ABSTRACT This article explores how young queer and feminist activists in Kenya find motivation for sociopolitical changemaking and ascribe meaning to their online engagement amid feelings of disillusionment. Engaging with academic literature on queer and feminist counterpublics and scrutinising online interactions through the conceptual framework of utopia, this study investigates how different online tactics nurture hope and utopian imagination within these counterpublics even when anti-feminist and anti-queer backlash appears overwhelming. It illustrates how young activists sustain hope within their own digital filter bubbles, and how their algorithmically and actively curated personal publics enable utopian imaginaries of societal change as something that is already emergent. It discusses three forms of online engagement in which such utopian hope is nurtured: attempting to diversify public narratives through online campaigning for queer rights; demanding accountability through online call-outs; and actively filtering out hate in personal online publics. Based on qualitative research with young feminist and queer activists in Kenya, this article analyses how activists experience their online engagement in which queer feminist worlds feel simultaneously inaccessible and actualised. It suggests that in contexts of backlash, the digital separation and even polarisation of audiences may enable and safeguard queer and feminist political deliberation.

Scientific literature is increasingly challenged by miscitation, publication bias, and the uncritical perpetuation of false information. One such case concerns the purported presence of caffeine in damiana, a botanical ingredient marketed for "sexual well-being." While consumer perception and regulatory implications make caffeine content highly relevant, a systematic review of the literature indicates that claims of caffeine in damiana largely stem from erroneous or circular referencing, including citations of studies where caffeine was never reported. To address this discrepancy experimentally, we conducted NMR analyses on authenticated aerial and seed material of T. diffusa and seeds of T. ulmifolia. No caffeine was detected, with a detection limit of <1 μM, corresponding to <4 μg caffeine per daily serving of damiana. These findings confirm that damiana is effectively caffeine free. Beyond correcting a specific phytochemical misconception, this study highlights how flawed referencing practices can perpetuate modern "scientific myths," emphasizing the need for more rigorous citation ethics and the value of experimental data. Addressing such errors is critical not only for scientific accuracy but also for consumer trust and evidence-based regulatory decision-making.

This study explores the historical roots, transformation within media culture, and ethical issues of clickbait journalism, which has long been a significant concern in Türkiye as well. Since the traditional era of the press, sensational news presentation has been prominent; however, with the rise of the internet, it has evolved to include various new strategies, shifting the focus from news quality to click rates. Clickbait journalism frequently employs techniques such as exaggeration, ambiguity, emotional intensity, and visual manipulation in both headlines and content. This practice is used to capture readers’ attention and to generate revenue through online advertising. The article examines examples of clickbait journalism from around the world and Türkiye, emphasizing the ethical challenges and potential consequences of misleading content, particularly in sensitive areas such as health and politics. Furthermore, it discusses how algorithms and social media platforms reinforce this culture through the concepts of “filter bubble” and “echo chamber.” In the conclusion, the article presents a set of nine recommendations addressing clickbait journalism, with a focus on strengthening media literacy and preserving professional and ethical principles in journalism.

The integration of artificial intelligence and algorithmic systems into social media platforms has fundamentally reshaped the information landscape during 2023-2024. This comprehensive study examines the transformation of the information space across major platforms: Facebook, X (formerly Twitter), TikTok, and YouTube, analyzing both positive and negative dimensions of AI-driven algorithmic curation. Through systematic analysis of platform policies, algorithmic mechanisms, and content moderation frameworks, this research identifies significant opportunities for information democratization and targeted harm prevention alongside concerning risks of filter bubbles, algorithmic bias, and manipulated discourse. The study demonstrates that AI algorithms, while enabling unprecedented scalability in content moderation achieving accuracy rates of 85-96%, simultaneously generate echo chambers that reduce exposure to diverse viewpoints. The research reveals critical disparities in algorithmic treatment across demographic groups and geographic regions, with particular challenges in non-Western language content moderation. A comprehensive framework for ethical algorithmic governance is proposed, emphasizing transparency requirements, bias auditing mechanisms, and participatory design approaches. This paper concludes that the future of information integrity depends not on algorithmic advancement alone but on institutional commitments to democratic accountability and cross-stakeholder collaboration in platform governance.

Scroll. Like. Divide. The filter bubble effect on electoral perceptions

The term "filter bubble" refers to the possibility that online content customization, resulting from the use of algorithms, could isolate users from wider or even differing perspectives. Recommender systems, which are implemented in all digital platforms and rely on algorithms to anticipate users' preferences and recommend relevant items, are particularly vulnerable to this phenomenon. The rapid development of generative artificial intelligence (GenAI) has brought in focus how recommender systems narrow what users see and strengthen the filter bubble effect. This literature review examines the recent work on the impact of GenAI on search, social media news feeds and customized recommendations. The most direct evidence suggests that GenAI can amplify selective exposure when users make use of large language model systems in ways that tend to confirm their views and expectations, as results and outputs are personalized to existing preferences. At the same time, GenAI can also be used to interrupt the exposure to more of the same information, by directing users to diverging material and alternative viewpoints. Ultimately, GenAI seems to be less of a cause for filter bubble emergence and more of an amplifier. It can intensify narrowing through personalization and distilled content, but it can also be deployed to diversify exposure if platforms choose to optimize for diversity rather than pure user engagement. The review concludes by identifying further research needed to establish causal effects across social media, search, and e-commerce.

Recommender systems are essential for managing the abundance of online content and enhancing user engagement through personalized suggestions. However, their reliance on personalization risks reinforcing ”filter bubbles,” a phenomenon where users repeatedly encounter content aligning closely with their existing preferences. This cycle can lead to a reduction in diversity, limiting exposure to novel ideas and exacerbating polarization and misinformation. Traditional recommender system approaches to mitigate filter bubbles typically emphasize recommended diversity, balancing the variety of items shown to users while maintaining relevance. Yet, a significant gap exists between recommended diversity (what users see) and consumed diversity (what users choose). Even when recommendations are diversified, users exhibiting strong selective exposure may continue to select content aligned with their existing preferences, thereby perpetuating filter bubbles. To address this issue, we propose PI-adaptDiv, an algorithm designed to increase consumed diversity directly rather than merely diversifying recommendations. PI-adaptDiv employs a Proportional-Integral (PI) controller to dynamically adjust recommendation diversity based on user interactions, prioritizing items that enhance consumed diversity while maintaining a high likelihood of user selection. Evaluations using offline simulations and an online user experiment on a video recommendation platform demonstrate PI-adaptDiv’s effectiveness in significantly enhancing consumed

Academic AbstractAdvances in AI require a revision of the psychological and socio-technical dynamics by which individuals are radicalized to embrace violent extremism. This review synthesizes process models of radicalization with research on social and personality risk factors, AI, and psychological mechanisms to propose a four-stage framework mapping the AI architecture of radicalization: (1) Exposure, where recommender systems and virality features create initial attraction to extreme content; (2) Reinforcement, where filter bubbles and group recommendations leverage biases to strengthen extremist beliefs and create echo chambers; (3) Group Integration, where ideologically homogenous clusters, AI bot swarms and companions foster group belonging and readiness for action; cumulatively resulting in (4) Violent Extremist Action. We examine how established social, cognitive, personality, and contextual vulnerability factors heighten psychological risk in the AI-driven radicalization process, as well as the emerging role of generative AI. We conclude by outlining a stage-based framework for governance and future research.Public AbstractAI-driven algorithms designed to maximize engagement on social media, compounded by generative AI, can unintentionally set the stage for radicalization. It begins with Exposure, where algorithms push users toward extreme content because it captures attention. Next, during Reinforcement, algorithms feed users personalized content while AI swarms can create a synthetic consensus that reinforces emerging biases, normalizes extremity, and insulates users from alternative views. Third, during Group Integration, individuals are absorbed into extremist networks, reinforced by human peers, AI companions, and bot swarms that validate radical beliefs and deepen identity ties. By exploiting psychological needs for belonging and certainty, this stage becomes particularly pernicious, potentially opening the door for violence. We propose policy measures that can reduce radicalization at each stage.

Coping with e-commerce filter bubbles: proactive versus reactive strategies

Dispersive effective metasurface model for bubbly media

This study investigates the relationship between the concept of the attention economy and the steering of Egyptian audiences toward consuming specific digital content via social media platforms. It does so through an evaluative examination of opportunities and risks, and by proposing a set of response mechanisms from the perspective of elites. Data were collected through semi-structured scholarly interviews with a sample of academic and professional media elites who maintain an active presence in digital public debate, alongside an online questionnaire administered to a sample of social media users. The findings indicate that while users view algorithms as the only factor influencing the consumption of particular digital content, media elites argue that this phenomenon results from a complex interaction among three key elements: algorithms, user behavior, and platforms’ commercial policies. The most significant risks include the emergence of filter bubbles that limit individuals’ exposure to diverse viewpoints, thereby narrowing their intellectual horizons, as well as algorithmic non-neutrality, given that algorithms are designed to maximize clicks in ways that serve the interests of specific institutions and entities, which in turn intensifies polarization and division.

With the development of the internet in the information era and the wide access to information the internet makes available, Echo Chambers and Filter Bubbles have developed. Echo Chambers and Filter Bubbles are a consequence of Reinforcement Learning Algorithms. An Echo Chamber is an environment where people only encounter beliefs or opinions that reinforce the beliefs and opinions to which they are already committed. (Sunstein, 2017). This serves the purpose of creating a constant positive feedback loop, which continually reinforces one idea or set of ideas (Murphy, 2022). A Filter Bubble develops when recommendation algorithms feed users content based on what narratives the recommendation algorithm determines an audience wants to hear to maximize user engagement (Pariser, 2011). Echo Chambers and Filter Bubbles are relevant to Cyber and Cognitive Warfare because state actors can take advantage of the existence of these environments and use them to influence discourse and public opinion. In the context of cyber warfare attackers can use bots and fake accounts where they pretend to be citizens of the target nation to flood these Echo Chambers with narratives that align with the audience’s current belief system and while also benefiting the attackers (Singer &amp; Brooking, 2018). In the context of cyber warfare attackers can abuse Filter Bubbles by using data breaches and advertising data to infiltrate the Filter Bubbles and direct them towards the attackers desired narrative (Matz et al., 2017). These abuses rise above the level of mere internet trolling, they are intentional and targeted acts of Cyber Warfare aimed at influencing the cognitive space of a nation’s population (Claverie &amp; du Cluzel, 2022). Echo Chambers and Filter Bubbles are constructed to accomplish a strategic objective, in this case they are aimed at influencing the decision making and opinions of an audience to influence elections, policy, protests, or overall public sentiment. These strategic objectives are accomplished by using non-kinetic weapons to destabilize society, diminish decision making capabilities, and erode trust in democratic institutions. This type of cyber-attack was made apparent in the COVID-19 disinformation schemes when wellness communities on social media platforms were flooded with anti-vaccine and medicine narratives leading to distrust in the medical system, and the politicians who promoted them (Dawson &amp; Innes, 2019). This paper identifies the technical, ethical, and anticipated ethical issues of Filter Bubbles and Echo Chambers and proposes a technical and policy framework to classify and prevent these acts of Cyber Warfare.

With the ongoing evolution of media channels, debates over the concept of mass communication have been reignited. When we live in a society of filter bubbles and AI-generated content, the very notion of a large uniform audience has been undermined. Indeed, the process of mass communication looks different today than in the early days of the field, which naturally affects how to define and measure media effects. In this forum, leading communication scholars provide arguments as to whether we should keep using the term “mass communication,” adapt its definition, or develop entirely new concepts that better reflect our fragmenting media environment.

The emergence of online markets was initially expected to lower prices and reduce price dispersion. However, empirical evidence does not seem to support these expectations. In this article, we first propose a mechanism that can explain these findings: selective information—arising from filter bubbles or echo chambers—may lead to increased price dispersion and higher average prices. Second, we show that these higher prices are not necessarily a cause for concern; in fact, the prevalence of filter bubbles may, somewhat surprisingly, have positive implications for overall welfare.

Algorithmic Recommendation Systems (ARS) are central to personalization in video streaming services, yet users increasingly express dissatisfaction due to inaccurate recommendations, filter bubbles, and privacy concerns. Despite their importance, user dissatisfaction with ARS remains underexplored. This study investigates the antecedents of ARS dissatisfaction and subsequent coping behaviors through in-depth interviews with 30 streaming users. The analysis identifies a three-stage process involving user perceptions, sources of dissatisfaction, and coping responses. Three key findings emerge. First, dissatisfaction with core service failures or the platform itself triggers approach coping, whereas externally driven issues lead to avoidance coping. Second, users’ perceptions of ARS shape dissatisfaction: low trust and perceived control result in core failures, while high trust combined with profit-oriented perceptions generates external dissatisfaction. Third, perceived losses of information, time, and personalization control contribute to disengagement. The study highlights the need for enhanced user control and transparency to sustain long-term engagement.

ABSTRACT This study examines personalized algorithmic pricing and consumer protection across three major jurisdictions the United States, European Union, and India analyzing how artificial intelligence‐driven pricing systems challenge traditional regulatory frameworks and threaten consumer autonomy. The research adopts a comparative methodology combining doctrinal legal analysis with empirical examination of enforcement patterns, scrutinizing recent regulatory developments including the EU's Digital Services Act, the US Department of Justice's RealPage litigation, and India's Consumer Protection Act amendments. The central argument demonstrates that transparency‐only approaches prove fundamentally inadequate in addressing algorithmic filter bubbles and market concentration. Evidence from India's fast‐commerce sector reveals sophisticated discrimination patterns, including device‐based pricing differentials and usage‐pattern exploitation, while “hub‐and‐spoke conspiracies” enable algorithmic collusion without explicit coordination between competitors. Key findings of study that existing legal frameworks, designed for pre‐digital markets, cannot effectively address technologically sophisticated forms of consumer harm and market manipulation. The study identifies critical gaps in jurisdictional approaches: India's reactive consumer protection model, the EU's proactive transparency requirements, and the US's antitrust‐centric enforcement. The research proposes moving beyond disclosure paradigms toward “information enrichment” mandates requiring platforms to actively diversify algorithmic recommendations, coupled with user‐controlled choice architectures and structural market reforms. These interventions, aligned with fundamental rights principles requiring states to serve as ultimate guarantors of diversity offering pathways for regulatory frameworks that balance technological innovation with consumer welfare and market competition. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Legal Issues Commercial, Legal, and Ethical Issues &gt; Ethical Considerations Commercial, Legal, and Ethical Issues &gt; Security and Privacy

This research analyses Spanish teenagers’ perceptions of political polarization, the role of social media in political debate and the normalization of hate speech in digital environments through four focus groups with 26 participants aged 16–17 from different regions of Spain. The results reveal a paradox: although adolescents recognize the importance of politics, they tend to disengage from political debates due to their association with conflict and social risk. Fear of judgement and exclusion leads them to restrict conversations to trusted circles, reinforcing ideological bubbles. In general, they do not trust schools as places to mitigate polarization. Gender differences in political engagement are modest: some female participants express emotional detachment from politics, fearing potential interpersonal conflicts. In contrast, male participants generally view ideological conflict as an inherent and acceptable part of democratic discourse. Social media, especially TikTok and Instagram, subtly shape their opinions through entertainment, contributing to the normalization of polarized narratives. Regional differences also emerge, with greater political engagement in nationalist contexts such as Catalonia and the Basque Country. This study highlights the importance of taking gender and regional context into account when fostering democratic engagement among young people.

The article is devoted to a comprehensive analysis of the evolution of political advertising genres in the context of pervasive digitalization and the development of digital platforms. The transformation of traditional formats (printed advertising, radio, television) under the influence of the digital media space is considered, and the characteristic features of new genre forms that have become widespread in the online environment are identified. Particular attention is paid to such viral formats as political memes and their semiotic transformation from political posters, short videos on the TikTok platform with an analysis of their structural features and mechanisms of influence on the youth audience, as well as interactive content and user-generated content. Scientific publications that initiated the solution of the raised problem are analyzed, and the relevance of further research on the specifics of genre transformation, mechanisms of influence, and ethical consequences of using new digital genres of political advertising is substantiated. The aim of the article is to study this transformation, identify genre features, mechanisms of influence, and ethical consequences of new viral formats of political communication in the online environment. Based on the analysis of theoretical approaches (dual-coding theory, network effects theory, social constructivism theory, incongruity theory of humor, affect theory, STEPPS model, social learning theory, framing theory, information overload theory, attention theory, musical influence theory, narrative persuasion theory, audience segmentation theory, political leader image theory, message simplification theory, filter bubble theory, social proof theory, imitation theory), the peculiarities of the functioning and influence of new digital genres of political advertising are revealed.

In the digital era, religious authority has undergone a significant shift from physical pulpits in mosques to "algorithmic pulpits" on social media. This study aims to analyze how content personalization mechanisms and filter bubbles affect perceptions of religious moderation among users. Social media algorithms are designed to maximize engagement by presenting content tailored to user preferences, inadvertently creating echo chambers. Using a qualitative method with a literature review and critical discourse analysis approach, this research explores the psychological and sociological impacts of this automated content curation. The results indicate that algorithms tend to reinforce confirmation bias, where users are exposed only to religious narratives that validate their pre-existing views, while isolating them from divergent perspectives (the religious other). This potential erodes the values of religious moderation (wasathiyah), which emphasizes balance and tolerance, and fosters polarization and extremism based on decontextualized texts. The study concludes that algorithmic literacy is essential as part of religious literacy, preventing believers from being trapped in blind fanaticism constructed by machines.

In the digital era, religious authority has undergone a significant shift from physical pulpits in mosques to "algorithmic pulpits" on social media. This study aims to analyze how content personalization mechanisms and filter bubbles affect perceptions of religious moderation among users. Social media algorithms are designed to maximize engagement by presenting content tailored to user preferences, inadvertently creating echo chambers. Using a qualitative method with a literature review and critical discourse analysis approach, this research explores the psychological and sociological impacts of this automated content curation. The results indicate that algorithms tend to reinforce confirmation bias, where users are exposed only to religious narratives that validate their pre-existing views, while isolating them from divergent perspectives (the religious other). This potential erodes the values of religious moderation (wasathiyah), which emphasizes balance and tolerance, and fosters polarization and extremism based on decontextualized texts. The study concludes that algorithmic literacy is essential as part of religious literacy, preventing believers from being trapped in blind fanaticism constructed by machines.