Filter Bubbles Research Articles

Bursting filter bubbles in a digital age: Opening minds and reducing opinion polarization through digital platforms

Filter Bubbles, exacerbated by use of digital platforms, have accelerated opinion polarization. This research builds on calls for interventions aimed at preventing or mitigating polarization. This research assesses the extent that an online digital platform, intentionally displaying two sides of an argument with methodology designed to “open minds” and aid readers willingness to consider an opposing view. This “open mindedness” can potentially penetrate online filter bubbles, alleviate polarization and promote social change in an era of exponential growth of discourse via digital platforms.Utilizing “The Perspective” digital platform, 400 respondents were divided into five distinct groups varying in number of articles reading material related to “Black Lives Matter” (BLM). Results indicate that those reading five articles, either related or unrelated to race, were significantly more open-minded towards BLM than the control group. Those who read five race-related articles also showed significantly reduced levels of holding a hardliner opinion towards BLM than control.

An Investigation of the Effectiveness of Facebook and Twitter Algorithm and Policies on Misinformation and User Decision Making

Prominent social media sites such as Facebook and Twitter use content and filter algorithms that play a significant role in creating filter bubbles that may captivate many users. These bubbles can be defined as content that reinforces existing beliefs and exposes users to content they might have otherwise not seen. Filter bubbles are created when a social media website feeds user interactions into an algorithm that then exposes the user to more content similar to that which they have previously interacted. By continually exposing users to like-minded content, this can create what is called a feedback loop where the more the user interacts with certain types of content, the more they are algorithmically bombarded with similar viewpoints. This can expose users to dangerous or extremist content as seen with QAnon rhetoric, leading to the January 6, 2021 attack on the U.S. Capitol, and the unprecedented propaganda surrounding COVID-19 vaccinations. This paper hypothesizes that the secrecy around content algorithms and their ability to perpetuate filter bubbles creates an environment where dangerous false information is pervasive and not easily mitigated with the existing algorithms designed to provide false information warning messages. In our research, we focused on disinformation regarding the COVID-19 pandemic. Both Facebook and Twitter provide various forms of false information warning messages which sometimes include fact-checked research to provide a counter viewpoint to the information presented. Controversially, social media sites do not remove false information outright, in most cases, but instead promote these false information warning messages as a solution to extremist or false content. The results of a survey administered by the authors indicate that users would spend less time on Facebook or Twitter once they understood how their data is used to influence their behavior on the sites and the information that is fed to them via algorithmic recommendations. Further analysis revealed that only 23% of respondents who had seen a Facebook or Twitter false information warning message changed their opinion "Always" or "Frequently" with 77% reporting the warning messages changed their opinion only "Sometimes" or "Never" suggesting the messages may not be effective. Similarly, users who did not conduct independent research to verify information were likely to accept false information as factual and less likely to be vaccinated against COVID-19. Conversely, our research indicates a possible correlation between having seen a false information warning message and COVID-19 vaccination status.

Turning Filter Bubbles into Bubblesphere with Multi‐Viewpoint KOS and Diverse Similarity

ABSTRACTThe filter bubble phenomenon and its negative societal effects have been extensively explored in the literature in the past decade. However, the ability of modern AI‐based systems to create personalized information bubbles, that is, to classify similar contents and users into clusters according to their interests and behavior, can actually be quite beneficial if utilized and managed properly and ethically. In this article we present ongoing research that aims to refine such bubble‐building smart systems by adopting an ethical, multi‐perspective approach that allows for linking isolated bubbles into a consolidated bubblesphere and offering users a choice to explore diverse bubbles related to their topics of interest. To implement the proposed approach, content matching should be based on diverse similarity, which can be derived from a multi‐viewpoint KOS. In addition, the study explores how such a multi‐viewpoint KOS and bubblesphere can be constructed using Wikidata's ranks and qualifiers.

Censorship Is Not a Panacea: Access To Information in a Resilient Society

AbstractInformation resiliency is a difficult idea to conceptualize, though it seems to include resistance to mis‐ and dis‐information, algorithmic injustices, and filter bubbles. A possible solution to these information challenges and the current “infodemic” is to restrict access to and circulation of certain information. Social media platforms, in particular, have an extraordinarily wide reach in contemporary society. While compelling these platforms to restrict information may be appealing, at first glance, there are inherent problems of power and control bound up in the suggestion of censorship (or, as it is sometimes framed, content moderation). Rules about permitted speech can be wielded in ways that increase harm to already marginalized populations. In this panel, we discuss these ideas and debate the merits of restricting access to information via social media content moderation. We anticipate a robust discussion with attendees

Science by YouTube: an Analysis of YouTube’s Recommendations on the Climate Change Issue

Citizens increasingly use online platforms, and in particular YouTube, to get informed. This paper addresses the role of YouTube’s recommendation system in science communication to assess whether YouTube's recommendation system is creating filter bubbles on the climate change issue. It, therefore, contributes to the growing literature that finds that such filter bubbles actually do not exist. The paper shows that users are likely to get content with similar opinions, at least for a few recommended videos in a row. In addition, it was observed that the same promoters of climate denial appear over and over again in different recommendations. Given all that, YouTube can lead to the creation of filter bubbles where users do not have access to all relevant scientific information.

Propaganda in America: How to Recognize and Defend Against It

Corporations and governments use modern propaganda techniques to influence and manipulate the public, incurring social and environmental consequences which are compounding over time. This paper provides both historical and modern examples of propaganda, then highlights key issues that arise as a byproduct, such as planned obsolescence, astroturfing, and the internet filter bubble. Companies like Google and Facebook use proprietary algorithms to shape the internet as well as their users’ minds through dopamine-driven feedback loops. The effects of a consumer driven culture spill over into the environment, with plastic pollution and e-waste littering the shores of distant countries. This paper details how plastic pollution in the environment has made its way back into the human body, while propaganda is used to distract people with targeted advertising using techniques pioneered by Edward Bernays. Propaganda is a multifaceted subject which requires historical context to understand and identify. This is an informative paper which has the goal of arming readers with an ability to recognize propaganda and defend against its negative effects. It is vital for Americans to become aware of the propaganda being used to manipulate them. Otherwise, societal issues caused by social media and other factors will continue to erode our ability to communicate and think critically.

Emotional Analysis of Tweets About Clinically Extremely Vulnerable COVID-19 Groups.

BackgroundClinically extremely vulnerable (CEV) individuals have a significantly higher risk of morbidity and mortality from coronavirus disease 2019 (COVID-19). This high risk is due to predispositions such as chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, smoking, or extreme age (≥75). The initial COVID-19 preventive measures (use of face masks, social distancing, social bubbles) and vaccine allocation prioritized this group of vulnerable individuals to ensure their continued protection. However, as countries start relaxing the lockdown measures to help prevent socio-economic collapse, the impact of this relaxation on CEVs is once again brought to light. In this study, we set out to understand the impact of policy changes on the lives of CEVs by analyzing Twitter data with the hashtag #highriskcovid used by many high-risk individuals to tweet about and express their opinions and feelings.MethodologyTweets were extracted from the Twitter API between March 01, 2022, and April 21, 2022, using the Twarc2 tool. Extracted tweets were in English and included the hashtag #highriskcovid. We evaluated the most frequently used words and hashtags by calculating term frequency-inverse document frequency, and the location of tweets using the tidygeocoder package (method = osm). We also evaluated the sentiments and emotions depicted by these tweets using the National Research Council sentiment lexicon of the Syuzhet package. Finally, we used the latent Dirichlet allocation algorithm to determine relevant high-risk COVID-19 themes.ResultsThe vast majority of the tweets originated from the United States (64%), Canada (22%), and the United Kingdom (4%). The most common hashtags were #highriskcovid (25.5%), #covid (6.82%), #immunocompromised (4.93%), #covidisnotover (4.0%), and #Maskup (1.40%), and the most frequently used words were immunocompromised (1.64%), people (1.4%), disabled (0.97%), maskup (0.85%), and eugenics (0.85%). The tweets were more negative (19.27%) than positive, and the most expressed negative emotions were fear (13.62%) and sadness (12.47%). At the same time, trust was the most expressed positive emotion and was used in relation to belief in masks, policies, and health workers to help. Finally, we detected frequently co-tweeted words such asmass and disaster, deadly and disabling, high and risk, public and health, immunocompromised and people, mass and disaster, and deadly and disabling.ConclusionsThe study provides evidence regarding the concerns and fears of high-risk COVID-19 groups as expressed via social media. It is imperative that further policies be implemented to specifically protect the health and mental wellness of high-risk individuals (for example, incorporating sentiment analyses of high-risk COVID-19 individuals such as this paper to inform the evaluation of already implemented preventive measures and policies). In addition, considerable work needs to be done to educate the public on high-risk individuals.

Coronavirus Conspiracy Theories in Southeast Europe: (Non-)Believers, Social Network Bubbles, and the Discourse of Blame

ABSTRACT Using survey and social network evidence from Southeast Europe, we advance the understanding of conspiracy theories and politics related to the coronavirus pandemic in three ways: (1) we show that beliefs in coronavirus conspiracy theories are related to ideological support for a nationalist vision of society and socialist vision of the economy; (2) we also show that both conspiracy believers and nonbelievers are living in bubbles of the like-minded; and (3) we use the tools of natural language processing to elucidate the unambiguous differences in the discourse related to the coronavirus used by conspiracy believers and nonbelievers.

User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI services

User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI services

Effect of bubble size on the rising behavior in pure water medium

The phenomenon of rising bubbles in pure water is an important aspect of the bubble and liquid interactions. Due to several forces that act on the bubble accompanied by the pressures inside and outside the bubble, the rising bubble behaviour may vary depending on a few parameters such as the characteristics of the bubble and liquid medium. In general, the bubble increase occurs following the drag motion determined by the bubble drag coefficient but under certain conditions bubble also may move by a rolling pattern. Both drag and rolling are related to other forces that worked on the bubble, particularly the buoyancy force. The velocity of them may be different due to the mechanism types of motions. Rolling motion can be assumed does not occur for a very small bubble so there is only a relation between size and velocity. Several observations of bubble sizes have shown that for a very small bubble there is a linear relationship between size and rising velocity. Meanwhile, for a larger bubble, the rising velocity becomes faster almost seems an exponential relation. The increase of the bubble size results in a higher buoyancy force so that the bubbles rise more easily to reach the water surface. This paper aimed to prove that the small bubble will show linear relation of the rising velocity depending on the size. Our experimental observation has shown that the linearity of velocity only occurred for a very small bubble. This linearity gradient becomes quite important for predicting the rising pattern of other various bubble sizes.

Present security of the neighbourhood urban parks considering SARS-CoV-2 potential spreading – A case study in Ursynów district in Warsaw

Motives: It is expected that COVID-19 pandemic will impact future design, use and perception of urban green spaces such as public parks. How to facilitate residents’ safe use of the urban green spaces and reduce health risks has become important question to consider by landscape architects and municipal authorities responsible for the public green areas resources. This research focuses on examination of urban parks of a typical residential district of a large city in terms of their security in time of COVID-19 pandemic. Aim: In urban parks of Ursynów district in Warsaw were assessed: quality and present security and possibilities of re-adjustments to increase their security. Results: Ursynów district has good quality, accessible parks. Larger parks offer picnic areas and shelters, have more than one, entrance and paths with widening for benches. All playgrounds are enclosed with gates to install dispenser for hand sanitization. Gates and knobs are made of metal allowing frequent disinfection. Benches and tables are usually properly distanced and those in shelters may be marked ‘for visitors from one social bubble only’ but in the future an emphasis should be put on more scattered pattern of park furniture. All parks are equipped with trash cans, but usually open or semi-closed, which should be avoided.

Unexpected interest recommender system with graph neural network

AbstractTraditional recommender systems often face the filter bubble problem when they focus on recommending familiar items to users. The over-specialized recommended contents will make users bored. To solve this problem, researchers have proposed models that focus on unexpectedness, but these models all suffer from incomplete learning of features. To address this problem, we propose an unexpected interest recommender system with graph neural network (UIRS-GNN). First, we preprocess the input data with a graph convolutional network. It enriches user and item feature vectors by aggregating neighborhood information. Second, we transform the GRU and propose the attention-based long short-term gated recurrent unit network to learn user preferences hidden in historical behavior sequences. Then, we input the preprocessed feature vectors of users and items into the unexpected interest model, and solve the problem of insufficient feature information learning by aggregating neighborhood information. Furthermore, our model also alleviates data sparsity due to our deep learning feature information. Finally, empirical evaluations with several competitive baseline models on three real-world datasets reveal the superior performance of UIRS-GNN.

Effects of the news finds me perception on algorithmic news attitudes and social media political homophily

AbstractPrior literature on political filter bubbles suggests an overall positive association between social media use and political networks diversification. Sometimes, this might not be the case. This study argues that the News Finds Me perception (NFM) or the belief that “one” can be well-informed about public affairs without actively seeking information as news will find “me” through “my” networks, tend to nurture a positive attitude toward algorithmic news gatekeeping. Likewise, NFM’s news over-reliance on one’s social network support the development of homogeneous information and discussion political networks in social media (political homophily). Results based on a variety of ordinary least squares regression models (cross-sectional, lagged, and autoregressive) from a U.S. representative panel survey, as we all as autoregressive structural equation model tests, indicate that this is indeed the case. This study serves to specifically clarify when and how social media and the NFM facilitate politically homogeneous filter bubbles.

Bubble in Carbon Credits during COVID-19: Financial Instability or Positive Impact (“Minsky” or “Social”)?

Incentivizing businesses to lower carbon emissions and trade back excess carbon allowances paved the way for rapid growth in carbon credit ETFs. The use of carbon allowances as a hedging alternative fueled this rally further, causing a shift to speculation and forming repetitive bubbles. Speculative bubbles are born from euphoria, yet, they are relatively predictable, provided their pattern matches the log periodic power law (LPPL) with specific stylized facts. A “Minsky moment” identifies a clear speculative bubble as a signal of financial system instability, while a “Social bubble” is regarded as relatively positive, increasing in the long run, infrastructure spending and development. The aim of this paper is to investigate whether various carbon credit bubbles during the pandemic period caused financial instability or had a positive impact (“Minsky” or “Social”). Particularly, we investigate the carbon credit bubble behavior in the ETF prices of KRBN, GRN (Global Carbon Credit tracking ETFs), and the SOLCARBT index during the COVID-19 pandemic period by adopting the log-periodic power law model (LPPL) methodology, which has been widely used, over the past decade, for detecting bubbles and crashes in various markets. In conclusion, these bubbles are social and propelled by the newfound interest in carbon credit trading, for obvious reasons.

Search engine paths not taken

This ongoing study builds on previous work that established a theoretical model for developing search expertise; this model then guided the design of an MLIS course on advanced search methods. To further enrich the course content, the design included a look-back at 1970s design decisions affecting search engines and a look-forward from challenges in today’s information landscape, such as algorithmic bias, filter bubbles, and market-driven results ranking. The study is evaluating the new course by analyzing peer-to-peer discourse in online discussion forums, using template analysis methodology. This paper reports on the analytical processes being used, preliminary results, and next steps.

Self-imposed filter bubbles: Selective attention and exposure in online search

It is commonly assumed that algorithmic curation of search results creates filter bubbles, where users’ beliefs are continually reinforced and opposing views are suppressed. However, empirical evidence has failed to support this hypothesis. Instead, it has been suggested that filter bubbles may result from individuals engaging selectively with information in search engine results pages. However, this “self-imposed filter bubble hypothesis” has remained empirically untested. In this study, we find support for the hypothesis using eye-tracking technology and link selection data. We presented partisan participants (n = 48) with sets of simulated Google Search results, controlling for the ideological leaning of each link. Participants spent more time viewing own-side links than other links (p = .037). In our sample, participants who identified as right-wing exhibited a greater such bias than those that identified as left wing (p < .001). In addition, we found that both liberals and conservatives tended to select own-side links (p < .001). Finally, there was a significant effect of trust, such that links associated with less trusted sources were attended less and selected less often by liberals and conservatives alike (p < .001). Our study challenges the efficacy of policies that aim at combatting filter bubbles by presenting users with an ideologically diverse set of search results.

Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date: August 31 st 2022

Political Communication in the Age of Populism and Mediatization

The loss of mainstream political parties’ credibility, the mistrust over the traditional sources of information, the rise of social media in the public scenario, and the ideological polarization splitting states into different sides seem to be the most recent personality of our democracy. Through this study, we will understand to what extent populist rhetoric affects our decisions in both the civic and personal sphere. Are we owners of our mindset, or are we just victims of the filter bubble? After investigating the basic concepts of political communication and explaining the factors of populism and mediatization that we are experiencing today, we must go further to study whether this ideological fragmentation of states should concern us, and more importantly, whether it is going to increase in Spain. For this, a very important factor is the youth, since are the new generations on whom the future of politics and ideological bias depend.

The complex link between filter bubbles and opinion polarization

There is public and scholarly debate about the effects of personalized recommender systems implemented in online social networks, online markets, and search engines. Some have warned that personalization algorithms reduce the diversity of information diets which confirms users’ previously held attitudes and beliefs. This, in turn, fosters the emergence opinion polarization. Critics of this personalization-polarization hypothesis argue that the effects of personalization on information diets are too weak to have meaningful effects. Here, we show that contributions to both sides of the debate fail to consider the complexity that arises when large numbers of interdependent individuals interact and exert influence on one another in algorithmically governed communication systems. Summarizing insights derived from formal models of social networks, we demonstrate that opinion dynamics can be critically influenced by mechanisms active on three levels of analysis: the individual, local, and global level. We show that theoretical and empirical research on these three levels is needed before one can determine whether personalization actually fosters polarization or not. We describe how the complexity approach can be used to anticipate and prevent undesired effects of communication technology on public debate and democratic decision-making.

Fashion, filter bubbles and echo chambers: questions of privacy, identity, and governance

ABSTRACT The discourse on filter bubbles and echo chambers applies to the use of social media analytics and consumer profiling for behavioural advertising in the fashion industry, this being relevant to an individual’s autonomy and control of personal information. However, we need to expand on the concept of filter bubbles and echo chambers to define the contours of self-exposure within the algorithmic context applied to the social and personal aspects of fashion. This paper claims that filter bubbles and echo chambers in fashion have an impact on the parameters and conditions of the right to privacy, influencing an individual’s perception and self-relationality. An analysis of the ECtHR’s interpretation of Article 8 of the ECHR Convention reveals that we need to shape notions of personal development and autonomy to include an individual’s plurality of needs, desires, and beliefs, as well as unconscious associations with fashion identity.