This article introduces the concept of layered affordances to affordance theory, providing a framework for analyzing how digital platform affordances intersect and reinforce each other in facilitating technology-facilitated gender-based violence (TFGBV). Focusing on the #MeTooIndia movement and the Nth Room case in South Korea, we explore how multiplatform affordances, such as visibility, anonymity, and shareability, combine to create digital environments that enable the spread, reinforcement, and normalization of misogynistic narratives. In both cases, perpetrators strategically exploited layered affordances across platforms like X, Instagram, and Telegram to evade moderation, amplify harmful behaviors, and establish echo chambers of digital harm. This article argues that layered affordances reveal complex cross-platform dynamics that are crucial for understanding TFGBV and the ways in which digital harms are structured and sustained. Our findings highlight the need for nuanced, cross-platform governance policies that address the compounded nature of digital violence, particularly in non-Western contexts where marginalized communities are often most affected. By conceptualizing layered affordances, this study provides a crucial framework for analyzing the affordances that enable digital harms and for developing targeted interventions, thereby paving the way for more effective digital policy and platform design strategies.

Social media provide unprecedented opportunities for public deliberation. However, a growing number of users perceive negativity in political debate taking place in those venues and are increasingly frustrated when discussing politics with those they disagree with. In this article, we test the proposition that perceiving online discussions as healthier (i.e. more polite and civil) than offline discussions invigorates online political participation. We rely on an online survey fielded in 17 European countries on more than 28,000 individuals. Our findings indicate that being embedded in healthier discussions on social media is more of an important predictor of online participation for those respondents reporting higher political discussion fatigue and less so for those perceiving online discussions as fun. Overall, our study offers cross-national evidence of why and for whom exposure to healthy political discussions online might be mobilizing.

This article examines how Donald Trump and Joe Biden used Instagram as a platform for emotional political storytelling during their campaigns and presidencies. Based on a thematic analysis of a large sample of Instagram posts (approximately 9000 reviewed, with selected posts analyzed in depth), and grounded in affect theory and visual political communication, the study identifies six central emotional patterns—nostalgia, fear, pride, hope, unity, and grievance—through which both leaders constructed emotionally resonant narratives. While often portrayed as ideological opposites, the analysis suggests that Trump and Biden operate within a shared emotional grammar: both mobilize longing for the past, symbolic restoration, and moral clarity to emotionally realign a fragmented electorate. Instagram, with its aesthetics of intimacy and symbolic condensation, enhances these strategies by offering a visual vocabulary attuned to affective public sentiment. The analysis explores how both leaders engage with and reshape prevailing emotional undercurrents in American society, focusing on how emotion functions as a narrative resource in digital political communication. In doing so, the article contributes to research on affective publics, the emotional simplification of politics, and the role of visual storytelling in the performance of democratic legitimacy in the digital age.

How social media fuels affective polarization, characterized by favorability toward in-party members and apathy toward out-party members, has emerged as a crucial topic in political communication. However, few studies examine the relationship between social media and affective polarization in multi-party contexts outside American politics. Applying Wagner’s methodology for measuring affective polarization in a multi-party system, we calculate affective polarization in three ways: the traditional method as the absolute difference between liberal and conservative politicians, mean distance as the average distance from a person’s favorite politician, and spread as the average distance from an individual’s mean feelings toward politicians. We investigate how social media use is related to affective polarization using these three measures based on a survey of 1,159 respondents from Colombia in 2022. The findings indicate that the three measures of affective polarization intensify with the use of social media, mediated by ideological extremity. The results suggest that the role of social media and ideology may be consistent regardless of the way of measuring affective polarization when a country has multiple parties and political elites, further suggesting implications for affective polarization in political systems with more than two parties.

What news spreads on social media equally depends on what news users do and do not share. However, prior research has predominantly focused on successful news sharing , overlooking the equally consequential behavior of deliberate news withholding. This study addresses that gap by proposing a self-presentation model of deliberate news withholding on social media, integrating three dominant approaches previously used to examine successful news sharing: (1) the informational approach focusing on the virality of news content, (2) the structural approach emphasizing social media network characteristics, and (3) the relational approach centered on users’ self-presentation and management of relationships with their audience. Specifically, this study combines two types of data: (1) survey data from 408 users and (2) a text analysis of news content they withheld in their three most active chatrooms. We examine how users selectively withhold news with varying levels of emotionality, argumentativeness, and hard or soft news value, depending on the characteristics of their audience networks – particularly network size and tie strength – and in relation to three self-presentational goals: self-construction, privacy protection, and audience-pleasing. Findings show that users strategically withhold varied types of news content across different user-audience networks to meet distinct self-presentational goals, thereby managing audience expectations and curating their online image. By shifting attention from news sharing to news withholding, this study offers a more complete account of how everyday users shape news flows and social discourse on social media.

Social media communities are increasingly considered as spaces where evidence-based information about history is undermined. We combine expert interviews and content analysis of debates about genocide in a popular online contrarian community to understand how to mitigate the spread of misleading information across such communities in the domain of history. We analyse 1725 entries on 10 Reddit forums dedicated to debating and promoting scepticism towards international consensus about prominent historical topics. The entries we analyse cover three topics referred to as genocide by forum members: the Holocaust, the Holodomor, and the COVID-19 vaccination. The prevailing view suggests that contrarian expression fosters smaller, ideologically homogeneous, and relatively radical online spaces where differing views are diminished or entirely absent. To the contrary, we analyse real-life behavioural data to demonstrate substantial scepticism towards contrarian narratives even in some of the most popular dedicated online communities, which may suggest that these spaces are more internally contested than prevailing theories imply.

Recent advances in generative AI have raised public awareness, shaping expectations and concerns about their societal implications. Central to these debates is the question of AI alignment—how well AI systems meet public expectations regarding safety, fairness, and social values. However, little is known about what people expect from AI-enabled systems and how these expectations differ across national contexts. We present evidence from two surveys of public preferences for key functional features of AI-enabled systems in Germany ( n = 1800) and the United States ( n = 1756). We examine support for four types of alignment in AI moderation: accuracy and reliability, safety, bias mitigation, and the promotion of aspirational imaginaries. U.S. respondents report significantly higher AI use and consistently greater support for all alignment features, reflecting broader technological openness and higher societal involvement with AI. In both countries, accuracy and safety enjoy the strongest support, while more normatively charged goals—like fairness and aspirational imaginaries—receive more cautious backing, particularly in Germany. We also explore how individual experience with AI, attitudes toward free speech, political ideology, partisan affiliation, and gender shape these preferences. AI use and free speech support explain more variation in Germany. In contrast, U.S. responses show greater attitudinal uniformity, suggesting that higher exposure to AI may consolidate public expectations. These findings contribute to debates on AI governance and cross-national variation in public preferences.

The media landscape now contains a growing mix of real and synthetic videos, presenting either authentic or false content. Drawing on the heuristic–systematic model, we conducted a mixed-design survey experiment in China to examine how perceived technical quality and content familiarity influence individuals’ perceptions of and performance in detecting real and synthetic videos. Multilevel analyses based on participant evaluations revealed that both perceived technical quality and content familiarity were positively associated with perceived video realness and trust in the vlogger. Higher perceived technical quality improved detection accuracy for real videos but decreased accuracy for synthetic videos. In addition, the positive association between content familiarity and trust in the vlogger was stronger for synthetic videos than for real videos. These findings deepen our understanding of how heuristic processing shapes perceptions and discernment of potentially synthetic videos and offer practical insights for mitigating the risks associated with AI-generated visual deepfakes.

Many individuals regularly use multiple social media platforms, and their information exposure is shaped by the various networks they maintain across these platforms. Given the rising trend of multi-platform social media use, this article introduces a two-step approach to investigate how networks across platforms conjointly and distinctly relate to incidental exposure to news and political information. Our analysis of survey data from the United States showed that greater immersion in multiple politically heterogeneous networks across platforms predicted higher counter-attitudinal incidental exposure, while greater immersion in multiple politically homogeneous networks across platforms predicted higher pro-attitudinal incidental exposure. Among the popular platforms, immersion in networks on Facebook, X (Twitter), and YouTube played a particularly influential role in these relationships. Surprisingly, we also found that greater immersion in homogeneous networks across multiple platforms predicted higher counter-attitudinal exposure, even though immersion in any single platform’s homogeneous network was not a significant predictor.

As China undergoes rapid demographic aging, older Wanghong (influencers over 60) on platforms like Douyin challenge traditional digital inclusion frameworks that conceptualize access as linear progression through motivational, material, skills, and usage dimensions. This study examines how older micro-Wanghong navigate challenges during content creation, revealing critical gaps in existing digital inclusion theory. Drawing on an exploratory qualitative study of 11 micro-Wanghong, we develop the dynamic access concept that reconceptualizes digital inclusion as continuous adaptation across three dimensions: motivational resilience (maintaining creator identity despite platform constraints), adaptive skill development (ongoing competency evolution responding to platform changes), and algorithmic navigation (strategic responses to systematic platform exclusions). Our analysis reveals systematic algorithmic exclusion through temporal marginalization (algorithms favoring high-frequency posting patterns that disadvantage older micro-Wanghong’s rhythms), knowledge domain restrictions (automated content moderation limiting topics relevant to older adults), and culture misalignment (platform architectures optimized for entertainment conflicting with older micro-Wanghong’s knowledge transformation preferences) that create structural barriers independent of individual competencies. Despite these constraints, participants demonstrate sophisticated adaptive strategies enabling sustained creative participation through ongoing platform negotiation rather than static skill application. These findings extend Van Dijk’s digital inclusion theory by demonstrating how platform-mediated content creation collapses traditional access boundaries into ongoing negotiation processes. The dynamic access addresses temporal dimensions which often assumed in digital appropriation or inclusion theories, positioning meaningful participation as requiring continuous adaptive capacity rather than one-time achievement. We argue that digital inclusion policy must evolve beyond basic access provision toward supporting ongoing adaptation to platform evolution.