ability to recognize and prioritize posts that pose a risk to reputation and audience awareness is a key challenge for communications and business continuity professionals.

Throughout history, one can find many examples of the spread of false news. During the Second World War, the Nazis used fakes to create anti-Semitic fervor. In the 19th century in the United States, racist attitudes led to the publication of false stories about the shortcomings of African Americans and their alleged crimes [9, p. 23].

In the 1890s, rival newspaper publishers Joseph Pulitzer and William Hearst competed for audiences through sensationalism, publishing rumors as if they were fact. This practice later became known as «yellow journalism». Their false news played an important role in the introduction of the USA into the Spanish-American War of 1898 [3, p. 212].

It is also important to note that the term «fake news» was also used to try to discredit news that did not suit influential people. When they are not satisfied with the news, there are accusations that this news is fabricated, that is, the news, which is not actually a fake, thus enters the information field of false publications [7, p. 1095].

Of course, to date, the phenomenon of fake information has been greatly transformed, it has absorbed the specifics of the time and the political and economic situation [12, p. 15]. Fake news as a propaganda tool has been around for centuries, and the Internet as a platform for its dissemination is the only new thing about it as a phenomenon. With the advent of the Internet, fake news began to spread much faster, due to insufficient legal and social regulation of this platform [2]. A recent study by the Pew Research Center found that Americans consider fake news and misinformation to be a bigger and more pervasive problem than racism, illegal immigration and terrorism [10].

Given society's understanding of the problem of fake news, the question arises - why does it spread so quickly? And why so many people still do not check the information. The reason is that fake news is misleading not only in its content, but also in the influence it exerts on the prejudices and inclinations of society.

The first reason for the spread of fakes can are the channels through which information is distributed. Of course, the greatest spread of news in the shortest time is facilitated by the development of the Internet. The online form is incredibly fast and easy to use [5]. And the fact that such fake news is usually published under the authorship (even if it is dubious) and is distributed in the form of reposts in social networks, it creates great trust in the audience. For the most part, we are friends on social networks with people we like and trust. Thus, we are more inclined to trust and believe any information that is transmitted by people we know [11]. Simply put, fake news uses the trust we have in our friends and family to trick us into trusting and believing it too.

In one of the studies in the UK, one in six respondents admitted that they believe everything that their friends share on social networks. The same study [6, p. 806] found that information seen on Facebook appears to be more credible than information from real experts. In short, fake news relies on the trust we have in our friends and family to get us to set aside our doubts and avoid too much scrutiny.

Another reason for the spread of fakes is their sensationalism [1, p. 283]. A lot of important news is very mundane - a city council hearing or a tax policy debate is a proven, reliable source, but boring to watch or read. Whereas fake news is almost always sensational. They tell a story, usually amazing, sometimes they can go to extremes. The most popular topics for fake news are usually those that shock, amaze, sometimes even offend. In a word, they cause an emotional reaction in readers and encourage not to carefully check sources or critical thinking, but to transmission and active sharing [8, p. 770]. The authors of fakes do not want the reader to think, they want to impress and leave a mark in the mind.

The only protection against fake news is vigilance. Taking the time to verify sources before sharing them and learning how to spot fake news in the wild are two important steps.

As practice shows, the truth is usually not so sensational, and true facts are not interesting or shocking, so they often lose in this fight against fakes. As Jonathan Swift wrote in 1710, «Falsehood flies, and Truth limps behind it» [4, p. 63]. Nowhere is this more true than on the Internet.

Fake news is a widespread phenomenon not only in Ukrainian media, but also in the news outlets over the world. It can have different content, forms and means of input. So it is important to understand, what are the types and classes of fake news depending of their characteristics.

Problem statement and research methods

The aim of the work is to propose a broad classification of fake news based on the generalization of Ukrainian and international research.

The methodology for classifying fake news typically involves a combination of machine learning algorithms and human annotators. Here are some of the key steps involved in this process. The first is data collection. This method was used to build a dataset of fake news articles from various sources. These sources included known purveyors of fake news, such as clickbait sites or conspiracy theory blogs, as well as reputable news sources that have been targeted by fake news campaigns. The next method is feature extraction. After collecting a dataset of articles, we extract relevant features that can be used as key words for search in Google. These features included word frequencies, grammatical structures, or other linguistic features that are known to be associated with fake news. With a dataset of articles and extracted features, we tried to classify articles as either real or fake. Overall, the methodology for classifying fake news is an iterative process that involves collecting data, extracting features, outlining key words for search engine, evaluating its performance, and refining the model based on human annotations. By combining these approaches, we developed highly accurate models for detecting fake news and gain a deeper understanding of their peculiarities.

Overall, the classification of fake news is an important tool for combating misinformation and promoting media literacy. By developing accurate and effective methods for detecting and classifying fake news, researchers can help mitigate the harmful effects of false information and promote a more informed and engaged society.

Results

Western researchers distinguish ten types of «fake news». Each of the ten forms of deceptive or illusory content carries a different level of threat, impact, and intent. The focus should be on identifying the types of content that are malicious and pose a threat of panic and confusion. Foreign researchers distinguish the following types of fakes: fake news, manipulation, deep fakes, puppet news, phishing, spreading rumors, bots, disinformation, clickbait, satire and parody. Now we will consider these types in more detail.

Fake news is the deliberate publication of false information and disguising it as news. Fake news is designed to misinform an audience, actual fake news is completely false and never reports the truth.

Manipulation. Manipulation by the media is a deliberate change of content, context, which directly affects the meaning. For example, using a quote out of context, as well as cropping an image to not accurately resemble the actual story.

Deepfake. To create a deepfake, digital technologies are used to prepare a fake video or photo fact. For example, a voice is superimposed on the video, and with the help of special software, graphics are added to the face, thereby falsifying the speeches, statements, performances of famous and influential people. The most popular viral examples of such falsifications are speeches by Barack Obama and Mark Zuckerberg.

News puppets fakes refer to the technique of creating and disseminating informational messages of opposing views with the purpose of intentionally causing two (or more) parties to collide in order to create conflict. Such news reports may spread information about fake events dedicated to supporters of opposition political parties that will take place at the same place, time and day. A vivid example of the use of such technology is the example of users from Russia creating a protest and spreading its date and place through the Facebook social network. While an announcement about a counter-protest appeared on the network on the same day and in the same place. It was later discovered that the author of these two events was the same organization.

Phishing is the use of schemes aimed at illegally obtaining personal information from Internet users. For example, a news post or page or email newsletter that is often subscribed to by media readers may contain malicious web links. After the user clicks on such a link, his personal data gets to cybercriminals and can be used for criminal purposes, or resold to other companies. Usually, such links are accompanied by fake messages with «clickbait» titles. Clickbait is a headline that forces an Internet user to click on it, usually containing information of a derogatory nature.

Misinformation. Usually, disinformation is a combination of accurate, true, and incorrect, fake content. Such combinations can be caused by various reasons - the use of uninformed, unverified or unreliable sources, distortion of reality, drawing an incomplete picture of the event, etc. Often this technique is used by journalists unintentionally - due to inexperience, but even more often to achieve commercial, propaganda and other goals.

Rumor spreading. Rumors are one of the types of information dissemination in modern journalism. This is the dissemination of information about odious personalities or high-profile events without proper verification of information sources. Most often, rumors spread much faster than official information. It is interesting that researchers rarely mention this type of fakes as a genre of journalism, since it can be argued that rumors are a genre at the intersection of journalism, sociology, philosophy, semiotics, the field of political technologies, advertising, management, etc. That is, this genre is diverse.

Clickbait. As we mentioned earlier, clickbait is a sensational headline that attracts the reader's attention and literally «forces» the reader to click on it. Clickbait as a kind of fake in journalism exists in the following forms: exaggeration of the truth, intrigue and paraphrasing and taking words out of context. Hyperbolization is used to give the news scale and significance. For example, the online version of TSN published an article: «Tractors» on firewood: Ukrainians began to invent «wood cars» due to the fuel crisis. At a time when only one man actually rode such a marvel of technology, the headline sounds as if this is not an isolated incident. And wood-burning tractors don't run yet: the idea of replacing diesel with wood remains just an idea. An intriguing title is a strong guarantee that the article will be read to the end. This technique works best when talking about issues that are painful for the audience: reforms, money, the dollar exchange rate, medicine, education, etc. Clickbait, like other types of fake news, defies standards of objectivity, accuracy, and credibility.

Satire and parody. Such types of fakes are created for comic and r entertainment purposes. Such fakes do not hide their «fakeness» and even flaunt it. A clear example of the use of this type of fakes is the UaReview.com platform, which is a satirical online publication that publishes fictional (fake) news. Unlike fake (real) news, publications on UaReview.com do not claim to be perceived as real, they exist solely for satirical and humorous purposes - to make fun or make people laugh. Although sometimes they play a «bad joke» when unqualified journalists from other media perceive publications in this media as real, and publish fictional information as real on their pages. At such moments, satirical news turns into fake news. This is another proof that fakes spread faster than facts.

Bots are social media profiles that are not managed by humans or real users. Such profiles are generated and managed by automated technological systems (often such systems are called bot farms or warehouses). Such bots will prevent the spread of fakes in social networks. They repost to themselves fake pages from online mass media, and also contribute to an active discussion under news publications in media accounts in social networks.

The above classification is quite narrow, as it covers specific examples of fake media publications. Considering the fact that the media market and the Internet as a platform are dynamic, changing and reacting to external factors, it is worth choosing both a narrow classification of fake news, with the help of which it is possible to identify a fake individually, and it is worth developing a broader classification that will work in the longer term, and which will also be able to adapt to dynamic changes in the genre.

Using the theory of the broader meaning of fake as a phenomenon, we can classify it as follows:

Depending on the ratio of reliable, verified information and unreliable, invented information: 100% fake, information that does not contain true facts; partially true publication with fictional elements.

Depending on the veracity of the time, place and other circumstances of the event referred to in the publication: the publication is presented as «fresh», that is, about what happened today or in the near future, but actually took place in the given past; publication about an event that actually took place, but its place has been changed in the publication (for example, the event took place in Russia, and the mass media presents it as in Ukraine, etc.).

Depending on the number of actors mentioned in the publication: the publication refers to the statement of a public figure attributed to a false author; the author in the publication presents a minor participant in the event as the main actor; the publication is false, with reference to persons who may have witnessed the event.

Depending on the purpose of creating and distributing the publication: publications intended to entertain the audience (such as satire); publications aimed at achieving political goals: for example, discrediting competitors, provocation, political coup, etc.; publications aimed at discriminating against social minorities - on the basis of status, language, origin, race, nationality, property status, religious beliefs, affiliation to NGOs, etc.; publications for the purpose of market manipulation, discrediting competitors or black PR.; publications to increase the traffic of website visits (clickbait is useful here); publications with the aim of attracting attention to an individual, company, project or movement.

Depending on the level of probability perception: publications that are obviously fake; publications that cause doubt in readers and force them to check information; «quality» publications, that is, those that are falsified so truthfully that they do not cause doubts in the audience.

In our opinion, this type of classification is the most successful, as it makes it possible to systematize fakes on various topics according to key features, and also allows to classify fake news despite the dynamic development and changes of the market and society.

Conclusions

Fake news is a dangerous phenomenon that can have serious consequences for society and individuals. Over the past few years, fake news has become widespread in social media and mass media. This can lead to panic among the population, political conflicts, misinformation, and other negative consequences.

To classify fake news, it is important to pay attention to several characteristics. By topic, the most common types are fake news about war, politics, and health news. In order to fight the spread of fake news, you need to be able to critically evaluate information sources and perform fact-checking before sharing news with others. It is also important to pay attention to official sources of information, verified media resources and trusted sources to avoid the spread of fake news.

Overall, the fight against fake news requires the cooperation of various sides, including government agencies, media, technology companies and civil society. A responsible attitude to information and a constant fight against fake news is an important element of ensuring a healthy information environment and protecting society from the negative consequences of fake news. Classification of fake news has a significant role in different directions. The first is identifying sources of misinformation. By accurately classifying fake news, researchers and journalists can identify the sources of misinformation and track the spread of false information. This can be useful for government, especially during the war, for public health officials, who need to combat rumors and conspiracy theories during a pandemic, or for political campaigns, who want to avoid being associated with fake news. The next is improving media literacy. The ability to identify fake news is an important aspect of media literacy. By developing different tools to classify fake news, it can help to educate the public on how to spot false information online and avoid being misled. The next, but not the least is developing countermeasures. Once fake news has been identified, journalists, scientists, the authority or NGO can develop countermeasures to combat its spread. For example, by analyzing the linguistic and semantic features of fake news, researchers can develop automated tools to detect and flag suspicious content on social media platforms. The last is protecting democratic institutions: Fake news has the potential to undermine trust in democratic institutions and erode social cohesion. By accurately classifying fake news, we can help to protect the integrity of democratic processes and promote a more informed public discourse.

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References

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