Skip to Content
MDPIMDPI
Search
Social SciencesSocial Sciences
PDF
  • Article
  • Open Access

28 November 2025

Young People and the Challenges of Disinformation: An Analysis of the Spanish Situation (2022–2023)

Order Reprints
,
and
Department of Journalism, Faculty of Communication Sciences, University of Malaga, 29010 Malaga, Spain
*
Author to whom correspondence should be addressed.
Soc. Sci.2025, 14(12), 690;https://doi.org/10.3390/socsci14120690
Version Notes

Abstract

Given their widespread use of social media—the preferred channel for spreading fake news—to keep up with current events, young people are the demographic most exposed to disinformation. In this article, we analyse young Spaniards’ perceptions of the phenomenon, as well as their perceived ability to recognize fake news, taking into account the influence that certain independent variables (age, gender, province, environment, level of education, and political ideology) may have on the results. The fieldwork is based on two nationally representative surveys (n = 2932) conducted in 2022 and 2023. Several interesting findings can be drawn from the data: (a) a change in the identified main source of disinformation (traditional media in 2022 and the TikTok network in 2023); (b) an increase in misleading content related to current political events; (c) a moderate decline in young people’s ability to recognize fake news; and (d) a decline in the habit of verifying dubious information. In all of the cases, most of the independent variables considered in this study also had a significant impact, with particularly substantial differences being recorded depending on the gender, educational level, and ideology of the respondents.

1. Introduction

The introduction of new technologies and the digitization of content have led to a profound transformation in information and entertainment habits worldwide (Benaissa 2024; Farias-Batlle et al. 2024). Social media and messaging apps have become some of the main sources of news, to the detriment of traditional media (Dabbous et al. 2022). The latest edition of the Digital News Report Spain (Sierra et al. 2025) shows that 46% of the population uses social media—mainly WhatsApp, Facebook, and YouTube—as their source of information while also revealing a gradual decline in the use of traditional media such as television and, particularly, newspapers. This is linked to the abrupt loss of media credibility: only 31% of Spaniards trust the news (Sierra et al. 2025), which is also linked to the rise in influencers as a source of information (Newman 2024).
Although this phenomenon affects the entire population (European Parliament 2025; Shearer and Gottfried 2017), it is particularly prevalent among younger generations, who mainly communicate and engage in leisure activities using the digital environment (Rideout et al. 2022). This is somewhat predictable, since, as Garrote-Rojas et al. (2018) point out, we must not forget the context in which young people have grown up—or are still growing up—which is completely digitized. Other authors express similar views, warning of the progressive distancing of young people from conventional journalistic content (Thurman and Fletcher 2019), a trend that, according to the latest data, seems to be accentuated in the case of Spain. According to the 2024 Youth Survey (European Parliament 2025), social media is the main source of information for 49% of Spanish people aged between 16 and 30, 7 points above the EU average of 42%.
In this landscape of profound media transformation, the phenomenon of disinformation has developed at an overwhelming rate. Its popularization and rise over the last decade mean that it cuts across all fields and disciplines (Ho et al. 2022; Pérez-Escolar et al. 2023), threatening to undermine the foundations of democracy and freedom of expression (Huber et al. 2022; Sádaba and Salaverría 2023). Given that, on the one hand, social media and messaging apps are the main channels through which fake news is disseminated (Naeem et al. 2021; Sierra et al. 2025) and on the other, that young people are the main consumers of these channels (Reuters Institute 2023), it is necessary to determine if they are the group most exposed to the dissemination of fake news. The growing flow of fake news, coupled with a lack of tools or training to distinguish false information from true information (Heram and Dagatti 2022), could have unpredictable consequences for younger generations, increasing their vulnerability to manipulation and lies (Altay et al. 2023).
In this article, we analyse Spanish youth’s perception of disinformation and how it has evolved in recent years, paying particular attention to how young people deal with the phenomenon and the influence that sociodemographic factors such as age, gender, educational level, political ideology, and environment have on their attitudes.

1.1. The Rise of Fake News and Its Persuasion Techniques

Although the spread of fake news for spurious purposes can be identified at any point in history, the widespread use of social media has led to a rise in disinformation over the last decade. Since 2016—a turning point for the phenomenon following the US presidential election and the Brexit referendum (Allcott and Gentzkow 2017; Tandoc et al. 2021)—its effects have been felt in countless processes and areas (Apuke and Omar 2021; López-Martín and Córdoba-Cabús 2024). The maelstrom created by fake news has reshaped the way audiences approach current affairs, giving rise to an informationally uncertain global scenario which some authors have termed the “post-truth era” (Bennet and Livingston 2018; Lewandowsky et al. 2017).
Although some researchers question the real influence and destabilizing power of fake news (cf. Margolin et al. 2017), the vast majority highlight the influence of these messages in establishing “issues, trends, and stances in public debate” (Hernández-Conde and Fernández-García 2019, p. 36), as well as in reinforcing the perceptions and beliefs of the population (Casero-Ripollés et al. 2023; Kapantai et al. 2021). In general terms, the proliferation of misleading content intensifies in crisis situations (Pérez-Curiel and Velasco-Molpeceres 2020), with the COVID-19 pandemic acting as a definitive example; such was the volume of fake news on the Internet that various authors described the situation as an “infodemic”, or information “epidemic” (Salaverría et al. 2020; Vaezi and Javanmard 2020).
The avalanche of disinformation has, as expected, recently led to growing academic interest in the subject, particularly the social sciences and the field of communication (Broda and Strömbäck 2024; Raponi et al. 2022). Although there is still a lack of consensus regarding the conceptualization and definition of the limits of the phenomenon (Baptista and Gradim 2022; García-Marín and Salvat-Martinrey 2022), there is one feature that researchers unanimously identify as inherent to fake news: these are messages that are intended to deceive and confuse the recipient (García-Marín 2021; Tandoc et al. 2018; Wardle and Derakhshan 2017). Gutiérrez-Coba et al. (2020) even consider that, although this content can be produced without any explicit harmful intent, it “ends up misinforming” (p. 238).
The success of fake news is largely due to the techniques used to construct it, which are practically universal: the most common are the prominence of audiovisual elements (Thomson et al. 2022), the absence of identified sources in the stories (Malquín-Robles and Gamir-Ríos 2023), and the use of emotional language that appeals to negative feelings such as fear, panic, or anger (Shariatmadari 2019; Tandoc et al. 2018). Similarly, it is common to adopt the aesthetics of journalistic reporting (Bastick 2021), using this appearance to reinforce the narrative by appropriating the trust that the public places in the impersonated media outlet.
The combination of a credible appearance and an appealing, emotionally charged narrative often allows fake news to spread faster than real news (Vosoughi et al. 2018). This, coupled with the multiplicity of channels through which it is disseminated (Imaduwage et al. 2022; Salaverría et al. 2020), gives this content enormous and difficult-to-control expansive capacity.
However, the phenomenon of disinformation is constantly changing. Digital advances enable the development of new and more sophisticated deceptive strategies that make detecting fake news even more complex. In this regard, the use of artificial intelligence to generate hoaxes (Rivas-de-Roca et al. 2025) stands out, constituting a “qualitative leap” (López-López et al. 2023). Although AI technology is still in its infancy (García-Marín and Salvat-Martinrey 2023), some of its implications can already be glimpsed with the emergence of new extreme-realism formats such as deepfakes (Hameleers et al. 2022; Weikmann et al. 2024). Similarly, the public’s propensity to explore new social networks or instant messaging applications gives rise to the reconfiguration or adaptation of misleading messages: this explains the rise in audiovisual formats as a means of disinformation, linked to the growing incidence of image-based social media such as Instagram or TikTok (Hoai-Lan and Minh-Tung 2024).
The credibility given to fake news is largely due to psychological mechanisms deeply rooted in its audience. As argued by Baptista et al. (2021), citizens tend to “seek out information that is compatible with their worldview (opinions, values, or beliefs), even if it is false” (p. 25). In fact, numerous studies have found evidence of the predisposition of populations to give greater credibility to messages that are in line with their ideology (cf. Freiling et al. 2023; Kappes et al. 2020; Thornhill et al. 2019), which in psychology is referred to as confirmation bias (Cinelli et al. 2021; Iandoli et al. 2021). Political affiliation also seems to influence fake news: individuals on the right of the ideological spectrum are more likely to believe misleading content and share it (Guess et al. 2020; Hinsley 2021).

1.2. Young People and Disinformation

The fact that social media is the main channel through which young people use to socialize places them in a more vulnerable position when it comes to disinformation (Gómez-Calderón et al. 2020). Although authors such as Huber et al. (2022) and Valera-Ordaz et al. (2022) focus on the adult population, which they consider to be the segment of citizens most likely to give credibility to misleading content due to their lower level of technological training, the bulk of the scientific literature identifies young people as the group most exposed to the phenomenon (De Ávila et al. 2024; Gómez-Calderón et al. 2023).
Their greater vulnerability appears to be linked to the amount of time they spend on social media, the main channel through which disinformation is disseminated (Altay et al. 2023; Wang et al. 2025). In addition to this high level of digital exposure, other authors such as Dumitru (2020) and Flynn and Lastra-Anadón (2025) identify a low motivation to verify content, as young people rarely show concern for the reliability of a source.
Recent findings (Kops et al. 2025) reinforce the difficulties young people face in detecting false information, often ignoring it rather than fact-checking. Various factors shape this attitude, including educational level, cognitive biases, and the influence of peers and the surrounding environment. In the Spanish context, young people acknowledge that they often accept messages as accurate even when they are not (Mendiguren et al. 2020).
The psychological need among young people to keep their close circles immediately informed, combined with a lack of time to engage in verification tasks, tends to foster their greater propensity to share misleading content (Talwar et al. 2020). In fact, numerous studies (McGrew et al. 2018) show that being digital natives does not mean—contrary to what one might think—that young people are better equipped to detect fake news. Quite the opposite, in fact, as young people are still in the process of cognitive maturation.
With regard to group-specific training on disinformation, there is some divergence in the literature, as well as a marked gap depending on age and educational level (Guess et al. 2019; Pennycook and Rand 2019). While compulsory education students rarely question the messages they receive (Dumitru 2020; Loos et al. 2018; Pilgrim et al. 2019), university students maintain the opposite attitude; in fact, García et al. (2021) estimate that 53.9% of this group have a medium or high ability to identify hoaxes circulating on the Internet. Similarly, according to the 2024 Youth Survey conducted by the European Parliament (2025), older young people and those who have completed post-secondary or higher education are the most confident in their ability to recognize disinformation when they receive potentially misleading messages. In addition to training, the gender of respondents also appears to have an influence here (men report a higher self-perception of their verification skills than women) as does geographical location: the larger the area, the better the training (European Parliament 2025).
It has repeatedly been found (European Commission 2024; European Parliament 2025; Pérez-Escoda et al. 2021) that young people consider themselves sufficiently prepared to detect fake news, something that some authors have identified as “immunity syndrome,” which is partly illusory (Galarza-Molina 2023; Jang and Kim 2018). This perception is transnational in nature: not surprisingly, 69% of European youth consider it “easy” to recognize fake news (European Commission 2024). In the specific case of Spain, the rate stands at 67% (European Parliament 2025).
Exposure to disinformation is, in any case, very high. According to the EU, 90% of young Spaniards receive fake news at least several times a month, which is above the EU average of 76% (European Parliament 2025).
As for the sources of this content, there is a clear predominance of social networks and messaging applications: mainly WhatsApp and X, and, to a lesser extent, Instagram. Facebook has lost prominence, likely due to the migration of young audiences to new channels (cf. Herrero-Diz et al. 2020; Mendiguren et al. 2020). Most young Spaniards say they check the messages they receive if they suspect they may not be reliable (Catalina et al. 2019); however, young people make very little use of fact-checking services and verification platforms (Pérez and Pedrero 2021).
For most researchers, the outlook is not encouraging. Disinformation is a major challenge for the younger generations, requiring joint and sustained action by institutions and citizens (Casero-Ripollés et al. 2023). For this reason, many authors (cf. Morató-Beltrán et al. 2022; Palau-Sampio 2018) highlight the need to implement measures aimed at teaching media literacy to the population from an early age, which would contribute to giving them greater autonomy and a critical attitude (Golob et al. 2021).
In relation to our object of study, the perspectives offered by the so-called digital media ecology theory (Scolari 2012) are particularly relevant. This theory conceptualizes communicative environments as ecosystems in which ways of knowing, interacting, and participating socially are shaped. From this perspective, the challenges young people face regarding disinformation are not merely technological but also cognitive, cultural, and democratic, as they involve navigating a hybrid informational landscape where traditional journalism and media coexist alongside user-generated content that lacks verification or oversight (Román-San-Miguel et al. 2022; Altay et al. 2023).
Within this context, disinformation not only undermines trust in institutions and media but also weakens public deliberation and tests the critical capacities of audiences (Bennet and Livingston 2018; Baptista et al. 2021). Digital media ecology provides a framework for understanding disinformation not solely as an individual issue of recognition or verification, but as a structural challenge inherent to the contemporary communicative ecosystem marked by polarization dynamics, attention economies, and participatory cultures that shape informational practices and youth vulnerabilities (Rodríguez-Pérez and García-Vargas 2021; Espinoza-Guanilo 2024).
Our research further draws on the dual-process theory (Tversky and Kahneman 1974; Kahneman 2011), which distinguishes between two modes of content assimilation: System 1, characterized as fast, intuitive, and emotional; and System 2, which is slower, more analytical, and deliberative. In social media contexts, System 1 predominates, facilitating uncritical acceptance of messages. Thus, the reception and credibility of disinformation are not solely due to gaps in knowledge or education but are also the outcome of automatic cognitive mechanisms amplified by emotional responses (Van der Linden et al. 2025) and the algorithmic functioning of platforms (Baptista et al. 2021).
Similarly, the functionalist uses and gratifications theory (Blumler and Katz 1974), alongside subsequent developments by McQuail and Windahl (1993), provide a robust framework to analyze young people’s engagement with social media in the context of disinformation. This theory posits that users are not passive recipients of media messages but active agents who select platforms and content according to their motivations, including the need for social interaction or human companionship (Martínez 2010). In contemporary digital ecosystems, social media satisfies these gratifications instantly while simultaneously exposing users to constant flows of unverified information. Consequently, the desire for belonging, recognition, or interaction may increase the likelihood of sharing content without verifying its accuracy (Apuke and Omar 2021).

2. Materials and Methods

This research was designed to determine the impact of disinformation among young Spaniards aged 15 to 24, according to their own perceptions, and the evolution of the phenomenon between 2022 and 2023. The authors set three subsidiary objectives: to understand how young people access misleading content (O1), to identify its most common topics (O2), and to determine the extent to which young people verify information of dubious credibility, taking the most frequently used sources of verification into account (O3). In all cases, the possible influence of the respondents’ sociodemographic characteristics of the respondents, as well as the evolution of these characteristics during the study period, were also taken into account.
This research was conducted using a questionnaire-based survey. This method enables the collection of data from a representative sample of the population through standardized procedures, and is a fundamental tool in social science research for predicting, describing, and explaining the characteristics of a defined population (Sierra-Bravo 1994). Its use is particularly justified in communication studies, as it allows for rigorous research with large datasets (Igartua 2006) and facilitates longitudinal studies, as exemplified in this case (Casas-Anguita et al. 2003).
The survey was administered over two consecutive years (2022 and 2023), targeting the Spanish population aged between 15 and 24. This amounted to 4,887,773 people on 1 January 2022 and 5,065,796 on 1 January 2023. Both surveys were based on a sample proportional to the strata of the population under study after applying the corresponding weighting coefficients. The confidence level was set at 95%; the total number of individual respondents was 2932, with margins of error of ±2.67% (2022 survey) and ±2.27% (2023 survey).
The 2022 sample consisted of 1067 individuals, with an average age of 19.98 years (ME = 20; SD = 2.20), divided into those aged between 15 and 19 (48.4%) and between 20 and 24 (51.6%). The majority had completed secondary education, corresponding to the upper levels of compulsory secondary education (3rd and 4th years), high school, or advanced vocational training (52.2%). The 2023 sample, composed of 1865 individuals, showed similar results: an average age of 19.9 years (ME = 19; SD = 2.34), 51.5% of individuals between 15 and 19 years old and 48.5% aged 20 and above, and 55.3% of respondents with secondary education.
The distributions of the other variables are shown in Table 1.
Table 1. Distribution of the sample by gender and autonomous community of residence.
The questionnaire consisted of ten questions, with both single and multiple answers, and was administered online by a polling company hired for this purpose between 12 and 23 December 2022 (first survey) and between 17 November and 6 December 2023 (second survey). The sample was selected using a random distribution system among individuals belonging to a research panel, with profiles and characteristics previously defined.
The questionnaire was designed to assess how frequently young people seek out current news, the topics they follow, the disinformation they encounter, and the credibility they assign to content across various social media platforms. Participants were also asked about their ability to identify fake news, the frequency with which they verified information, and the methods they typically used for fact-checking. Notably, the questionnaire was previously validated through a pilot study conducted in 2021, which involved a sample of 1066 individuals from across the country.
For descriptive and inferential analyses, we used the statistical software SPSS (v28.0), which allowed us to verify the association between the dependent and independent variables of this study based on the data matrix generated with the records obtained1. In all cases, the chi-square (χ2) contrast statistic was calculated and, where deemed necessary, Spearman’s correlation test was applied to verify the consistency of the association between variables.

3. Results

3.1. Sources and Frequency of Disinformation

In 2022, young people consumed information frequently on social media platforms such as Instagram, TikTok, and, to a lesser extent, WhatsApp and YouTube. In contrast, applications such as Twitch, Facebook, Telegram, and Spotify were significantly less commonly used to search for news.
This trend, although with certain statistically significant changes, remained stable in 2023. The most notable changes are the increase in information consumed on TikTok [χ2 (5, N = 2567) = 11.477, p = 0.043] and the decline of YouTube [χ2 (5, N = 2567) = 37.548, p = 0.000], WhatsApp [χ2 (5, N = 2567) = 14.820, p = 0.011], Telegram [χ2 (5, N = 2567) = 20.146, p = 0.001], and Facebook [χ2 (5, N = 2567) = 16.023, p = 0.007], networks that already had low informational use, which seems to indicate that this is an upward trend.
The platforms most frequently consulted for information tended to be those with the highest rates of fake news dissemination, although significant changes were detected with regard to the sources of this content [χ2 (11, N = 2662) = 47.564, p = 0.000]: thus, in 2022, the most cited channel was traditional media (21.2% of respondents), while in 2023, TikTok led the way (23.8%), although traditional media remained in second place (18.8%) (Table 2).
Table 2. Main channels for accessing disinformation *.
In order to contrast the relationship between information consumption and the reception of fake news on social media, a contingency table was created, showing a statistically significant association between both variables [χ2 (25, N = 2932) = 223.578, p = 0.000]. This is a positive relationship, since the greater the frequency of searches, the greater the exposure to fake news.
Spearman’s test showed that the correlation between the two variables (searching for information and receiving fake news) was statistically significant, although not conclusively [rho = 0.188, N = 2932, p = 0.001], suggesting that there must be other factors influencing the reception of misleading content.

Disagreements Regarding the Origin of Disinformation

When relating sociodemographic variables to the means by which disinformation was accessed, significant differences were found in terms of gender [χ2 (11, N = 2666) = 59.938, p = 0.000]. Boys mainly received this content through mainstream media (21.1% of responses), followed by TikTok (18.60%), influencers (11.5%), and Instagram (11.5%). Girls mainly cited TikTok (23.5%), followed by general media (18.6%), influencers (17.4%), and Instagram (10.9%).
Age also had a significant influence [χ2 (11, N = 2663) = 34.922, p = 0.000]. In this case, younger people (aged 15–19) mainly received disinformation through TikTok (23.8%), followed by general media outlets (16.6%). In the older age group (20–24), however, the order was reversed, as misleading content was mainly received from the media (22.10% of incidents, compared to 18.4% from TikTok).
Likewise, the environment was revealed to be a significant variable [χ2 (11, N = 2667) = 25.865, p = 0.007]. Although the main channels for distributing disinformation did not differ between municipalities with more than 10,000 inhabitants and those with less (TikTok, general media, and influencers), there were variations in the percentages. TikTok was a more prominent source in towns with fewer than 10,000 inhabitants (24.2%), compared to those with more than 10,000 (20.3%). In the latter, however, mainstream media and influencers slightly exceeded the figures obtained in smaller municipalities (19.6% and 14.5%, compared to 18% and 13.5%, respectively).
Educational level also showed a clear association with access to disinformation [χ2 (44, N = 2665) = 122.191 p = 0.000]. For young people in secondary education, the main source was TikTok (20% and 22.9% of responses, respectively), while for those in secondary and tertiary education, it was the general media (indicated by 21.7% of respondents in the first group and 20.5% in the second).

3.2. Most Common Fake News Topics Received

Table 3 shows the most common topics in the fake news received by respondents in 2022 and 2023. As can be seen, politics, celebrity gossip, and, to a lesser extent, current events were the most common areas, and although not many differences are present between years, an increase in messages related to politics can be seen.
Table 3. Topics of fake news received *.
This variable was influenced by the gender of the respondents [χ2 (4, N = 2663) = 309.674, p = 0.000]. For women, disinformation was mainly related to celebrity gossip (38.7% of responses), followed far behind by politics (19.9%) and current events (15.9%). In contrast, men mainly received content about politics (28.3%) and sports (15.6%), followed by social commentary and current events in equal proportions (14.3%).
Level of education [χ2 (16, N = 2663) = 99.622 p = 0.000] also had an impact on the topics of the misleading content received. Once again, celebrity gossip, politics, and current events were the predominant topics in all educational categories except among respondents with “other” levels of education, for whom politics and current events were the main topics (20.5% of incidents in both categories), followed by sports in this case (15.4%).
Population density also led to significant differences [χ2 (8, N = 2663) = 21.233, p = 0.007] between young people living in municipalities with fewer than 10,000 inhabitants—for whom celebrity gossip (30.3%), politics (19.3%), and events (12.5%) were the predominant topics of disinformation—and those who lived in larger urban centres, who received more political (25.3% of responses) and social (25.2%) content.
Finally, ideology appears to have a significant influence on the thematic divergences recorded [χ2 (24, N = 2661) = 69.320, p = 0.000]: thus, respondents on the right of the ideological spectrum received more fake news related to sports (13.2% of the total, compared to 7.6% of left-wing respondents) and on political issues (26.1% compared to 23.4%).

3.3. Recognition of Disinformation and Frequency and Methods of Verification

The data reflect a moderate decline in young people’s ability to detect disinformation during the period analysed [χ2 (4, N = 2934) = 10.045, p = 0.040]. In 2022, 3.7% of respondents rated themselves on the lowest level of the applied Likert scale (1), while in 2023, the figure rose to 4.7%. In total, the percentage of respondents who rated themselves on level 2 rose from 10.8% to 13.9%, and those on level 3 rose from 42.4% to 42.9%. Conversely, the highest options decreased: the percentage of respondents rating themselves on level 4 reduced from 32.5% in 2022 to 29.3% in 2023, and from 10.6% to 9.3% for those on level 5 (Table 4).
Table 4. Ability to detect disinformation *.
Various sociodemographic variables influence this process, including educational level [χ2 (16, N = 2933) = 206.984, p = 0.000]. On average, young people who had completed higher education cycles more frequently ranked themselves on the upper levels of the scale (4, 5), while the lower levels (1, 2) were chosen more frequently by respondents with less education (Table 5).
Table 5. Ability to detect disinformation according to level of education *.
Gender also affected the ability to detect disinformation [χ2 (4, N = 2933) = 27.932 p = 0.000]. Men tended to score themselves on levels 4 (33.7% of responses) and 5 (11%) more often than women (26.9% and 8.4%, respectively); conversely, women more commonly chose the lower values on the scale (2 (14.2%, compared to 11.4% of men) and 1 (4.8% compared to 3.9%)).
Socioeconomic status also showed a clear influence [χ2 (8, N = 2933) = 61.387 p = 0.000]. Respondents in the upper or upper-middle classes ranked themselves on levels 3, 4, and 5 more frequently (29.07% of occurrences) than those in the middle (27.43%) and the lower-middle or lower classes (25.73%). Conversely, lower-class young people ranked themselves on levels 1 and 2 to a greater extent than other respondents (11.4% of responses, compared to 8.9% of middle-class individuals and 6.4% of upper-class individuals).
Ideology also had a significant impact on the differences recorded [χ2 (12, N = 2933) = 117.933, p = 0.000]. Respondents on the right considered themselves to be better able to recognize misleading content (13.6% chose 5 on the Likert scale, compared to 6.6% of young people on the left). Similarly, only 2.6% of this group selected 1, the lowest value, compared to 8.1% of left-wing respondents.
The frequency with which young people verified information they found suspicious, as shown in Table 6, declined between 2022 and 2023.
Table 6. Frequency with which young people verify news stories they consider misleading *.
In this case, gender [χ2 (4, N = 2930) = 11.355, p = 0.023] and age [χ2 (4, N = 2932) = 10.718, p = 0.030] were found to have an influence. Women were more likely to respond that they “always” (8.3% of responses compared to 7.5%) and “frequently” (22.7% compared to 19.2%) verified information compared to men, although they were also the most likely to “never” verify information (9.90%, compared to 8.8% of men).
In terms of age, it was found that older respondents (aged 20–24) verified information more regularly; 8.5% of these respondents reported “always” doing so compared to 7.2% of younger people, while 8.1% of 20–24-year-olds “never” verified compared to 10.6% of respondents under 20.
Social class [χ2 (8, N = 2932) = 46.123 p = 0.000] and level of education [χ2 (16, N = 2931) = 111.895 p = 0.000] also had a significant influence on this variable. The higher the socioeconomic status, the more frequent the verification: upper-class respondents “always” or “almost always” verified in 15.5% of cases, compared to those from the middle class, who do so in 14.9% of cases, and those from the lower class, who did so in 12.3% of cases. At lower frequencies, the opposite trend is observed.
In terms of education, it seems clear that the higher the level of education, the greater the habit of verification, albeit with fluctuations (see Table 7).
Table 7. Frequency of verification according to level of education *.
If we look at the two highest frequencies, we see that young people with tertiary education (second cycle) checked information in 16.6% of cases, compared to 13.4% at the level immediately below (tertiary education, first cycle). The opposite effect is also evident, as 13.7% of respondents in the first cycle of secondary education “never” or “almost never” verified, while only 4.5% of young people with higher education (master’s or doctorate) chose these options.
In addition, the frequency of verification was also influenced by environment [χ2 (4, N = 2933) = 9.457, p = 0.051]. The three highest frequencies were seen among young people living in municipalities with more than 10,000 inhabitants, who “always” checked the news in 8.3% of cases (the figure among respondents from smaller towns did not exceed 6.3%). Conversely, only 8.7% of this group “never” checked, a rate that rose to 12.4% among individuals from smaller municipalities.
When it comes to the channels young people used to verify information they did not trust, the Google search engine and specialized websites stood out. Furthermore, between 2022 and 2023, mainstream media gained ground and influencers declined in this regard (Table 8).
Table 8. Most common methods of verification.
This variable is mainly influenced by social class [χ2 (8, N = 2657) = 17.497, p = 0.025], environment [χ2 (4, N = 2656) = 10.151, p = 0.038], and ideology [χ2 (12, N = 2660) = 23.325, p = 0.025]. Based on social class, the rankings of verification channels did not differ from the general distribution; variations in percentages were mainly seen, slightly lower for specialized websites and traditional media among lower-class individuals, and higher for influencers in the same group.
However, when considering the respondents’ place of residence, the variations are greater. In all cases, the most common sources of verification were the Google search engine and specialized websites. However, while young people living in municipalities with more than 10,000 inhabitants turned to general media (12.5% of responses), family and friends (11%), and influencers (7.7%), individuals from smaller towns showed the opposite distribution: 12.8% used family and friends, 10.3% used influencers, and only 9.9% used conventional media.
Finally, ideological positioning also had a significant relationship with this variable. Young people on the left relied more on mainstream media to verify dubious information than those on the right (13.2% of responses, compared to 12.2%) in addition to specialized websites (28.8–24.4%), while those on the right more often turned to family and friends (13.4% of cases, compared to 9.6%).

4. Discussion

Disinformation is undoubtedly a key—albeit undesirable—component of the contemporary media ecosystem. This study, based on a large and representative sample, aimed to provide a rigorous approach to young Spaniards’ perceptions of the phenomenon and their exposure to it.
With regard to the channels through which misleading content is accessed (O1), the data obtained reflects a clear change: while in 2022, traditional media outlets were perceived as the main source of this kind of content, in 2023, that role shifted to TikTok. It is significant—and worrying—that traditional news outlets, the only professional sources analysed in this study, are perceived as sources of misleading messages compared to social media, where any user can broadcast whatever they wish, whether true or false. Equally noteworthy is the rapid growth of TikTok as a news consumption platform, as noted in the latest edition of the Digital News Report Spain (Sierra et al. 2025).
The results reveal a positive correlation between the frequency of news searches and the reception of fake news, which is logical.
Secondly, we set out to identify the predominant themes of disinformation received by young Spaniards (O2). The analysis indicates that fake news was mainly related to three areas: politics, social issues—understood as information referring to public figures and personalities linked to celebrity gossip—and events. This trend is consistent with the findings of Vosoughi et al. (2018), Mendiguren et al. (2020), and Pérez and Pedrero (2021), who agree that political issues are overwhelmingly the main focus of hoaxes in the digital environment.
However, data collected in 2022 ranked social content in first place; so, there are fluctuations in this area. The gradual increase in politically oriented disinformation observed in 2023 could reflect the growing polarization in national public debate. However, it may be the case that there was no actual increase in misleading messages, but rather an improvement in young people’s ability to detect them.
At this point, the contrast with the independent variables reveals significant differences based on the gender of the respondents. Thus, there was a higher incidence of receiving fake news related to social issues—and to a lesser extent, events and culture—among women. In contrast, men reported receiving disinformation related to politics and sports more frequently.
Ideological orientation also seemed to have a significant impact on the subject matter received, with those who considered themselves left-wing being more likely to encounter political topics, while conservatives reported being exposed to more misleading content related to sports.
Thirdly, the self-perceived ability of young people to detect disinformation was addressed, as well as their verification routines—when they practiced them (O3). Here, a negative change can be seen over time: between 2022 and 2023, respondents’ perceived ability to identify misleading news content declined. Looking at the independent variables in this study, there is a clear influence of sex, as men tended to report greater detection abilities than women. This, however, does not necessarily imply actual differences between the two groups, since we are referring throughout to self-perception. Respondents with higher levels of education, those with medium-high and high socioeconomic status, and those living in municipalities with more than 50,000 inhabitants, were also more likely to report a greater detection ability. This coincides, at least partially, with the previous findings of Guess et al. (2019), Pennycook and Rand (2019), Casero-Ripollés et al. (2023), Gómez-Calderón et al. (2023) and the European Parliament (2025).
Once again, ideology was a significant factor in this regard, as respondents on the right considered themselves to be better able to recognize fake news than young people on the left, who were less confident in this regard.
In line with previous scientific findings (Catalina et al. 2019), the practice of verifying dubious news among young people seems to be as common as the reception of such content itself, reaching more than 90% of respondents, although according to our data, the rate has fallen slightly between 2022 and 2023.
Information checking was more common among individuals with a high level of education and those living in densely populated areas, which is consistent with patterns of access to information resources and digital skills.
Likewise, women tended to check more frequently than men, and similarly, participants who identified with progressive views checked more frequently than those on the right—especially the far right—of the political spectrum. This ideological disparity in verification practices has also been observed in previous research, albeit with smaller samples (cf. Martín-Herrera and Micaletto-Belda 2021; Mendiguren et al. 2020).
When it comes to the sources used to verify news, respondents mainly used the Google search engine, specialized websites, and, to a lesser extent, traditional media outlets. Here, young people diverge from the general population, which, according to the Digital News Report Spain 2025 (Sierra et al. 2025), mainly uses the press as a resource for verifying information. In this regard, the education of young people makes a difference: those with more limited education preferred to turn to influencers or their personal circle (family and friends), while those who had completed higher education, whether university or vocational training, tended to use specialized platforms. Similarly, informal verification channels (YouTubers, personal contacts) were used more frequently by those who identified with right-wing ideological positions.
The analysis presented here shows significant changes both in the channels through which fake news is disseminated and in the verification habits of young Spaniards. Both findings highlight the constantly changing nature of disinformation and the advisability of studying it from a longitudinal perspective.
In this regard, the short period under study represents one of the clear limitations of this research. It would be advisable to extend the time frame to between five and ten years in order to strengthen the findings obtained, as well as to increase the number of items in the questionnaire, which was constrained in this study due to the large sample size.
Additionally, the results of this research are subject to certain biases, as they relied on respondents’ self-perceptions. This limitation could be addressed by incorporating questions designed to measure responses objectively—for instance, through tests for detecting fake news—which would allow future studies to refine the conclusions. Likewise, the inclusion of qualitative methods, not employed here due to time constraints, would provide a deeper understanding of the research subject.
Disinformation is a form of cognitive distortion for which audiences are often ill-prepared. The only antidote is media literacy, but its scope and pervasiveness—according to the available scientific literature—is currently very unequal. Young people are the ideal population in which to test the effectiveness of strategies to combat fake news, hence the importance of understanding their habits and abilities in relation to information.

Author Contributions

Conceptualization, B.G.-C.; methodology, Y.C. and B.G.-C.; investigation, Á.L.-M.; resources, Á.L.-M. and Y.C.; data curation, Y.C. and B.G.-C.; writing—original draft preparation, B.G.-C., Y.C. and Á.L.-M.; writing—review and editing, B.G.-C.; supervision, B.G.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research and the journal APC were funded by the State R+D+I Program Orientated to the Challenges of Society (Ministry of Science, Spanish Government). Grant Number: 50,000.

Institutional Review Board Statement

Ethical review and approval were waived for this study. In the Spanish context, research in the social sciences that does not involve clinical interventions or the collection of sensitive or identifiable personal data does not require evaluation by a biomedical or university ethics committee. According to current legislation, institutional ethical review is only mandatory in the cases defined by Law 14/2007, of July 3, on Biomedical Research, and by Organic Law 3/2018, of December 5, on Personal Data Protection and the Guarantee of Digital Rights, for studies that affect the health, integrity, or individual privacy of participants. In the case of our study, when applying for the national project from the Ministry of Science and Innovation of the Government of Spain, the Ministry evaluated and approved the project’s methodology and ethical implications in accordance with the criteria established in Order CIN/1337/2011, which regulates public calls for R&D&I projects and includes the review of ethical principles and scientific integrity. Therefore, following standard practice in the Spanish research system, and given that the project was evaluated and funded by a national public body that ensures compliance with the ethical research principles established by Spanish and European legislation, no additional ethical review by the University of Málaga Ethics Committee was necessary. As legal reference texts, the following may be cited: Law 14/2011, of June 1, on Science, Technology and Innovation (https://www.boe.es/buscar/pdf/2011/BOE-A-2011-9617-consolidado.pdf, accessed on 15 July 2025); and the Regulations of the Ethics Committee of the University of Málaga (https://www.boe.es/buscar/pdf/2011/BOE-A-2011-9617-consolidado.pdf, accessed on 15 July 2025), in addition to the national call for R&D projects under which the research was conducted (https://www.aei.gob.es/sites/default/files/stfls/MICINN/Ayudas/PE_2017_2020/PE_Orientada_Retos_Sociedad/FICHEROS/Proyectos_IDI_Retos_Investigacion/RESOLUCION_CONVOCATORIA_PROYECTOS_IDI_2019.pdf, accessed on 15 July 2025).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
EUEuropean Union

Note

1
There were seven independent variables in this study: age, gender, province, environment, level of education, and political ideology of the respondents.

References

  1. Allcott, Hunt, and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Journal of Economic Perspectives 31: 211–36. [Google Scholar] [CrossRef]
  2. Altay, Sacha, Manon Berriche, and Alberto Acerbi. 2023. Misinformation on misinformation: Conceptual and methodological challenges. Social Media + Society 9: 20563051221150412. [Google Scholar] [CrossRef]
  3. Apuke, Oberiri Destiny, and Bahiyah Omar. 2021. Fake news and COVID-19: Modelling the predictors of fake news sharing among social media users. Telematics and Informatics 56: 101475. [Google Scholar] [CrossRef]
  4. Baptista, Joao Pedro, and Anabela Gradim. 2022. A working definition of fake news. Encyclopedia 2: 632–45. [Google Scholar] [CrossRef]
  5. Baptista, Joao Pedro, Elisete Correia, Anabela Gradim, and Valeriano Piñeiro-Naval. 2021. Partidismo: ¿el verdadero aliado de las fake news? Un análisis comparativo del efecto sobre la creencia y la divulgación. Revista Latina de Comunicación Social 79: 23–47. [Google Scholar] [CrossRef]
  6. Bastick, Zach. 2021. Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation. Computers in Human Behavior 116: 106633. [Google Scholar] [CrossRef]
  7. Benaissa, Samia. 2024. Activistas, influencers y usuarios de redes sociales como fuente de desinformación: Una tipología operativa de nuevos líderes de opinión en los entornos digitales. Enrahonar. An International Journal of Theoretical and Practical Reason 73: 105–29. [Google Scholar] [CrossRef]
  8. Bennet, Lance, and Steven Livingston. 2018. The disinformation order: Disruptive communicative and the decline of democratic institutions. European Journal of Communication 33: 122–39. [Google Scholar] [CrossRef]
  9. Blumler, Jay G., and Elihu Katz. 1974. The Uses of Mass Comnunication. Beverly Hills: Sage Publications. [Google Scholar]
  10. Broda, Elena, and Jesper Strömbäck. 2024. Misinformation, disinformation and fake news: Lessons from an interdisciplinary, systematic literature review. Annals of the International Communication Association 48: 139–66. [Google Scholar] [CrossRef]
  11. Casas-Anguita, Juana, José Ramón Repullo-Labrador, and Juana Donado-Campos. 2003. La encuesta como técnica de investigación. Elaboración de cuestionarios y tratamiento estadístico de los datos (I). Atención Primaria 31: 527–38. [Google Scholar] [CrossRef]
  12. Casero-Ripollés, Andreu, Hugo Doménech-Fabregat, and Laura Alonso-Muñoz. 2023. Percepciones de la ciudadanía española ante la desinformación en tiempos de la Covid-19: Efectos y mecanismos de lucha contra las noticias falsas. Icono14 21: 2. [Google Scholar] [CrossRef]
  13. Catalina, Beatriz, Jorge Pedro Sousa, and Li-Chang Cristina. 2019. Consumo de noticias y percepción de fake news entre estudiantes de Comunicación de Brasil, España y Portugal. Revista de Comunicación 18: 93–115. [Google Scholar] [CrossRef]
  14. Cinelli, Matteo, Gianmarco De-Francisci-Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021. The echo chamber effect on social media. Proceedings of the National Academy of Sciences USA 118: e2023301118. [Google Scholar] [CrossRef] [PubMed]
  15. Dabbous, Amal, Karine Aoun Barakat, and Beatriz de Quero Navarro. 2022. Fake news detection and social media trust: A cross-cultural perspective. Behaviour & Information Technology 41: 2953–72. [Google Scholar] [CrossRef]
  16. De Ávila, Juan, Freddy Vergara, and Francisco Maza. 2024. Consumo y percepción de Fake News. Un análisis bibliométrico. Revista Palobra Palabra Que Obra 24: 231–51. [Google Scholar] [CrossRef]
  17. Dumitru, Elena-Alexandra. 2020. Testing children and adolescents’ ability to identify fake news: A combined design of quasi-experiment and group discussions. Societies 10: 71. [Google Scholar] [CrossRef]
  18. Espinoza-Guanilo, Antonio. 2024. Journalism in times of post-truth and misinformation: Analizing the work of digital platform’s journalists from El Comercio and RPP. Desde el Sur 16: e0032. [Google Scholar] [CrossRef]
  19. European Commission. 2024. Standard Eurobarometer 102. Media Use in the European Union. Brussels: European Comission. Available online: https://europa.eu/eurobarometer/surveys/detail/3215 (accessed on 15 July 2025).
  20. European Parliament. 2025. Youth Survey 2024. Brussels: European Parliament. Available online: https://europa.eu/eurobarometer/surveys/detail/3392 (accessed on 15 July 2025).
  21. Farias-Batlle, Pedro, Alba Córdoba-Cabús, and Bernardo Gómez-Calderón. 2024. Jóvenes y redes sociales: Hábitos de consumo informativo y credibilidad de las noticias. Comunicar 32: 155–65. [Google Scholar] [CrossRef]
  22. Flynn, Daniel, and Carlos Lastra-Anadón. 2025. Desinformación entre adolescentes en España: Desafíos y oportunidades de mejora en la escuela y online. Papeles de Economía Española 184: 178–88. [Google Scholar]
  23. Freiling, Isabelle, Nicole Krause, Dietram Scheufele, and Dominique Brossard. 2023. Believing and sharing misinformation, fact-checks, and accurate information on social media: The role of anxiety during COVID-19. New Media & Society 25: 141–62. [Google Scholar] [CrossRef]
  24. Galarza-Molina, Rocío. 2023. Youth in the face of disinformation: A qualitative exploration of Mexican college students’ attitudes, motivations, and abilities around false news. Communication & Society 36: 97–113. [Google Scholar] [CrossRef]
  25. García, Néstor, Yuleidy Sanjuán, and Francisco Javier Maza. 2021. Actitud frente a las fake news entre jóvenes universitarios. Revista de Jóvenes Investigadores Ad Valorem 4: 82–102. [Google Scholar] [CrossRef]
  26. García-Marín, David. 2021. Agenda temática, metodologías e impacto de la investigación sobre desinformación. Revisión sistemática de la literatura (2016–2020). Doxa Comunicación 33: 321–46. [Google Scholar] [CrossRef]
  27. García-Marín, David, and Guiomar Salvat-Martinrey. 2022. Tendencias en la producción científica sobre desinformación en España. Revisión sistematizada de la literatura (2016–2021). AdComunica 23: 23–50. [Google Scholar] [CrossRef]
  28. García-Marín, David, and Guiomar Salvat-Martinrey. 2023. Desinformación y guerra. Verificación de las imágenes falsas sobre el conflicto ruso-ucraniano. Icono14 21: 1–23. [Google Scholar] [CrossRef]
  29. Garrote-Rojas, Daniel, Sara Jiménez-Fernández, and Isabel Gómez-Barrento. 2018. Problemas derivados del uso de Internet y el teléfono móvil en estudiantes universitarios. Formación Universitaria 11: 99–108. [Google Scholar] [CrossRef]
  30. Golob, Tea, Matej Makarovic, and Mateja Rek. 2021. Meta-reflexivity for resilience against disinformation. Comunicar 66: 107–18. [Google Scholar] [CrossRef]
  31. Gómez-Calderón, Bernardo, Alba Córdoba-Cabús, and Álvaro López-Martín. 2023. Las fake news y su percepción por parte de los jóvenes españoles: El influjo de los factores sociodemográficos. Doxa Comunicación 36: 19–42. [Google Scholar] [CrossRef]
  32. Gómez-Calderón, Bernardo, Alba Córdoba-Cabús, and Antonio Méndez-Nieto. 2020. Jóvenes y fake news. Un análisis sociodemográfico aplicado al caso andaluz. IC–Revista Científica de Información y Comunicación 17: 481–504. [Google Scholar] [CrossRef]
  33. Guess, Andrew, Brendan Nyhan, and Jason Reifler. 2020. Exposure to untrustworthy websites in the 2016 US election. Nature Human Behaviour 4: 472–80. [Google Scholar] [CrossRef]
  34. Guess, Andrew, Jonathan Nagler, and Joshua Tucker. 2019. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances 5: eaau4586. [Google Scholar] [CrossRef]
  35. Gutiérrez-Coba, Liliana, Patricia Coba-Gutiérrez, and Javier Andrés Gómez-Díaz. 2020. Noticias falsas y desinformación sobre el Covid-19: Análisis comparativo de seis países iberoamericanos. Revista Latina de Comunicación Social 78: 237–64. [Google Scholar] [CrossRef]
  36. Hameleers, Michael, Anna Brosius, Franziska Marquart, Andreas Goldberg, Erika Van Elsas, and Claes De Vreese. 2022. Mistake or manipulation? Conceptualizing perceived mis- and disinformation among news consumers in 10 european countries. Communication Research 49: 919–41. [Google Scholar] [CrossRef]
  37. Heram, Yamila, and Mariano Dagatti. 2022. Consumo en pandemia: Fakes news, saturación y autocontrol. Cuadernos del Centro de Estudios de Diseño y Comunicación 161: 37–55. [Google Scholar] [CrossRef]
  38. Hernández-Conde, Macarena, and Manuel Fernández-García. 2019. Partidos emergentes de la ultraderecha: ¿fake news, fake outsiders? Vox y la web Caso Aislado en las elecciones andaluzas de 2018. Teknokultura. Revista de Cultura Digital y Movimientos Sociales 16: 33–53. [Google Scholar] [CrossRef]
  39. Herrero-Diz, Paula, Jesús Conde, and Salvador Reyes. 2020. Teens’ motivations to spread fake news on WhatsApp. Social Media + Society 6: 2056305120942879. [Google Scholar] [CrossRef]
  40. Hinsley, Amber. 2021. Cued up: How audience demographics influence reliance on news cues, confirmation bias and confidence in identifying misinformation. ISOJ Journal 11: 89–109. [Google Scholar]
  41. Ho, Shirley, Tong Goh, and Yan-Wah Leung. 2022. Let’s nab fake science news: Predicting scientists’ support for interventions using the influence of presumed media influence model. Journalism 23: 910–28. [Google Scholar] [CrossRef]
  42. Hoai-Lan, Duong, and Tran Minh-Tung. 2024. Exploring fake news awareness and trust in the age of social media among university student TikTok users. Cogent Social Sciences 10: 2302216. [Google Scholar] [CrossRef]
  43. Huber, Brigitte, Porismita Borah, and Homero Gil de Zúñiga. 2022. Taking corrective action when exposed to fake news: The role of fake news literacy. Journal of Media Literacy Education 14: 1–14. [Google Scholar] [CrossRef]
  44. Iandoli, Luca, Simonetta Primario, and Giuseppe Zollo. 2021. The impact of group polarization on the quality of online debate in social media: A systematic literature review. Technological Forecasting and Social Change 170: 120924. [Google Scholar] [CrossRef]
  45. Igartua, Juan José. 2006. Métodos Cuantitativos de Investigación en Comunicación. Barcelona: Bosch. [Google Scholar]
  46. Imaduwage, Sarith, Paul P. N. V. Kumara, and Wishma J. Samaraweera. 2022. Importance of user representation in propagation network-based fake news detection: A critical review and potential improvements. In 2022 2nd International Conference on Advanced Research in Computing (ICARC). Belihuloya: ICARC, pp. 90–95. [Google Scholar] [CrossRef]
  47. Jang, S. Mo, and Jonn K. Kim. 2018. Third person effects of fake news: Fake news regulation and Media Literacy Interventions. Computers in Human Behavior 80: 295–302. [Google Scholar] [CrossRef]
  48. Kahneman, Daniel. 2011. Thinking, Fast and Slow. London: Penguin. [Google Scholar]
  49. Kapantai, Eleni, Androniki Christopoulou, Christos Berberidis, and Vassilios Peristeras. 2021. A systematic literature review on disinformation: Toward a unified taxonomical framework. New Media & Society 23: 1301–26. [Google Scholar] [CrossRef]
  50. Kappes, Andrea, Ann H. Harvey, Terry Lohrenz, P. Read Montague, and Tali Sharot. 2020. Confirmation bias in the utilization of others opinion strength. Nature Neuroscience 23: 130–37. [Google Scholar] [CrossRef]
  51. Kops, Maxime, Catherine Schittenhelm, and Sebastian Wachs. 2025. Young people and false information: A scoping review of responses, influential factors, consequences, and prevention programs. Computers in Human Behavior 169: 108650. [Google Scholar] [CrossRef]
  52. Lewandowsky, Stephan, Ullrich Ecker, and John Cook. 2017. Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition 6: 353–69. [Google Scholar] [CrossRef]
  53. Loos, Eugene, Loredana Ivan, and Don Leu. 2018. Save the Pacific Northwest tree octopus: A hoax revisited. Or: How vulnerable are school children to fake news? Information and Learning Sciences 119: 514–28. [Google Scholar] [CrossRef]
  54. López-López, Paulo Carlos, Andrea Mila-Maldonado, and Vasco Ribeiro. 2023. La desinformación en las democracias de América Latina y de la Península Ibérica: De las redes sociales a la inteligencia artificial (2015–2022). Revista de Comunicación y Cultura 8: 69–89. Available online: https://dialnet.unirioja.es/descarga/articulo/9058319.pdf (accessed on 16 July 2025). [CrossRef]
  55. López-Martín, Álvaro, and Alba Córdoba-Cabús. 2024. Desinformación sobre la guerra de Israel-Hamás: Análisis de los bulos identificados por las plataformas de verificación españolas. Estudios Sobre el Mensaje Periodístico 30: 311–22. [Google Scholar] [CrossRef]
  56. Malquín-Robles, Amdrea, and José Gamir-Ríos. 2023. Desinformación y sexismo digital. El feminismo y su agenda como objeto de engaño en español. Icono14 21: 12. [Google Scholar] [CrossRef]
  57. Margolin, Drew, Aniko Hannak, and Ingmar Weber. 2017. Political fact-checking on Twitter: When do corrections have an effect? Political Communication 35: 196–219. [Google Scholar] [CrossRef]
  58. Martínez, Fátima. 2010. La Teoría de Los Usos y Gratificaciones Aplicada a Las Redes Sociales. In II Congreso Internacional Comunicación 3.0. Salamanca: University of Salamanca. Available online: https://pure.urosario.edu.co/es/publications/la-teor%C3%ADa-de-los-usos-y-gratificaciones-aplicada-a-las-redes-soci/ (accessed on 5 November 2025).
  59. Martín-Herrera, Inmaculada, and Juan Pablo Micaletto-Belda. 2021. Opiniones y actitudes de los estudiantes universitarios de Comunicación ante las fake news. Diagnóstico en un ecosistema docente. Comunicación y Hombre 17: 193–206. [Google Scholar] [CrossRef]
  60. McGrew, Sarah, Joel Breakstone, Teresa Ortega, Mark Smith, and Sam Wineburg. 2018. Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory & Research in Social Education 46: 165–93. [Google Scholar] [CrossRef]
  61. McQuail, Denis, and Sven Windahl. 1993. Communication Models for the Study of Mass Communication. London and New York: Routledge. [Google Scholar]
  62. Mendiguren, Terese, Jesús Pérez-Dasilva, and Koldobika Meso. 2020. Actitud ante las fake news: Estudio del caso de los estudiantes de la Universidad del País Vasco. Revista de Comunicación 19: 171–84. [Google Scholar] [CrossRef]
  63. Morató-Beltrán, Carolina, Álvaro López-Martín, and Laura López Romero. 2022. Enseñanza y aprendizaje con medios de comunicación en Educación Secundaria en Málaga. Revista Fuentes 24: 270–81. [Google Scholar] [CrossRef]
  64. Naeem, Salman Bin, Rubina Bhatti, and Aqsa Khan. 2021. An exploration of how fake news is taking over social media and putting public health at risk. Health Information and Libraries Journal 38: 143–49. [Google Scholar] [CrossRef]
  65. Newman, Nic. 2024. ¿Qué Sabemos del Ascenso de los ‘Influencers’ de Noticias en Plataformas Sociales y de Vídeo? Oxford: Reuters Institute. Available online: https://reutersinstitute.politics.ox.ac.uk/es/digital-news-report/2024/el-ascenso-de-voces-alternativas-e-influencers-de-noticias-en-plataformas (accessed on 15 July 2025).
  66. Palau-Sampio, Dolors. 2018. Fact-checking and scrutiny of power: Supervision of public discourses in new media platforms from Latin America. Communication and Society 31: 347–65. [Google Scholar] [CrossRef]
  67. Pennycook, Gordon, and David Rand. 2019. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188: 39–50. [Google Scholar] [CrossRef]
  68. Pérez, Ana, and Luis Miguel Pedrero. 2021. Retos del periodismo frente a las redes sociales, las fake news y la desconfianza de la generación Z. Revista Latina de Comunicación Social 79: 67–85. [Google Scholar] [CrossRef]
  69. Pérez-Curiel, Concha, and Ana María Velasco-Molpeceres. 2020. Tendencia y narrativas de fact-checking en Twitter. Códigos de verificación y fake news en los disturbios del Procés (14-O). AdComunica 20: 95–122. [Google Scholar] [CrossRef]
  70. Pérez-Escoda, Ana, Gema Barón-Dulce, and Juana Rubio-Romero. 2021. Mapeo del consumo de medios en los jóvenes: Redes sociales, fake news y confianza en tiempos de pandemia. Index.comunicación 11: 187–208. [Google Scholar] [CrossRef]
  71. Pérez-Escolar, Marta, Darren Lilleker, and Alejandro Tapia-Frade. 2023. A systematic literature review of the phenomenon of disinformation and misinformation. Media and Communication 11: 76–87. [Google Scholar] [CrossRef]
  72. Pilgrim, Jodi, Sheri Vasinda, Christie Bledsoe, and Elda Martinez. 2019. Critical thinking is critical: Octopuses, online sources, and reliability reasoning. The Reading Teacher 73: 85–93. [Google Scholar] [CrossRef]
  73. Raponi, Simone, Zeinab Khalifa, Gabriele Oligeri, and Roberto Di Pietro. 2022. Fake news propagation: A review of epidemic models, datasets and insights. ACM Transactions on the Web 16: 1–34. [Google Scholar] [CrossRef]
  74. Reuters Institute. 2023. Digital News Report 2023. Available online: https://lc.cx/r_fE9o (accessed on 29 July 2025).
  75. Rideout, Victoria, Alanna Peebles, Supreet Mann, and Michael Robb. 2022. Common Sense Census: Media Use by Tweens and Teens. San Francisco: Common Sense. Available online: https://www.commonsensemedia.org/sites/default/files/research/report/8-18-census-integrated-report-final-web_0.pdf (accessed on 30 July 2025).
  76. Rivas-de-Roca, Rubén, Concha Pérez-Curiel, and Andreu Casero-Ripollés. 2025. Reporting AI in Political Disinformation: Journalistic Discourses in European Quality Newspapers. Journalism Practice 18: 1–18. [Google Scholar] [CrossRef]
  77. Rodríguez-Pérez, Carlos, and Gustavo García-Vargas. 2021. Understanding which factors promote exposure to online disinformation. In Politics of Disinformation. Edited by Guillermo López-García, Dolors Palau-Sampio, Bella Palomo, Eva Campos-Domínguez and Pere Masip. New York: Wiley-Blackwell, pp. 173–86. [Google Scholar]
  78. Román-San-Miguel, Aránzazu, Nuria Sánchez-Gey Valenzuela, and Rodrigo Elías Zambrano. 2022. Information professionals and fake news during the COVID-19 pandemic. Vivat Academia 155: 131–49. [Google Scholar] [CrossRef]
  79. Sádaba, Charo, and Ramón Salaverría. 2023. Tackling disinformation with media literacy: Analysis of trends in the European Union. Revista Latina de Comunicación Social 81: 17–32. [Google Scholar] [CrossRef]
  80. Salaverría, Ramón, Nataly Buslón, Fernando López-Pan, Bienvenido León, Ignacio López-Goñi, and María-Carmen Erviti. 2020. Desinformación en tiempos de pandemia: Tipología de los bulos sobre la COVID-19. Profesional de la Información 29: e290315. [Google Scholar] [CrossRef]
  81. Scolari, Carlos. 2012. Media ecology: Exploring the metaphor to expand the theory. Communication Theory 22: 204–25. [Google Scholar] [CrossRef]
  82. Shariatmadari, David. 2019. Could Language Be the Key to Detecting Fake News? The Guardian. September 2. Available online: https://www.theguardian.com/commentisfree/2019/sep/02/language-fake-news-linguistic-research (accessed on 21 June 2025).
  83. Shearer, Elisa, and Jeffrey Gottfried. 2017. News Use Across Social Media Platforms 2017. Pew Research Center. Available online: https://lc.cx/PlQJtI (accessed on 7 July 2025).
  84. Sierra, Aurken, Roncesvalles Labiano, María Novoa, and Alfonso Vara. 2025. España. In Digital News Report 2025. Oxford: Reuters Institute. Available online: https://reutersinstitute.politics.ox.ac.uk/es/digital-news-report/2025/espana (accessed on 15 July 2025).
  85. Sierra-Bravo, Restituto. 1994. Técnicas de Investigación Social. Madrid: Paraninfo. [Google Scholar]
  86. Talwar, Shalini, Amandeep Dhir, Dilraj Singh, Gurnam Singh Virk, and Jari Salo. 2020. Sharing of fake news on social media: Application of the honeycomb framework and the third-person effect hypothesis. Journal of Retailing and Consumer Services 57: 102197. [Google Scholar] [CrossRef]
  87. Tandoc, Edson C., Ryan J. Thomas, and Lauren Bishop. 2021. What is (fake) news? Analyzing news values (and more) in fake stories. Media and Communication 9: 110–19. [Google Scholar] [CrossRef]
  88. Tandoc, Edson C., Zheng Lim, and Richard Ling. 2018. Defining “Fake News”: A Typology of Scholarly Definitions. Digital Journalism 6: 137–53. [Google Scholar] [CrossRef]
  89. Thomson, T.-J., Daniel Angus, Paula Dootson, Edward Hurcombe, and Adam Smith. 2022. Visual mis/disinformation in Journalism and Public Communications: Current verification practices, challenges and future opportunities. Journalism Practice 16: 938–62. [Google Scholar] [CrossRef]
  90. Thornhill, Calum, Quentin Meeus, Jeroen Peperkamp, and Bettina Berendt. 2019. A digital nudge to counter confirmation bias. Frontiers in Big Data 2: 11. [Google Scholar] [CrossRef] [PubMed]
  91. Thurman, Neil, and Richard Fletcher. 2019. Has digital distribution rejuvenated readership? Revisiting the age demographics of newspaper consumption. Journalism Studies 20: 542–62. [Google Scholar] [CrossRef]
  92. Tversky, Amos, and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. Science 185: 1124–31. [Google Scholar] [CrossRef]
  93. Vaezi, Atefeh, and Shaghayegh Javanmard. 2020. Infodemic and risk communication in the era of CoV-19. Advanced Biomedical Research 9: 10. [Google Scholar] [CrossRef]
  94. Valera-Ordaz, Lidia, Marina Requena-i-Mora, Dafne Calvo, and Guillermo López-García. 2022. Desenredando la desinformación: Nociones y discursos de la población española. Comunicar 30: 21–32. [Google Scholar] [CrossRef]
  95. Van der Linden, Sander, Dolores Albarracín, Lisa Fazio, Deen Freelon, Jon Roozenbeek, Briony Swire-Thompson, and Jay Van Bavel. 2025. Using Psychological Science to Understand and Fight Health Misinformation: An APA Consensus Statement. Washington: American Psychological Association. Available online: https://www.apa.org/pubs/reports/misinformation-consensus-statement.pdf (accessed on 15 July 2025).
  96. Vosoughi, Soroush, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359: 1146–51. [Google Scholar] [CrossRef]
  97. Wang, Jian, Yujia Zhai, and Fakhar Shahzard. 2025. Mapping the terrain of social media misinformation: A Scientometric exploration of global research. Acta Psychologica 252: 104691. [Google Scholar] [CrossRef]
  98. Wardle, Claire, and Hossein Derakhshan. 2017. Information Disorder. Toward an Interdisciplinary Framework for Research and Policymaking. Brussels: Council of Europe. Available online: https://edoc.coe.int/en/media/7495-information-disorder-toward-an-interdisciplinary-framework-for-research-and-policy-making.html (accessed on 15 July 2025).
  99. Weikmann, Teresa, Hannah Greber, and Alina Nikolaou. 2024. After deception: How falling for a deepfake affects the way we see, hear, and experience media. The International Journal of Press/Politics 30: 187–210. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Grievances and Polarization on Social Media: Perspectives from Religious Young Adults in Conflict-Ridden Amsterdam
Previous
Survival to Dignity? The Precarious Livelihood of Street Food Vendors in South Mumbai and Their Path Toward Decent Work
Next