Abstract
The aim of this study was to study the perceptions and behaviors of Iranian users in the face of the publication of fake news on the social networks Instagram and Telegram. The research method was a survey and the data collection tool was an online questionnaire. The statistical population includes Iranian users who use the social media "Instagram and Telegram" to receive news. The sample size was determined using power analysis, which was 400 people, including 234 women and 166 men. To determine the validity of the questionnaire, face validity was used, and to determine the reliability of the questionnaire, Cronbach's alpha (0.89) was used. The research findings showed that information expansion, media literacy in recognizing the publication of fake news, and prescriptive expectations from social media significantly predict trust and distrust towards "Instagram and Telegram", which in turn affects the intensity of Iranian users' use of information platforms. The results of the factor analysis showed that information-based features and user characteristics significantly affect trust and distrust, but between the two distinct constructs of trust and distrust are important mediators through which the spread of fake news affects user usage. Trust has a much stronger effect on increasing the intensity of use of "Instagram and Telegram".
Keywords: Fake news, trust, social networks, online post-truth, Iranian users' perceptions.
 
Introduction
The rapid expansion of digital technologies and the ubiquity of social media platforms have dramatically transformed the contemporary news ecosystem, making the circulation of fake news more pervasive, consequential, and difficult to control than in any previous period. In Iran, where platforms such as Instagram and Telegram attract tens of millions of active users, news and information are increasingly accessed, shared, and interpreted within highly interactive and networked online environments that both empower users and expose them to intensive information overload. Within these environments, the large-scale production and recirculation of misleading, fabricated, or strategically distorted news undermine public trust in traditional and online information systems, intensify political polarization, and foster widespread cynicism toward official media and public institutions.
Building on theoretical distinctions between misinformation, disinformation, and malinformation, as well as on the concepts of the network society, the filter bubble, and credit perceptions in the sense of Allameh Tabataba’i as interpreted by Ali Asghar Mosleh, this study investigates how the spread of fake news on Instagram and Telegram shapes Iranian users’ trust, distrust, and subsequent intensity of use of these platforms. Specifically,
the research examines how information-related characteristics (information credibility and information elaboration) and user-related characteristics (media literacy and prescriptive expectations of the platforms) jointly influence trust and distrust,and how these two attitudinal constructs,treated as conceptually distinct, mediate the relationship between antecedents and the intensity of social media use.
 
 
Materials & Methods
A quantitative survey design was employed to explore users’ perceptions and behaviors when confronted with fake news on Instagram and Telegram. The target population consisted of Iranian adult users who consume news through these platforms, and inclusion criteria required participants to be at least 18 years old and to use Instagram and/or Telegram for news purposes for a minimum of 15 minutes per day, while individuals not meeting these conditions were excluded from the study. Using an online questionnaire administered via an Iranian survey platform, 400 valid responses were collected, comprising 234 women (58.5%) and 166 men (41.5%), with the majority falling into the 30–45 age group and holding at least a bachelor’s degree.​
Measurement items were adapted from established scales in prior research and were rated on five-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree). Trust and distrust toward Instagram and Telegram were measured using items adapted from Cheng et al. (2018) and related work, while intensity of use was captured with items drawn from Ellison et al.’s conceptualization and subsequent adaptations for social media contexts. Perceived information credibility was measured with items adapted from Lee et al. (2015), information elaboration with items from Wei et al. (2010) and the elaboration likelihood tradition, media literacy with modified items from studies on online selfefficacy and literacy, and prescriptive expectations were operationalized based on Expectancy Violations Theory and related communication research.​
Data analysis was conducted using SPSS 28 and AMOS 20. After initial descriptive analyses of demographic variables and key constructs, the measurement model was assessed through confirmatory factor analysis; convergent and discriminant validity were examined using factor loadings, average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha. All constructs demonstrated acceptable internal consistency (e.g., Cronbach’s alpha around 0.89 for the overall instrument) and AVE and CR values above commonly accepted thresholds, supporting the adequacy of the measurement model. In the structural stage, structural equation modeling (SEM) was used to test the hypothesized relationships among information credibility, information elaboration, media literacy, prescriptive expectations, trust, distrust, and intensity of use, while controlling for age, gender, prior trust/distrust in media, exposure to fake news, and general social media use.​
 
Discussion & Result
The findings indicate that the spread of fake news on Instagram and Telegram significantly affects both trust and distrust in these platforms and, in turn, jointly shapes users’ intensity of use. Information credibility had a significant positive effect on trust and a negative effect on distrust, meaning that when users perceive news as accurate and reliable, they form more positive expectations and reduce suspicion toward the platforms. Conversely, higher levels of information elaboration—users’ cognitive engagement with and reflection on fake news—tended to decrease trust while increasing distrust, as users became more aware of potential harms and governance shortcomings.
Media literacy played a dual role. Users with higher media literacy felt more confident in identifying fake news and managing their own sharing behavior, which supported higher trust in the informational value of the platforms. At the same time, greater literacy was associated with reduced distrust, indicating that knowledgeable users are less likely to generalize individual fake news incidents to a blanket rejection of the entire system. Prescriptive expectations—normative beliefs about what Instagram and Telegram ought to do regarding monitoring, flagging, and removing fake news—were negatively related to trust and positively related to distrust when users perceived these expectations as unmet. Thus, stronger demands for platform responsibility, when violated, exacerbated negative evaluations and skepticism.
The structural model showed that trust and distrust function as empirically distinct mediators between the antecedent variables and intensity of use, supporting a two-dimensional view of these constructs. Trust in Instagram and Telegram positively predicted intensity of use, reflecting users’ inclination to stay engaged with platforms perceived as competent, fair, and beneficial. In contrast, distrust negatively predicted intensity of use, indicating that users who view platforms as irresponsible, exploitative, or systematically unreliable are less willing to use them intensively. Overall, fake news operates within a broader socio-technical and cognitive context where information properties, user capabilities, and normative expectations converge to shape differentiated attitudes and behaviors.
From Mosleh’s interpretation of Allameh Tabataba’i’s theory of “credit perceptions,” users’ understandings of platforms, news, and norms are embedded in socially constructed, context-dependent frameworks that may not fully align with external reality but still guide action. Within Castells’ network society and Pariser’s filter-bubble environment, these perceptions are amplified by algorithmic curation and homophilous networks, reinforcing trust in familiar sources and distrust toward perceived out-groups or opaque institutions. The findings show that fake news can reshape these credit perceptions, altering users’ sense of fairness, responsibility, and authenticity in ways that directly affect their engagement with Instagram and Telegram.
 
Conclusion
This study offers a comprehensive model of how fake news in Iranian social media environments shapes trust, distrust, and the intensity of use of Instagram and Telegram by integrating perspectives from the network society, filter bubble theory, trust and distrust theory, and the philosophy of credit perceptions. By empirically distinguishing trust from distrust and demonstrating their differential antecedents and outcomes, the research extends previous work that often treated distrust merely as the opposite pole of trust and underscores the need to consider both constructs when assessing the social consequences of fake news.​
The results carry several practical implications. First, enhancing users’ media literacy appears crucial for fostering informed trust while mitigating blanket distrust, implying that educational programs and awareness campaigns can play a central role in empowering citizens to critically evaluate online news. Second, platforms should increase transparency and accountability regarding their policies and technical measures for detecting, labeling, and removing fake news in order to better align with users’ prescriptive expectations and prevent the erosion of trust. Third, communication channels that enable more active dialogue between platforms and users may help recalibrate credit perceptions, reduce misaligned expectations, and support healthier, more reflective patterns of use. Finally, the study highlights the need for further interdisciplinary research that connects philosophical accounts of perception and value with empirical analyses of digital media, so as to better understand how socially constructed realities mediate the impact of fake news on attitudes, behavior, and democratic culture in highly networked societies.