Fake News in Tourism: A Systematic Literature Review

Abstract

In recent years, the number of fake news stories has significantly increased in the world of media, especially with the widespread use of social media. It has impacted several industries, including tourism. From a tourism point of view, the spread of fake news can contribute to the reduction of the popularity of a destination. It may influence travel decisions by discouraging tourists from visiting certain places and thus damage the reputation of the destination, contributing to economic loss. After a literature review on the communication aspect of fake news and a general introduction of fake news in the tourism and hospitality industry, we conducted a systematic literature review (SLR), a research methodology to collect, identify, and analyse available research studies through a systematic procedure. The current SLR is based on the Scopus, Web of Science, and Google Scholar databases of existing literature on the topic of fake news in the tourism and hospitality industry. The study identifies, lists, and examines existing papers and conference proceedings from a vast array of disciplines, in order to give a well-rounded view on the issue of fake news in the tourism and hospitality industry. After selecting a total of 54 previous studies from more than 20 thousand results for the keywords ‘fake news’ and ‘tourism,’ we have analysed 39 papers in total. The SLR aimed to highlight existing gaps in the literature and areas that may require further exploration in future primary research. We have found that there is relatively limited academic literature available on the subject of fake news affecting tourism destinations, compared to studies focused on hospitality services.

1. Introduction

In recent years, the number of fake news stories has significantly increased in the world of media, especially with the widespread use of social media. It has impacted several industries, including tourism. From a tourism point of view, the spread of fake news can contribute to the reduction of the popularity of a destination. It may influence travel decisions by discouraging tourists from visiting certain places and thus damage the reputation of the destination, contributing to economic loss.

1.1. Fake News

The phenomenon of fake news has garnered significant attention in recent years, particularly due to its profound impact on public perception and decision-making processes. Understanding the distinction between misinformation and disinformation is crucial for analysing the mechanisms through which fake news spreads and influences society. According to Leeder (2019), fake news is closely associated with other forms of inaccurate information, such as misinformation—defined as false or misleading information—and disinformation, which refers to false information intentionally spread to deceive people (Paskin 2018). While misinformation can arise unintentionally, disinformation is deliberately crafted to mislead or deceive (Di Domenico et al. 2021). Most fake news qualifies as disinformation, as it is typically created or distributed with the explicit purpose of deception (Sousa et al. 2020). The digitisation of news has challenged traditional definitions of news. Online platforms provide space for non-journalists to reach a mass audience. The rise of citizen journalism challenged the link between news and journalists (Tandoc et al. 2017).

According to Broda and Strömbäck (2024), in light of the current lack of conceptual clarity, multiple closely related terms are used to describe various forms of false and misleading information. In addition to misinformation, disinformation, and fake news, other terms such as (computational) propaganda, malformation, alternative facts, and rumours are also commonly employed. Tandoc et al. (2017) examined articles that used the term “fake news” and found that the view of fake news is very broad. According to their study, the typology itself refers to several types of fake news, namely news satire, news parody, fabrication, manipulation, advertising, and propaganda. Based on the research, these definitions are based on two basic dimensions, namely, the level of factuality and the level of deception.

Another important aspect is whether and how fake news is shared and how its dissemination in the media space is ensured. Arin et al. (2023) examined the area of fake news sharing behaviour in Germany and the UK. Accidental and intentional sharing may also be due to general sharing behaviour. Those who never share any news are mostly not at risk of sharing fake news. To assess the extent of sharing in general, the authors estimate regressions in which they explain general sharing behaviour using individual-level characteristics. As a proxy for general sharing behaviour, the authors used the number of truthful headlines a respondent wants to share. The study found that sharing activity decreases with age; right-leaning respondents and men tend to share more, especially in the UK (the sample studied). Therefore, some of the significant individual-level determinants of accidental sharing identified earlier by the authors may indeed be partly due to general fake news sharing behaviour. Fake news is covered by many serious and public media outlets. What is important to watch is the sharing of fake news via Facebook. According to Lazer et al. (2018), there is empirical evidence that misinformation has the same likelihood of going viral as credible news on social media sites such as Facebook and X (formerly known as Twitter), as well as being shared more frequently and more quickly than truthful information. This is particularly the case when the fake news is political or aimed at spreading emotive information. Lazer argues that once fake news or misinformation is deemed true, it is difficult to correct. The sharing of fake news and misinformation can have disastrous consequences whenever vital events in the country are involved. According to several authors (Lawson and Kakkar 2022; Damstra et al. 2021), fake news sharing usually takes place in a political context and is very often positively associated with conservative political ideology. However, it is not possible to generalise in this way, as there is a risk of further increasing political polarisation, which is already at a high level. A study by Lawson and Kakkar (2022) claims to offer a more nuanced account, finding that fake news sharing is largely driven by conservatives with low levels of conscience. With high levels of conscientiousness, there is no difference between liberals and conservatives; thus, political factors would not affect fake news sharing. Untruth and fake news spread much further, faster, deeper, and wider than the truth in all categories of information at all times. These findings, according to Vosoughi et al. (2018), were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. The authors also found that fake news was newer and fresher than true news, suggesting that people were more likely to share new information. While false news elicited responses of fear, disgust, and surprise, true news elicited anticipation, sadness, joy, and trust.

In summary, fake news represents a complex phenomenon that significantly influences public perception, decision-making, and political dynamics. The distinctions between misinformation, disinformation, and other related terms highlight the multifaceted nature of inaccurate information and its mechanisms of spread. Understanding the behavioural patterns of fake news sharing and its emotional triggers is essential for developing effective strategies to counter its dissemination and mitigate its societal impact.

1.2. Approaches to Handling Misinformation in Communication

Disinformation, not only in traditional media but especially permeating the social media space, has become one of the most pressing communication issues as it has a significant impact on public opinion, social cohesion, and decision-making. This problem interferes with the marketing communication decisions of businesses. Dealing with misinformation now requires a strictly multidisciplinary approach that takes into account both the technological and human dimensions of the problem. Strategies such as media literacy, fact-checking systems, and inoculation theory are currently being explored to enable communicators to effectively counter the spread of false information.

Although historical inaccuracies can be noted concerning fake news, for the most part, the historical record illustrates the visibility of the relationship between politics and fake news. During the last years, both the media and the communicators of the political spectrum have been disseminating one-sided opinions and a lot of information that often lacks credibility and the possibility of fact-checking (Abu Arqoub et al. 2020). Therefore, such a question can also be asked in the everyday present: to what extent cannot only political news be controlled, even in a democratic state? Damstra et al. (2021) have focused on exploring fake news in the world of social media. Their study did not address satire, and neither did commercial (native) advertising or clickbait articles that are created for commercial reasons only. From a social media perspective, they focused on studies that look at deliberate misinformation that is created primarily to influence public opinion, and not just to entertain followers, for example. Here, however, they note that the intentions of news sources are usually unknown. It is this area of the reasons for sharing and spreading fake news that poses a major challenge for further research in this area, as communication scholars generally distinguish misinformation from disinformation and conventional news by referring to the intentions of the source, without measuring them. Here, the authors note that the most common way of working around this problem is to rely on datasets with verified examples of reports that have been shown to be false, i.e., to contain information with a very low level of factuality. Research findings of Aoun Barakat et al. (2021) indicate that proficiency in social media usage and verification practices positively contributes to individuals’ ability to identify fake news. Conversely, higher levels of trust in social media as a reliable information source are linked to reduced effectiveness in identifying misleading content. Additionally, the analysis highlights the mediating influence of trust in social media information and verification behaviour on fake news identification. These findings underline the importance of fostering critical media literacy and promoting verification behaviour among social media users to enhance their ability to recognise fake news. Addressing over-reliance on social media as an information channel is also crucial to mitigating the spread of misinformation.

Detecting fake news is a very challenging problem, especially given the nuances of language, but also the varying degrees of veracity of media or news stories. When it comes to news reporting, every news story has a purpose and an objective. But how to know if it is fake news? Braşoveanu and Andonie (2021) in their study focused on the fact that in order to understand the motivation of a news story, it is best to analyse the relationship between the speaker and the subject of the news, as well as various credibility metrics. Inferring the details of the different actors involved in a message is a big problem that requires a hybrid approach, which also combines machine learning, semantics, and natural language processing. This study deals with a semantic method for fake news detection that is built on relational features such as sentiment, entities, or facts extracted directly from the text. The authors find that if they focus on short texts with varying degrees of truthfulness, these show that the addition of semantic features significantly improves accuracy. Pennycook et al. (2020) claim to have identified a potential consequence of attaching warnings to inaccurate news headlines that is unlikely to have been documented before: as an ‘implied truth’ effect, whereby unmarked headlines (even if false) are considered more accurate and are more likely to be taken into account when shared on social media. The authors conducted two experimental studies and found that the magnitude of this effect was about one-third the size of the baseline warning effect (in which false headlines with warnings are believed and shared less), and they also found that the increase in sharing intentions caused by the implied truth effect was as large as the increase caused by explicitly marking a headline as true. The issue of fake news is a topic of concern today and plays on people’s moods. This problem spread rapidly during the COVID-19 pandemic due to the easy availability of social media. Bajpai and Sharma (2021) investigated a group of news stories and used blockchain technology and its framework, which served as an effective tool to combat the spread of fake news, looking for ways to avoid fake news. Fake news and misinformation can do immeasurable damage to democracy, as so-called hostile actors can undermine the effectiveness of democratic decision-making and faith in democracy itself by deliberately spreading fake news and partisan views, and then these effects can also manifest themselves unintentionally in social media. Ball (2021) concludes that it may be necessary to change not only the way information is currently processed online, but also the information environment in which people move.

It could be noticed that addressing misinformation in communication requires a multidisciplinary approach that considers both technological tools and human behaviour. Strategies such as media literacy, fact-checking systems, and semantic methods have proven effective in identifying and mitigating the spread of fake news. Promoting critical verification practices and reducing over-reliance on social media as a trusted information source are essential for minimising the societal and democratic impacts of fake news.

1.3. Fake News and the Tourism Aspect

Fake news is now a phenomenon whose presence is in all spheres of human life. It can be said that this phenomenon has crossed social and political boundaries and has penetrated the tourism and hotel industries. The growing awareness of this phenomenon has aroused considerable interest in scientific and professional circles (Vasist and Krishnan 2022). The literature on fake news in tourism and hospitality is not yet as rich as it needs to be and thus lacks a strong theoretical basis. It can be concluded that, on the whole, this kind of research is rather fragmented. Fake news permeates a new state of information structure as it is disseminated and consumed in the postmodern era. Fake news is increasingly appearing in the tourism and hospitality industry. This industry is deeply dependent on information, so the opacity and lack of reliability that fake news brings to its foundation can affect users in particular. And this negative impact can also affect the overall outcome of service consumption, as it will affect customers’ final expectations and their overall experience. Key themes that have already been explored in tourism academia include authenticity, consumer behaviour (including perceptions of risk), marketing in this area, and crisis resolution (Fedeli 2019).

Today’s advanced technologies that enable companies to take new approaches to marketing and communication, and the concomitant change in corporate decision-making and their traditional approaches to tourism marketing (Sousa et al. 2020), are simply examples of what is seen as a multifaceted and geographically complex activity, and tourism is increasingly creating new (and different) market segments with different individual interests. This is also associated with problems in detecting the veracity of information.

A study conducted by Song et al. (2021) shows that the quality of arguments, credibility of the source, the amount of information, and the understanding of emotive words have a positive effect on the perceived usefulness of eWOM, also in the tourism and hospitality industry. Perceived usefulness has a positive effect on eWOM information intake, which in turn predicts purchase intentions of predominantly young consumers. False information within the tourism sector has the potential to shape consumer behaviour, alter expectations, and influence perceptions regarding destinations or entertainment options. Therefore, understanding the implications of fake news on the industry and its affected stakeholders is crucial. To address these challenges, tourism organisations can promote awareness through workshops and digital campaigns aimed at educating businesses and consumers on identifying fake news. Additionally, further research could explore the readiness of stakeholders to implement such recommended practices (Ismail et al. 2024).

When we talk about misinformation in tourism and the typology of this misinformation, it is absolutely crucial to take into account an area such as the phenomenon of recommendations, which plays an important role in the creation of the image of destinations and services. Recommendations, whether they come from influencers, celebrities, or through eWOM (electronic word-of-mouth), often significantly influence information credibility and consumer decision-making (Ismail et al. 2024). Research shows that the quality of recommendations and their perceived authenticity have a direct impact on perceptions of destinations or services, which can amplify the negative consequences associated with false information if these recommendations are not credible (Song et al. 2021). Another important aspect is the emotional element of the actual recommendation by already active tourism participants of a particular destination or tourism facility, whereby positive emotional reactions to recommendations can increase trust and the likelihood of a purchase decision (Ecker et al. 2022). Conversely, people’s negative recommendations that are based on misinformation can negatively affect brand perception, which can have long-term consequences on the reputation of a destination or service (Vasist and Krishnan 2022). Therefore, it is essential to examine not only the quality of the sources of recommendations but also their authenticity and reliability to minimise the spread of false information. Moreover, nowadays, in the era of digitalisation of services, technologies such as artificial intelligence can be used to analyse recommendations in the online environment, which would help to better identify their credibility (Braşoveanu and Andonie 2021). Research in this area should also include the dynamics between tourism stakeholders, such as businesses and consumers, and their willingness to recognise and address potential damage caused by false recommendations. Extending research to this aspect could contribute to a better understanding of the impact of recommendations on consumer decision-making and more effective management of misinformation in the tourism sector.

In the realm of fake news, it is crucial to concentrate on the cognitive, social, and emotional elements that drive individuals to generate or support fake news and misunderstandings. It is equally vital, particularly in the tourism and service sectors, to possess an adequate understanding of the situation once the false information has been rectified (Ecker et al. 2022), allowing us to discuss theories of ongoing influence.

2. Review Method

Following a literature review on the communication aspect of fake news and a general introduction of fake news in the tourism and hospitality industry, we conducted a systematic literature review (SLR), a research methodology to collect, identify, and analyse available research studies through a systematic procedure. The current SLR is based on the Scopus, Web of Science, and Google Scholar databases of existing literature on the topic of fake news in the tourism and hospitality industry. The study identifies, lists, and examines existing papers and conference proceedings from a vast array of disciplines, including tourism, hospitality, psychology, behavioural studies, computation, and tourism management, in order to give a well-rounded view on the issue of fake news in the tourism and hospitality industry. The authors defined two aims of the systematic literature review. The first aim is to systematically search, analyse, and synthesise research results of the published, English-language literature between 2000 and 2024, taking into account the value and accuracy of the studies to create a narrower research topic and research question(s). Specifically, the review investigates how fake news is conceptualised and defined in tourism and hospitality research, what impact fake news and fake reviews have on tourist behaviour, destination image, and the tourism and hospitality industry, what methods and approaches are used in existing studies, and what research gaps can be identified from previous literature on the topic. These may serve as the basis of future primary research as a structured, thematically organised data collection.

2.1. PRISMA Method

To perform our work effectively, we followed appropriate academic rigour and consistency. In the study, the authors followed the guidelines of the PRISMA method. As Page et al. (2021) state, the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, first published in 2009 and revised in 2020, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. This systematic review complies with the PRISMA 2020 guidelines, synthesising and analysing evidence on fake news and fake reviews within the tourism and hospitality industry. Following the PRISMA 2020 checklist, the systematic literature review clearly states its title and objectives and focuses on how fake news (and fake reviews) impact destination image and industry operation. Peer-reviewed articles and conference papers were selected manually from three databases, and findings were grouped thematically within the tourism and hospitality industry (tourism, hotels, restaurants, and review platforms). The PRISMA Flow Chart (Figure 1) shows the selection process of the articles.

Data were extracted from 39 studies, including variables such as author, year, methodology, and findings. Among the selected papers, qualitative and quantitative research were both included. Although formal tools for assessing bias were not applied, we have acknowledged the limitations (geographical focus, study scope, and few academic sources). We have also noted the absence of automation in study selection and bias assessments. The manual selection means that there is a potential for subjectivity in the inclusion/exclusion process.

Although not registered in PROSPERO and lacking a more formal protocol, the review declares no funding or competing interests. Data and summaries are available upon request, supporting transparency and future replication.

2.2. Study Selection and Data Extraction Process

The search strategy involved both automatic and manual searches to explore a broader perspective. A total of three online scientific databases were selected as main sources for the systematic literature review: Scopus, Web of Science, and Google Scholar. The three databases were selected based on their coverage of different disciplines. The searches focused on peer-reviewed journal articles and conference proceedings published between 2000 and 2024 that addressed fake news, misinformation, or disinformation in the context of tourism and hospitality. The last searches occurred in March 2025.

The automatic search was an electronic search based on the defined keywords and Boolean operators: ‘fake news AND tourism,’ ‘fake news AND hospitality,’ ‘misinformation AND travel,’ and ‘disinformation AND (tourism OR hospitality)’. In the manual search, references of relevant articles were also checked to add any papers not included in the list. The search strings were adapted to the exact database. However, the semantic content was consistent in all three platforms. As Google Scholar gave broader and less structured results, manual exclusion was also necessary. All papers were collected and processed in a spreadsheet, and duplicates were removed manually.

To reduce the chance of bias, a data extraction form has been created, and it includes the following columns: identification number of the study (study ID), title and authors, year of publication, name of the journal it was published in, context of the research, key findings, used methodology, and the abstract. The context of the research refers to the segment of the industry each paper addresses: tourism (destinations, destination management, etc.), hospitality (hotels, restaurants), or the whole industry as a whole and in a more general approach. (Appendix A)

2.3. Inclusion and Exclusion Criteria

As

Table 1. Inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
Available in full text	Full text unavailable electronically
Published between 2000 and 2024	Outside of the 2000–2024 timeframe
Written in English	Written in other languages than English
Related to the research topic (communication, fake news and tourism)	Vaguely or not related to the research topic
Published in the three selected databases (Web of Science, Google Scholar and Scopus)	Research without the description of data sources and methodology

shows, we have compiled a list of inclusion and exclusion criteria for the selection process. Only papers that were available in full text online and were published after 2000 were considered. All papers are written in English, related to the research topic (communication, fake news, and the tourism and hospitality industry). As mentioned before, all papers selected were published in the three databases used by the authors. First, titles and abstracts were checked for relevance, then the full texts were analysed according to the inclusion and exclusion criteria.

After careful selection based on the abstract screening, a total of 54 papers remained. Of the 54 articles retrieved for full-text review, 15 were excluded following a detailed screening. In most cases, articles first appeared to meet the criteria; however, several of them were not available in full text online or were only partially available in English. In some instances, their primary focus was outside the scope of this literature review (only mentioned the tourism and hospitality industry). As a result, only a total of 39 studies were selected to be included in the current article.

3. Results

The results of the systematic literature review show that although there is significant academic material on fake news itself, the tourism and hospitality industry is somewhat underrepresented in this matter. The papers were published in respected tourism and hospitality journals, showing the relevance of fake news as an emerging topic in the industry. Most of the papers included in the SLR were published in the Journal of Travel Research (5 publications), the International Journal of Hospitality Management (4), and Tourism Management (4). This shows growing interest in the topic in the wider industry.

The papers used multiple methodologies: qualitative, quantitative, and mixed methods were all applied in at least one. Out of the total 39 studies, 19 papers used qualitative methodology, out of which a total of 3 were systematic literature reviews. These studies analyse existing papers and knowledge, without collecting new empirical data. 16 studies utilised quantitative methodology, and 4 studies employed mixed methodology. This means that 48% used qualitative (16% of which were systematic literature reviews), 41% used quantitative, and 11% used mixed methodology.

In a total of 6 articles, survey-based methods were used, which mainly used structured questionnaires (often paired with 5 or 7-point Likert scales) to examine user perception, trust, and behaviour. Computational approaches (4 articles) applied machine learning, big data analytics, and scraping techniques to detect fake content and analyse large-scale patterns in user-generated reviews. The same number of articles (4 articles) used interviews, phenomenological research, and comparative analysis, typically to explore underlying processes. Statistical modelling and simulation techniques were used in 3 studies to examine relationships between variables and/or to predict outcomes. Experimental methods appeared in 2 studies, which tested user responses to manipulated reviews.

3.1. Chronological Distribution of the Articles

One of the selection criteria was the publication date: only articles published between 2000 and 2024 were considered for inclusion in the list, and one single article was also included from 2025. However, as Figure 2 and Appendix A show, the first paper found in the topic only dates back to 2009, most probably because fake news became a more pressing issue with the widespread use of social media platforms. Articles dealing with fake news in the tourism and hospitality industry surged right before and after ‘fake news’ became Collins Dictionary’s word of the year in 2017. However, most relevant articles were published on fake news in the tourism and hospitality industry in 2020 and 2021, with 12 and 9 research articles dealing with the phenomenon, respectively. The spike in interest toward fake news is probably linked to the COVID-19 pandemic, which caused a significant increase in fake news stories and misinformation and had a great effect on the tourism and hospitality industry as well.

3.2. Topical Distribution of the Articles

Studies were also categorised based on their ‘research context’, or topical distribution, which means the specific segment of the tourism and hospitality industry each of them addresses. Papers that were categorised in the tourism sector examine topics such as destinations, travel behaviour, and destination marketing. Studies related to the hospitality sector analyse issues connected to accommodations, restaurants, and other hospitality services. Some papers address the tourism and hospitality industry as a whole, with broader topics such as social media influence, influencer marketing, crisis communication, destination reputation, or policy issues. It is also important to add that the great majority of the articles, a total of 56% (22 studies), were related to fake reviews in the hospitality industry, mainly dealing with hotels, services, restaurants, and booking agents (e.g., Booking.com and review sites such as Yelp or TripAdvisor). As Figure 3 shows, the main themes that the 39 selected articles summarise.

3.3. Geographical Distribution of the Articles

As Figure 4 shows, the geographical distribution of the articles is quite wide. However, most studies (a total of 16 articles) did not have a specific geographical scope. These articles were mostly literature reviews and conceptual papers. Many qualitative studies take a broader or more general view, sometimes discussing global patterns or examples without focusing on a single country.

At the same time, it is clearly visible that geographical distribution is more relevant in quantitative research, as most of the time it is the location where the data was collected. Most of the analysed literature measured the impact of fake news, or fake reviews, focused on Asia, and specifically China (e.g., Beijing, Shanghai), as well as the United States (e.g., Boston, Las Vegas, or the 75 largest U.S. cities). A number of articles also adopted a multi-country or global perspective, analysing data from regions such as 12 international markets, former Soviet states, or 56 countries. Single-country case studies included Singapore, Italy, Greece, Ethiopia, Mexico, and Zimbabwe.

4. Discussion

After selecting the 39 articles and analysing some of the traits they have, including the chronological, topical, and geographical distribution, we explain the main ideas and findings of each article in more detail below.

4.1. Articles Focusing on Fake Reviews in the Hospitality Industry

Firstly, using co-topic analysis, a paper identified five major categories for fake news research relevant to the hospitality industry and fake reviews: political, social media, credibility detection, diffusion, and social impact (Abedin et al. 2020).

A paper’s results show that negative electronic word-of-mouth (eWOM) causes strong avoidance behaviour in tourists (Duong et al. 2025). A study conducted by Mayzlin et al. (2014) concluded that hotels located next to smaller, independent competitors tend to receive more negative reviews, which may suggest manipulation.

Most of the articles focusing on fake reviews in the hospitality industry dealt with the experiences of hotels. In this sector, fake reviews can be especially harmful and damaging, as they can cause significant distrust among travellers. However, it is really interesting to see that consumers are more likely to believe positive reviews when it comes to luxury hotels, while they trust negative reviews more easily and faster for budget accommodations (Banerjee 2022).

Fake reviews tend to have catchy titles, and they blend informative and subjective content that people appreciate more than official information. Writing them is not difficult, while the creation of fake positive and neutral reviews sometimes needs more work and research (Banerjee and Chua 2014). Even so, some hotels, restaurants, and products try to increase their visibility by adding fake positive reviews. With just 50 more positive reviews, visibility grows significantly, which is a budget-friendly self-promotion (Lappas et al. 2016). One of the papers claims that positive reviews boost hotel bookings, while mixed reviews or a variance in reviews reduces popularity (Ye et al. 2009).

Although it is hard to filter out fake reviews, authors of these often avoid the use of personal pictures: they rather use anonymous or unrelated pictures to avoid detection, which shows that they are aware of the misconduct (Fong et al. 2021). However, a study claims that reviewer attributes of fake identity and ulterior motives were found to drive distrust in online hotel reviews, particularly in hospitality settings (Ahmad and Sun 2018). Research on fake review detection is now dominated by algorithms and sentiment analysis. However, in the future, more research will use advanced technology, including blockchain (Reyes-Menendez et al. 2019) and AI. Zhang et al. (2016) suggest that incorporating nonverbal reviewer traits improves fake review detection models. The study highlights how these traits are more impactful than verbal ones in identifying deception.

The selected articles also mentioned restaurant reviews: newly opened restaurants tend to receive less extreme sentiment in reviews compared to ones that have been open for a longer period of time (Hlee et al. 2021). This may also include fake reviews and suggest that new restaurants are less likely to suffer from negative opinions, because of the initial trust and goodwill of customers. Another paper found that around 16% of Yelp reviews are filtered because they are too extreme. It also suggested that review fraud is more common in restaurants, which have a weaker reputation or are under competitive pressure (Luca and Zervas 2016).

4.2. Articles Focusing on Fake News in the Tourism Industry

Similarly, existing literature on fake news in the tourism industry (destinations, destination management, etc.) was analysed and synthesised during the research process. Some papers focused on fake news in the tourism industry in general, analysing how disinformation may affect the image and reputation of a destination, and on tourist behaviour (Abdallah 2021). In a paper, four main themes were identified in tourism and hospitality-related fake news: epistemic crisis, food myths, health scares, and deceptive travel narratives (Vasist and Krishnan 2022). The same authors also found that disinformation significantly impacts tourism, and the phenomenon needs thorough research and different research approaches (Vasist and Krishnan 2023).

Another study found that social media also influences travel decisions (especially right after holidays), and travellers find user-generated content (UGC), which may include fake reviews, more trustworthy than official tourism websites (Fotis et al. 2012).

It was also stated that young adults, i.e., people aged 18–35, are more likely to choose destinations and get information from social media (Berhanu and Raj 2020)—they also tend to trust the gathered information; however, this trust decrease over time, as they age. Social media influencers also have an effect on tourist decision-making; however, it seems that when it comes to the decision between influencer marketing and traditional marketing tools, rural destinations benefit more from the former (Chatzigeorgiou 2017).

Another study collected several case studies from around Latin America, where fake news stories about destinations led to serious consequences in the tourism industry. The authors stated that a decline in visitor numbers, as well as the damaged reputation of the destinations, were noticeable after the spread of fake news (Anton et al. 2020). At the same time, a paper examined the decline in tourism in Mexico, following travel warnings concerning crime rates in the country, which turned out to be exaggerated or even false (Vidriales 2020). A third study focused on disinformation on weather and climate damage issues, and the effects they have on tourism (Tham and Chen 2022).

As stated before, fake news research in the tourism industry spiked in the years of the coronavirus pandemic, likely because misinformation in connection with COVID-19 spread fast on social media sites (Williams et al. 2022) and not only influenced public risk perception, but travel behaviour as well.

Unfortunately, relatively few studies focus on how destination management organisations may combat fake news and repair the image of the country/area/city, but Johnson (2024) thinks that it requires spatially aware, real-time strategies incorporating context and proactive debunking.

Finally, in his paper, Fedeli (2019) already identified a research gap and emphasised the need to investigate the implications of fake news within tourism, beyond just the scope of fake reviews. However, there are still limited academic resources available on this subject; thus, there is a substantial opportunity for further exploration. Zeng and Gerritsen (2014) also agree that social media’s impact on tourism remains under-explored, and its value to marketing, local communities, and overall tourism management needs fuller investigation.

5. Limitations and Future Research Paths

Like all research, ours is also not without limitations. As the literature gap shows, there are only a limited number of studies have been found which discuss tourism destinations, thus it is hard to find quotes and references from academic sources, case studies and quantitative research on the topic. Thus, a major limitation of this research is the need to include more publications from mainstream media platforms (news portals, television, and radio) to get a relevant, thorough picture of fake news in the industry. Although these sources are valuable, they may lack the methodology and the rigour of academic publications, which are grounds for omitting them from the systematic literature review. The literature review was also limited to articles published in English; thus, we may have excluded relevant studies written in other languages. As the search was restricted to only three databases (Scopus, Web of Science, and Google Scholar), it may not capture all relevant articles, including industry reports and government papers. Although consistent exclusion/inclusion criterion was used, the subjective, manual nature of the full-text screening may have introduced selection bias, especially in cases where tourism or hospitality was not the primary but a secondary focus.

The SLR also excluded some interdisciplinary aspects, such as psychology (the research on tourist behaviour), as well as crisis strategies (tools and good practices), which are also important factors in dealing with fake news. Future research may elaborate on these aspects as well.

Other possible future research paths can focus on underrepresented regions, such as Africa, South America, and the Middle East, where although tourism plays a significant role, it is still under-researched. Thus, methodologically longitudinal designs, experimental studies, and comparative cross-national research would also add to the field to better understand the dynamics and impact of fake news in the industry. Studies could also explore the dissemination of fake news beyond traditional social media platforms, such as TikTok, messaging apps and AI-generated content as well.

In conclusion, exploring fake news in the tourism and hospitality industries is essential, as these sectors heavily rely on accurate and reliable information to shape consumer behaviour, expectations, and experiences. The spread of fake news can significantly impact perceptions of destinations and services, ultimately influencing customer satisfaction and purchase decisions. Addressing this issue requires further research to establish a strong theoretical foundation, as well as practical efforts such as education campaigns to help businesses and consumers identify and mitigate the effects of false information.