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Accelerated by technological advancements, while online platforms equipped with recommendation algorithms offer convenience to obtain information, it also brought algorithm bias, shaping the norms and behaviors of their users. The filter bubble, conceived as a negative consequence of algorithm bias, means the reduction of the diversity of users' information consumption, garnering extensive attention. Previous research on filter bubbles typically used users' self-reported or behavioral data independently. However, existing studies have disputed whether filter bubbles exist on the platform, possibly owing to variations in measurement methods. In our study, we took content category diversity to measure the filter bubbles and innovatively used a combination of participants' self-reported and website behavioral data, examining filter bubbles on a single online video platform (Bilibili). We conducted a questionnaire survey among 337 college students and collected 3,22,324 browsing records with their informed authorization, constituting the dataset for research analysis. The existence of filter bubbles on Bilibli is found, such that diversity will decrease when viewing Game videos increases. Furthermore, we considered the factors that influence filter bubbles from the perspective of demographics and user behavior. In demographics, female and non-member users are more likely to be trapped in filter bubbles. In user behavior, results of feature importance analysis indicate that the diversity of information consumption of heavy users is higher than others, and both activity and fragmentation have an impact on the formation of filter bubbles, but in different directions. Finally, we discuss the reasons for these results and a theoretical explanation that the filter bubbles effect may be lower than we thought for both heavy and normal users on online platforms. Our conclusions provide valuable insights for understanding filter bubbles and platform management. [ABSTRACT FROM AUTHOR]

This study explored selective exposure and confirmation bias in the choices participants made about which political videos to watch, and whether their political positions changed after they watched videos that either agreed with or opposed their positions on two controversial issues in South Korea: North Korea policy and social welfare policy. The participants completed questionnaires before and after they watched the videos, were asked to select thumbnails of videos before they watched any, and had their brain wave activity measured through electroencephalogram (EEG) as they watched both types of videos. The participants demonstrated selective exposure as they primarily selected video thumbnails with content that matched their political orientations, and they demonstrated confirmation bias as their questionnaire responses after they watched the videos indicated that their positions had hardened. There were also statistically significant differences in alpha, beta, sensory motor rhythm, low beta, mid beta, and fast alpha activity depending on the political orientation consistency between the participants and the videos. Future studies could expand this line of research beyond college students and beyond Asia, and longitudinal work could also be conducted to determine if the obtained patterns remain constant over time. [ABSTRACT FROM AUTHOR]

It is often claimed that social media accelerate political extremism by employing personalization algorithms that filter users into groups with homogenous beliefs. While an intuitive position, recent research has shown that social media users exhibit self-filtering tendencies. In this paper, I apply Hannah Arendt’s theory of political judgment to hypothesize a cause for self-filtering on social media. According to Arendt, a crucial step in political judgment is the imagination of a general standpoint of distinct yet equal perspectives, against which individuals compare their own judgments in order to test their defensibility. I argue that social media inhibit this step by gamifying the pursuit of social status, which encourages users to consider the perspectives of others not for the sake of a general standpoint but for the sake of improving their social status, resulting in self-filtering. Consequently, ameliorating political extremism on social media requires not just reforming the algorithms that deliver content to users, but the interfaces on which users present their social identities. [ABSTRACT FROM AUTHOR]

Accelerated by technological advancements, while online platforms equipped with recommendation algorithms offer convenience to obtain information, it also brought algorithm bias, shaping the norms and behaviors of their users. The filter bubble, conceived as a negative consequence of algorithm bias, means the reduction of the diversity of users’ information consumption, garnering extensive attention. Previous research on filter bubbles typically used users’ self-reported or behavioral data independently. However, existing studies have disputed whether filter bubbles exist on the platform, possibly owing to variations in measurement methods. In our study, we took content category diversity to measure the filter bubbles and innovatively used a combination of participants’ self-reported and website behavioral data, examining filter bubbles on a single online video platform (Bilibili). We conducted a questionnaire survey among 337 college students and collected 3,22,324 browsing records with their informed authorization, constituting the dataset for research analysis. The existence of filter bubbles on Bilibli is found, such that diversity will decrease when viewing Game videos increases. Furthermore, we considered the factors that influence filter bubbles from the perspective of demographics and user behavior. In demographics, female and non-member users are more likely to be trapped in filter bubbles. In user behavior, results of feature importance analysis indicate that the diversity of information consumption of heavy users is higher than others, and both activity and fragmentation have an impact on the formation of filter bubbles, but in different directions. Finally, we discuss the reasons for these results and a theoretical explanation that the filter bubbles effect may be lower than we thought for both heavy and normal users on online platforms. Our conclusions provide valuable insights for understanding filter bubbles and platform management.

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In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play. [ABSTRACT FROM AUTHOR]

It is commonly assumed that personalization technologies used by Google for the purpose of tailoring search results for individual users create filter bubbles, which reinforce users' political views. Surprisingly, empirical evidence for a personalization-induced filter bubble has not been forthcoming. Here, we investigate whether filter bubbles may result instead from a searcher's choice of search queries. In the first experiment, participants rated the left-right leaning of 48 queries (search strings), 6 for each of 8 topics (abortion, benefits, climate change, sex equality, immigration, nuclear family, Islam, and taxation). An independent sample of participants were then asked to select one of these queries for each of the 8 topics. With the exception of the topic of Islam, participants were significantly more likely to select a query corresponding to their own political leaning, compared to other queries, explaining between 12% and 39% of the variance. A second experiment investigated the effect of the political leaning of the same queries on the overall political leaning of Search Engine Result Pages (SERPs) in Google Search. The top six results of each SERP were rated collectively by a third group of participants, explaining 36.3% of the variance across all 48 search terms (p <.00001). That is, (1) participants in our experiments tended to select own-side search queries, and (2) using those queries tended to yield own-side search results when using the Google search engine. Our results are consistent with the notion of a self-imposed filter bubble in which query selection plays a salient role. [ABSTRACT FROM AUTHOR]

This study examined the role of the 'filter bubble,' an algorithm-mediated YouTube video suggestion system, in political polarization and the presence of echo chamber patterns in public engagement. We examined the mechanism by which automated YouTube recommendations augment selective exposure to ideologically similar content and the network-based dynamics of collective polarization. We collected lists of videos recommended by conservative and progressive news media and the accompanying replies using YouTube application programming interfaces (APIs) embedded in YouTube Data Tools and Webometric Analyst 2.0. The study examined similarities in content of related videos and conversation networks between commenters, as well as the relationships between the videos, channels, and commenters. Almost half of the videos had content similar to the original videos offered by both conservative and progressive news media. The filter bubbles in progressive media were not as strong as those in conservative media. More than 30% of the videos recommended by progressive media were from legacy media, while this figure was 25% for conservative sources. This study has significant implications for reverse engineering studies because it collected data through APIs, which are neutral and can access YouTube servers without logging into individual IDs. [ABSTRACT FROM AUTHOR]

When tourists search information online, personalization algorithms tend to contextually filter the vast amount of information and provide them with a subset of information to increase relevance and avoid overload. However, limited attention is paid to the dark side of these algorithms. An influential critique of personalization algorithms is the filter bubble effect, a hypothesis that people are isolated in their own information bubble based on their prior online activities, resulting in narrowed perspectives and fewer discovery of new experiences. An important question, therefore, is whether algorithmic filtering leads to filter bubbles. We empirically explore this question in an online tourist information search with the three-dimensional 'cascade' tourist decision-making model in a two-step experiment. We train two virtual agents with polarized YouTube videos and manipulate them to conduct travel information searches from both off-site and on-site geolocations in Google Search. The first three pages of search results are collected and analyzed with two mathematical metrics and follow-up content analysis. The results do not show significant differences between the two virtual agents with polarized prior training. However, when search geolocations change from off-site to on-site, 39–69% of the search results vary. Additionally, this difference varies between search terms. In summary, our data show that while algorithmic filtering is robust in retrieving relevant search results, it does not necessarily show evidence of filter bubbles. This study provides theoretical and methodological implications to guide future research on filter bubbles and contextual personalization in online tourist information searches. Marketing implications are discussed. [ABSTRACT FROM AUTHOR]

This study aimed to examine the degree of homogeneity versus heterogeneity of individuals' political information environments across offline and online media types and relations with sociodemographic variables, personality, and political attitudes. In two online surveys, German participants (sample 1: N = 686; sample 2: N = 702) provided information on sociodemographic variables, consumption of political news, and voting intentions, and completed the Big Five Inventory and Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) scales. Results revealed that absolutely homogeneous political news consumption was evident for a small proportion of individuals (2.04% and 0.43%). Openness (positively) and Agreeableness (negatively) exhibited significant associations with the degree of heterogeneity of political information environments across samples. No consistent patterns of relations with either the ideological attitudes of RWA and SDO or voting intentions were observed. The findings shed light on the existence of absolutely homogeneous political information environments and "who" might be prone to a more homogeneous versus more heterogeneous information environment. [ABSTRACT FROM AUTHOR]

It is widely believed that we are facing a problem, even a crisis, caused by so‐called "echo chambers" and "filter bubbles." Here, David Coady argues that this belief is mistaken. There is no such problem, and we should refrain from using these neologisms altogether. They serve no useful purpose, since there is nothing we can say with them that we cannot say equally well or better without them. Furthermore, they cause a variety of harms, including, ironically, a tendency to narrow public debate within predetermined limits. [ABSTRACT FROM AUTHOR]

The article focuses on addressing filter bubbles in interactive recommender systems and proposes a counterfactual interactive recommender system (CIRS) that combines offline reinforcement learning with causal inference to burst filter bubbles. Topics include the challenges of filter bubbles, the role of causal inference in recommendation, and the creation of an authentic Reinforcement Learning (RL) environment for evaluation.

Copyright of Bulletin of the 'Carol I' National Defence University / Buletinul Universitatii Nationale de Aparare 'Carol I' is the property of Carol I National Defence University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Whereas 'Web 2.0 technology' has pushed the learning agenda towards connectivity and boundary crossing, in the current 'new new media ontology' the fear that algorithms might block our avenues to knowledge and connections prevails. In response to this, media scholars have argued that knowledge based on the algorithmic experiences of users is key to reformulating the agenda of critical media education. In this study of the algorithmic experiences of secondary education students in the Netherlands, we want to contribute to building such knowledge, making use of the concepts of algorithmic imagination, power and critical evaluation. Results show students build situational, practical-experiential knowledge of algorithmic workings that is closely in line with the features of the interface of the social media platforms they use. Implications for media literacy include providing students with system-level awareness and agency, including insights into the societal-political consequences of algorithmic workings. [ABSTRACT FROM AUTHOR]

Contemporary communication is often characterized using metaphors as “echo chambers” or “filter bubbles.” Despite the popularity of these terms, however, growing concerns about vague definitions are complemented by empirical evidence that contests their widespread existence. Nonetheless, today’s media environment offers ample opportunities for human and/or algorithmic selection processes with undesirable outcomes. To reconcile these two observations, I propose taking a feedback-loop perspective. Such a perspective explains how processes can reinforce themselves without producing catastrophic consequences. This can solve the paradox that while extrapolating from typical filter bubble and/or echo chamber models results in full radicalization within a short period, this outcome has not, despite the long-standing presence of the relevant technologies, become omnipresent. After mapping different types of feedback loops in communication research, I review various empirical approaches, discuss how they can improve research on feedback-loop phenomena, and consider how this can enable us to build better theories of communication in the digital society.

The proliferation of misinformation and the emergence of echo chambers in the online environment pose significant challenges to modern democracies, directly impacting public opinion and social behaviors. This study focuses on the analysis of a Facebook group centered around a prominent Romanian political figure, boasting 93,800 members and averaging ten daily posts. Using advanced machine learning and AI-based hate speech detection, the study uncovers systematic echo chamber construction and the amplification of misinformation. The findings emphasize the influence of online echo chambers on public opinion and underscore the need to maintain information integrity in the media landscape and communication. This research has important implications for scholars, policymakers, and media practitioners, indicating the critical need to address the challenges posed by misinformation and echo chambers in the online environment.

Book review: Bruns, A. (2023). Are Filter Bubbles Real? HSE Publishing House.

Social media platforms are often accused of disproportionally exposing their users to like-minded opinions, thereby fueling political polarization. However, empirical evidence of this causal relationship is inconsistent at best. One reason could be that many previous studies were unable to separate the effects caused by individual exposure to like-minded content from the effects caused by the algorithms themselves. This study presents results from two quasi-experiments in which participants were exposed either to algorithmically selected or randomly selected arguments that were either in line or in contrast with their attitudes on two different topics. The results reveal that exposure to like-minded arguments increased participants’ attitude polarization and affective polarization more intensely than exposure to opposing arguments. Yet, contrary to popular expectations, these effects were not amplified by algorithmic selection. Still, for one topic, exposure to algorithmically selected arguments led to slightly stronger attitude polarization than randomly selected arguments.

The aim of the study is to present the online processes related to religious phenomena appearing on digital platforms, primarily the practice of content filtering (gatekeeping, echo chamber, filter bubble), and a critical review of the scientific literature on the field. At the same time, the goal is to create a theoretical introduction to the special issue and a comprehensive examination of the scientific context. For the first time, the study shows that, in terms of media content, filtering can appear from two directions. One is the selections from different events by professional journalists during content creation. The media theoretical literature refers to this aspect as the phenomenon of gatekeeping. Filtering in the other direction takes place on the part of the receivers, who choose from among the available media contents. This phenomenon has already been described by several media scholars, with the concept of selective exposure (Klapper), Daily Me (Negroponte), echo chamber (Sunstein) or filter bubble (Pariser). Focusing on the phenomenon of the filter bubble, the study presents this theory, its criticism and its relevance to religious content and religious communities. The second part of the study focuses on religious filter bubbles and presents the related investigations so far. It analyses in detail the document published by the Catholic Church on 28 May 2023, entitled Towards Full Presence, Pastoral Reflection on Engagement with Social Media. During the detailed analytical presentation of the text, the study covers how the opportunities and dangers of network communication and the use of social media appear (including the filter bubble) and what solutions the Catholic Church proposes in this regard. [ABSTRACT FROM AUTHOR]

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area. This article is categorized under:Fundamental Concepts of Data and Knowledge > Human Centricity and User InteractionApplication Areas > InternetCommercial, Legal, and Ethical Issues > Ethical ConsiderationsCommercial, Legal, and Ethical Issues > Security and Privacy [ABSTRACT FROM AUTHOR]

As the role of artificial intelligence (AI) agents in information curation has emerged with recent advancements in AI technologies, the present study explored which users would potentially be susceptible to the filter bubble phenomenon. First, a large-scale analysis of conversational agent users in South Korea (N = 2808) was conducted to investigate the relative importance of content optimization algorithms in shaping positive user experience. Five user clusters were identified based on their information technology proficiency and demographics, and a multiple-group path analysis was performed to compare the influences of content optimization algorithms across the user groups. The results indicated that the personalization algorithm generally exhibited a stronger impact on evaluations of an AI agent's usefulness than the diversity algorithm. In addition, increased user age and greater Internet usage were found to decrease the importance of objectivity in shaping trust in AI agents. This study improves the understanding of the social influence of AI technology and suggests the necessity of segmented approaches in the development of AI technology. [ABSTRACT FROM AUTHOR]

The prevalence of artificial intelligence (AI) tools that filter the information given to internet users, such as recommender systems and diverse personalizers, may be creating troubling long-term side effects to the obvious short-term conveniences. Many worry that these automated influencers can subtly and unwittingly nudge individuals toward conformity, thereby (somewhat paradoxically) restricting the choices of each agent and/or the population as a whole. In its various guises, this problem has labels such as filter bubble, echo chamber, and personalization polarization. One key danger of diversity reduction is that it plays into the hands of a cadre of self-interested online actors who can leverage conformity to more easily predict and then control users' sentiments and behaviors, often in the direction of increased conformity and even greater ease of control. This emerging positive feedback loop and the compliance that fuels it are the focal points of this article, which presents several simple, abstract, agent-based models of both peer-to-peer and AI-to-user influence. One of these AI systems functions as a collaborative filter, whereas the other represents an actor the influential power of which derives directly from its ability to predict user behavior. Many versions of the model, with assorted parameter settings, display emergent polarization or universal convergence, but collaborative filtering exerts a weaker homogenizing force than expected. In addition, the combination of basic agents and a self-interested AI predictor yields an emergent positive feedback that can drive the agent population to complete conformity. [ABSTRACT FROM AUTHOR]

Echo chambers and filter bubbles are potent metaphors that encapsulate widespread public fear that the use of social media may limit the information that users encounter or consume online. Specifically, the concern is that social media algorithms combined with tendencies to interact with like-minded others both limits users’ exposure to diverse viewpoints and encourages the adoption of more extreme ideological positions. Yet empirical evidence about how social media shapes information consumption is inconclusive. We articulate how characteristics of platform algorithms and users’ online social networks may combine to shape user behavior. We bring greater conceptual clarity to this phenomenon by expanding beyond discussion of a binary presence or absence of echo chambers and filter bubbles to a richer set of outcomes incorporating changes in both diversity and slant of users’ information sources. Using a data set with over four years of web browsing history for a representative panel of nearly 200,000 U.S. adults, we analyzed how individuals’ social media usage was associated with changes in the information sources they chose to consume. We find differentiated impacts on news consumption by platform. Increased use of Facebook was associatedwith increased information source diversity and a shift toward more partisan sites in news consumption; increased use of Reddit with increased diversity and a shift toward more moderate sites; and increased use of Twitter with little to no change in either. Our results demonstrate the value of adopting a nuanced multidimensional view of how social media use may shape information consumption. [ABSTRACT FROM AUTHOR]

Copyright of Revista Thesis Juris is the property of Revista Thesis Juris and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

How has the diffusion of online communities changed howtheir users construct, view, and define their identity? In thispaper, we choose to approach this issue by considering twoparticular philosophical problems related to personal identity:1) The Characterization Question, namely “which actions, ex-periences, beliefs, values, desires, character traits, and so oncan we attribute to a given person?” 2) “How do self-other re-lations affect the ethical implication of identity construction?”To address them, we adopt a comprehensive framework com-posed of cognitive niches and cognitive niche construction the-ories, and we discuss different philosophical and technologicalnotions. In particular: the Filter Bubble problem, the conceptof affordances, and the Sartrean idea of Bad Faith.

Under which conditions do news recommender systems (NRSs) amplify or reduce selective exposure? I provide the Recommender Influenced Selective Exposure framework, which aims to enable researchers to model and study the conditional effects of NRSs on selective exposure. I empirically test this framework by studying user behavior on a news site where the choice environment is designed to systematically influence selective exposure. Through two preregistered online experiments that simulate different NRSs and unobtrusively log user behavior, I contribute empirical evidence that an NRS can increase or decrease the chance that selective exposure occurs, depending on what the NRS is designed to achieve. These insights have implications for ongoing scholarly debates on the democratic impact of NRSs. [ABSTRACT FROM AUTHOR]

In this contribution, we present some considerations on the role digital artifacts could and should play in 21st-century language teaching. Our argument focuses on social media feeds (in particular: the Instagram feed) as a prototypical example of digital artifacts. We highlight the potentially manipulative force of the Instagram feed using the example of conspiracy theories as a case-in-point. Illustrating our argument with examples from ongoing empirical work, we suggest and elaborate on three challenges entailed by implementing social media feeds in the language classroom: They constitute a moving target due to their dynamics, inspire a false sense of security because of filter bubble as well as third-person effects and confront us with the question of “What can we do in the classroom?”. We conclude by suggesting possible components of Critical Digital Literacy as an answer to this question.

The present work revisits how artificial intelligence, as technology and ideology, is based on the rational choice theory and the techno-liberal discourse, supported by large corporations and investment funds. Those that promote using different algorithmic processes (such as filter bubbles or echo chambers) create homogeneous and polarized spaces that reinforces people’s ethical, ideological, and political narratives. These mechanisms validate bubbles of choices as statements of fact and contravene the prerequisites for exercising deliberation in pluralistic societies, such as the distinction between data and values, the affirmation of reasonable dissent, and the relevance of diversity as a condition indispensable for democratic deliberation.

A pesar del fenómeno de la sobreinformación, las plataformas tecnológicas han encontrado una nueva forma de acceso a los contenidos para los usuarios mediante la personalización. Esta microsegmentación o microtargeting conlleva una creciente polarización de la política con posturas ideológicas enfrentadas entre sí creando cámaras de resonancia cada vez más difíciles de desarticular. Con esta investigación se pretende exponer la teoría de la burbuja de filtros enunciada por Eli Pariser (2017) en el contexto español en Facebook e Instagram, siendo esta última red social una novedad en la investigación del tema en España. Mediante un análisis empírico-analítico sustentado en la creación de tres cuentas ficticias de tendencia de derechas, izquierdas y neutra en ambas plataformas, y en un plazo de 6 semanas, se pretendió alcanzar tres objetivos: (1) determinar el número de días que son necesarios en cada cuenta para que actúen los algoritmos y se perciban en primer lugar los contenidos afines y así descubrir qué predisposiciones actúan antes; (2) observar si la burbuja de filtros conduce a una cámara de resonancia que potencie la polarización política y la exaltación de los extremos en esas redes sociales; y (3) definir la temática de las noticias que más se repite en las diferentes cuentas de las dos plataformas sociales y qué medios son los emisores. Los resultados y conclusiones apuntaron que tanto Facebook como Instagram cuentan con algoritmos que personalizan las interacciones en un corto periodo de tiempo -aunque, curiosamente, con dinámicas diferentes según las tendencias-, lo que aviva la polarización y la crispación en el debate político con medios recurrentes para potenciarlo. [ABSTRACT FROM AUTHOR]

This article considers the link between the increasingly important role of automated information curation online and the rise of what we call 'commercial populism'. We invoke the term to refer to the convergence of populism as a marketing tool – a way of selling, for example, nutritional supplements or survivalist merchandise – with political strategies that cater to the citizen as consumer (whose freedoms are framed in the individual register of personal taste unfettered from civic concerns or constraints). Perhaps unsurprisingly in this context, we draw on the example of Donald Trump's political rise, which while not unrelated to his particular idiosyncrasies, demonstrates how the automated curation of social media aligns itself with what the aggressive rise of commercial populism. The goal of such an analysis is to consider how the combination of hyper-commercialism with the formal attributes of social media contributes to inter-related political pathologies of polarization and conspiracy theory. The consumer-oriented model of personal taste catered to by algorithmic curation highlights the paradox of 'social' media: they promise to enhance the social by displacing or reframing it fundamental a-social. The offloading of social decisions and formation onto commercial, automated systems for curating news and information reinforces this version of individualism, contributing to the forms of misrecognition that enable it. [ABSTRACT FROM AUTHOR]

The aim of the study is to present the online processes related to religious phenomena appearing on digital platforms, primarily the practice of content filtering (gatekeeping, echo chamber, filter bubble), and a critical review of the scientific literature on the field. At the same time, the goal is to create a theoretical introduction to the special issue and a comprehensive examination of the scientific context. For the first time, the study shows that, in terms of media content, filtering can appear from two directions. One is the selections from different events by professional journalists during content creation. The media theoretical literature refers to this aspect as the phenomenon of gatekeeping. Filtering in the other direction takes place on the part of the receivers, who choose from among the available media contents. This phenomenon has already been described by several media scholars, with the concept of selective exposure (Klapper), Daily Me (Negroponte), echo chamber (Sunstein) or filter bubble (Pariser). Focusing on the phenomenon of the filter bubble, the study presents this theory, its criticism and its relevance to religious content and religious communities. The second part of the study focuses on religious filter bubbles and presents the related investigations so far. It analyses in detail the document published by the Catholic Church on 28 May 2023, entitled Towards Full Presence, Pastoral Reflection on Engagement with Social Media. During the detailed analytical presentation of the text, the study covers how the opportunities and dangers of network communication and the use of social media appear (including the filter bubble) and what solutions the Catholic Church proposes in this regard.

Research Question: The research lead to answer the question: Do filter bubbles distort the electoral process in such a way that it violates the right to free elections? Purpose: The main research objective is to show that information bubbles violate decisional privacy, which in consequence leads to a violation of the correctness of the election process. Method: : The research methodology includes a systematic review of scientific papers and studies developed by international organizations and of reports. Results: The research shows the effects of filter bubbles on election process. Thanks to this, we can see that not all information is distributed evenly on the Internet and some of the information does not reach the recipients. The main research objective is to show that filter bubbles violate decisional privacy, which in consequence leads to a violation of the correctness of the election process. The author presents why states should regulate social media in the field of filter bubbles and what steps should be taken. Organization: The development of the information society poses new challenges for the legislator. One of them is the problem of regulating social media and related tools - including filter bubbles. The article presents the influence of filter bubbles on the electoral process. The article reviews the current research on this problem- both empirical and doctrinal. Society: The article deals with a socially relevant topic, which is the manipulation of the electoral process. The examples studied in the work on the cases of recent elections in the USA, Great Britain or Brazil show that the problem affects practically every corner of the world. The result of the study is to show that filter bubbles pose a threat to democracy. Originality: The problem of filter bubbles and their impact on the electoral process is still an unexplored phenomenon. The work analyzes examples of the impact and indicates the steps that should be taken to start work on regulating this phenomenon. This research is based on the concept of social control over the social media algorithm. Limitations / further research: The purpose of this article is to identify problems and review solutions. Research on the problem should be further established on the basis of legal and factual analysis.

In the last decade, digital filter bubbles have become widely discussed phenomena in different fields within the broader discipline of media and communication studies. This paper focuses on the question of why they are problematic for the functioning of the public sphere. This paper argues that algorithmic personalisation can lead to the fragmentation, polarisation, and radicalisation of the public sphere because of the complex relationship between human agency and technology that mutually encourage one another through habitual adaptation. Through the concept of habit, such theoretical grounding enables a critique of existing empirical research regarding the filter bubble effect, with the argument that the main problem is not information isolation or the reduced accessibility/visibility of selected content, but the habitual adaptation of content to individual users, which can explain why users stick to certain content. The article concludes with the finding that the problem of algorithmic personalisation should be studied as a broader historic phenomenon indicative of the decline of the public sphere, which is itself caused by the conflict between public and commercial interests. [ABSTRACT FROM AUTHOR]