#BigTech @Minors: social media algorithms have actionable knowledge about child users and at-risk teens

The role of social media in providing fitness-related health information has been widely discussed; however, there is a notable lack of research on fitness-related health information behaviors among youth within the social media context. This study aims to address this gap by integrating Self-Determination Theory (SDT)-based internal factors and external factors (social media algorithms and source credibility). A voluntary sample of 600 participants, aged 15 to 29, was recruited. Data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to examine the relationships between variables. The analysis revealed that all three intrinsic needs-competence, autonomy, and relatedness-along with social media algorithms and source credibility, positively correlated with fitness-related health information use behaviors among youth. Additionally, social media algorithms moderated the relationship between the need for relatedness and fitness-related health information behavior. These findings provide new insights into developing health communication strategies on social media, particularly targeted toward the youth demographic, enhancing our understanding of effective health information dissemination in digital environments.

This study aims to explore how social media algorithms influence brand visibility and customer engagement for new ventures, identifying key strategies and challenges faced by these businesses. This qualitative study employed semi-structured interviews to gather in-depth insights from 18 participants, including founders and social media managers of new ventures. Participants were selected using purposive sampling to ensure relevance to the study's focus. Interviews were transcribed and analyzed using NVivo software, with data saturation achieved after 15 interviews. Thematic analysis was conducted to identify recurring themes and subthemes related to social media algorithms, brand visibility, and customer engagement. Three main themes emerged from the analysis: the impact of algorithms on brand visibility, customer engagement through social media, and challenges in leveraging algorithms. Within these themes, subcategories included platform-specific algorithm changes, content optimization techniques, timing and frequency of posts, interaction with algorithmic features, engagement tactics, content personalization, influencer collaborations, resource allocation, platform dependency, knowledge gaps, and data privacy concerns. Participants highlighted the need for continuous adaptation to algorithm changes, effective content strategies, and personalized engagement tactics to enhance visibility and engagement, while also facing significant challenges such as resource constraints and maintaining ethical practices. The study concludes that understanding and effectively leveraging social media algorithms is essential for new ventures to enhance brand visibility and customer engagement.

Social media is becoming a dominant factor in electoral processes, with platforms like Facebook, Twitter, YouTube and WhatsApp increasingly having a tremendous influence on the creation, dissemination and consumption of political content in Africa. The social media platforms collect and collate large amounts of data from their users. The power of algorithms in filtering, ranking, selecting and recommending content makes them a significant factor in election campaigns. The objective of this chapter is to examine how these social media algorithms and bots are influencing elections in the African context. The chapter argues that social media algorithms and bots are slowly changing the dynamics of elections in Africa, presenting new prospects as well as challenges for the fledgling democracies.

This chapter offers an analytical framework that captures the complex dynamics of the relationship between social media algorithms and human users, and contributes to and intervenes in scholarly debates regarding political implications of social media algorithms. Detailing the ways in which social media are central to the contemporary media landscape in which emotional capitalism is pursued, intensified, and amplified, the chapter demonstrates the affective constitution of communication networks. Given that social media algorithms are largely designed to enhance targeted advertisement and built on sorting principles, this biases them towards the superlative, notably contents that generate extreme, binary affective gestures, such as love or hate. The neoliberal social media landscape, affective networks, and social media algorithms, taken together, assemble a habitat that privileges and encourages the emergence of political clusters that resort to binary affective rhetorics in what the author terms “algorithmic enclave.” This enclave is a discursive algorithmic enclave where users voluntarily form an affective political cluster exclusively dedicated to promote the wellbeing, rights, and interests of their own, while negating the rights of “the Others.”

This article looks at interest-driven and informal social media practices that have flourished in the pandemic period and its ensuing renaissance of domesticity. It investigates how tending plants and discussing them on social media serve as a particular site for connecting around loving and taking care of plants. Its focus is on the discursive means with which posters – guided by social media algorithms – rhetorically co-construct a morally acceptable version of a pandemic lifestyle around houseplants. More specifically, drawing on multimodal discourse studies, critical sociolinguistics and work on digital surveillance, it investigates how members of a Finland-based social media site observe and monitor themselves and others via their linguistically heterogeneous and multimodal posts. The paper demonstrates how constructions of tending plants highlight a normative subject who besides cultivating plants also cultivates themselves and others in the allegedly safe microcosm of the home, surrounded by the risk-ridden, tension-full, dangerous pandemic world. In the same way, as in many other types of informal and interest-driven social media activities, surveillance forms a crucial part of the routine digital activities and interactions about and around plants. Three manifestations of surveillance are discussed in detail: site-specific panoptic surveillance, peer surveillance and self-surveillance.

The purpose of this work is to consider the possibility of nonlinear influence of social media algorithms to the users opinions. A social media inherent algorithm of information ranging interacts with the user inherent bias and that increases the positive feedback loop. The result of this interaction is receiving by the user the only one side of an opinion and the user looses the very possibility to receive the opposite information. The conditions for the society polarization by means of a social media are investigated. Methods. In this paper, a model of users opinions dynamics was studied. There are two types of user’s strategy was considered: strategy 1, when a user puts "like" on information with proximity to his own view, but differed in any direction; strategy 2, when a user puts "like" on information along his own view, but more strict. Results. It was shown that for strategy 1 the society comes to a consensus, but for strategy 2 the society polarizes to the two opposite views. Considering the mixed society, where both strategies are used, it was found that the bifurcation to the society polarization appears when there are more than 40% of people using strategy 2. Conclusion. The inherent algorithms of social media, which are created to adapt in coming information to the user’s interests, creates or amplifies the bias of the user’s opinion and locks the user in an information chamber of only one type. That effect is substantially created by the social media algorithm itself. Thus interaction of users within a social media may increase the polarization of a society more than if they would communicate offline.

While social media algorithms have brought positive effects, they have also brought a series of crises that have sparked public concern about algorithmic technology. In response, many countries have adopted a series of regulations on platform algorithms. The EU mainly adopts a collaborative regulatory model between administrative agencies and private institutions, emphasizing both ex-ante and ex-post regulation of algorithms, and further reinforcing the obligations and responsibilities of social media platforms, as well as adopting focused regulation on large social media platforms. The U.S. mainly focuses on external accountability, industry self-regulation, and the protection of freedom of expression and democratic politics. By analyzing the dynamics and practices of administrative regulation of social media algorithms in the EU and the U.S., it is suggested that China should: build a multiple regulation model, realize the substantial participation of other subjects, explore the hierarchical and categorical regulation of social media platforms, strengthen the construction of anti-monopoly for large social media platforms, and enhance the whole process regulation of social media algorithms, with a belief of building a perfect social media algorithm regulation system.

Drawing on Catholic social ethics and Pope Francis’ vision of a culture of encounter, this essay demonstrates that social media algorithms function presently as “structures of sin.” Algorithms that govern social media platforms amplify hatred, spread mis(dis)information, and foment political polarization and extremism. They are obstacles to a culture of encounter that encourages solidarity across difference. As structures of sin, social media platforms are particularly potent catalysts for online radicalization. In the U.S. context, social media algorithms contributed to the rise of two overlapping extremist communities involved in the political violence on January 6, 2021 at the U.S. Capitol: Christian nationalists and the QAnon movement. With an eye to dismantling this structure of sin, the essay offers faith-based communities’ practical suggestions for enacting solidarity with those most susceptible to online radicalization for advocating for public legislation to push social media companies to design algorithms responsible to the common good.

It is widely established that social media afford social movement (SM) organizations new ways of organizing. Critical studies point out, however, that social media use may also trigger negative repercussions due to the commercial interests that are designed into these technologies. Yet empirical evidence about these matters is scarce. In this article, we investigate how social media algorithms influence activists’ actualization of collective affordances. Empirically, we build on an ethnographic study of two SM organizations based in Tunisia. The contributions of this paper are twofold. Firstly, we provide a theoretical framework that specifies how algorithms condition the actualization of three collective affordances (interlinking, assembling, augmenting). Specifically, we show how these affordances are supported by algorithmic facilitation, that is, operations pertaining to the sorting of interactions and actors, the filtering of information, and the ranking and aggregation of content. Secondly, we extend the understanding of how social media platforms’ profit-orientation undermines collective action. Namely, we identify how algorithms introduce constraints for organizing processes, manifested as algorithmic distortion, that is, information overload, opacity, and disinformation. We conclude by discussing the detrimental implications of social media algorithms for organizing and civic engagement, as activists are often unaware of the interests of social media-owning corporations.

The process of digitization gives easy access to online communication. This evolution should, in theory, make political processes more democratic and increase political accountability since social networks provide instant contact with politicians and provide for accountability. Yet digitalization has also introduced new threats for democracy, with the advent of fake news, online bot networks, artificial intelligence (AI), and social media algorithms. These developments are linked to a rise of far-right parties in Europe and around the world in recent years. Populism and extremism can thrive in the digital ecosystem because the relative costs of reaching a mass audience on social networks to spread their messages are low, and it is an efficient way of reaching out to potential supporters. This article examines how echo chambers form and function on social media platforms and how they are connected to driving societal polarization. The analysis is based on desk research and critical inquiry through the lenses of echo chambers, polarization, and the logic of virtual communication. The first aim of the article is to study how social media allows echo chambers to form and how online spaces are exploited by political actors. The second goal is to observe patterns of social polarization and explore how echo chambers are connected to this process. Keywords: Digitalization, virtual communication, social media algorithms, echo chambers, polarization, confirmation bias, selective exposure, homophily

Social media content filtering algorithms can both provide desired personalized content and ads for users. However, sometimes these recommendations can resemble individual private information. How might users navigate these experiences to best manage their private information? The present exploratory study utilizes the rules- and systems-based framework of communication privacy management (CPM) theory to explore social media users’ experiences of privacy breakdowns with social media algorithms and investigates what users do in response to said breakdowns. These responses were refined using content analysis and divided into different categories of privacy breakdowns and recalibration strategies. Implications for future research surrounding human-machine communication privacy management are discussed in light of our findings.

The need and desire to communicate information, ideas, and emotions has been strong in humans. It has evolved from traditional forms such as story-telling, plays, paintings, carvings, community gatherings to digital forms of communication. As digital social media platforms are growing at a rapid pace and are touching billions of people worldwide, it becomes important to understand how these platforms work and their effect on users and the society. The present paper analyzes social media algorithms, how they rank content, and how Machine Learning drives these algorithms. The shortcomings of social media algorithms and their future are also discussed.

This article explores the dynamics of rumors, fake news, disinformation, and algorithm-driven propaganda on social media during the period of July–August 2024 in Bangladesh, particularly in relation to the mobilization of young jihadist groups. By combining critical discourse analysis, algorithmic studies, and ethnographic insight, this study investigates how militant ideologies were digitally diffused, manipulated, and algorithmically amplified across major platforms like Facebook, TikTok, YouTube, and Telegram. The paper interrogates the algorithmic curation of content that contributed to militant radicalization, the role of socio-political instability, and the systemic exploitation of religious and communal sentiments. It further evaluates state responses, counter-narratives, and the limitations of digital governance mechanisms in addressing algorithmically supported disinformation ecosystems. Using case studies and field interviews, the research provides a comprehensive analysis of digital militarism in Bangladesh and concludes with policy-oriented recommendations on countering digital extremism. This study investigates the dynamics of rumors, fake news, disinformation, and propaganda disseminated through social media algorithms during the July–August 2024 wave of young jihadist hostility in Bangladesh. Drawing from digital ethnography, content analysis, and algorithmic auditing, the research explores how extremist narratives were produced, circulated, and amplified across popular platforms such as Facebook, TikTok, and Telegram. The study critically examines the role of algorithmic curation in intensifying socio-political tensions, enabling ideological recruitment, and fostering digital echo chambers that radicalize youth. By analyzing over 1,000 pieces of multimedia content and tracking network diffusion patterns, this research highlights the interplay between platform design, user behavior, and the strategic manipulation of information. Findings reveal that disinformation campaigns were not only orchestrated by militant sympathizers but also inadvertently supported by platform algorithms favoring engagement metrics over content integrity. The study underscores the urgent need for algorithmic transparency, localized content moderation, and strategic counter-narratives to mitigate the risks of digital radicalization in volatile socio-political contexts like Bangladesh. The paper contributes to scholarship at the intersection of media studies, political communication, and counterterrorism, with implications for policymakers, platform regulators, and civil society stakeholders in South Asia.

In the context of the evolving digital landscape, this research paper explores the operations of social media algorithms and their potential to identify individuals with neurodevelopmental disorders. Neurodiversity is a significant aspect of contemporary mental health discussions in the digital age. The primary objective is to unravel the mechanisms of social media algorithms and assess their capacity to identify users diagnosed with neurodevelopmental disorders. Employing a dual-method approach, both quantitative and qualitative methodologies were utilized. A quantitative survey targeted general social media users, collecting 143 valid responses to gauge their interactions with algorithm-generated content. Simultaneously, a qualitative survey involved interviews with artificial intelligence specialists, providing expert insights into algorithmic functionality. The analysis revealed that social media algorithms operate on recommender systems, categorizing content based on users' historical preferences. However, these algorithms lack the inherent capability to identify neurodevelopmental disorders. Instead, user-interacted content influences subsequent algorithmic recommendations.

Countering cyber extremism is a crucial challenge in the digital age. Social media algorithms, if designed and used properly, have the potential to be a powerful tool in this fight, development of technological solutions that can make social networks a safer and healthier space for all users. this study mainly aims to provide a comprehensive view of the role played by the algorithms of social networking sites in countering electronic extremism, and clarifying the expected ease of use by programmers in limiting the dissemination of extremist data. Additionally, to analyzing the intended benefit in controlling and organizing digital content for users from all societal groups. Through the systematic review tool, a variety of previous literature related to the applications of algorithms in the field of online radicalization reduction was evaluated. Algorithms use machine learning and analysis of text and images to detect content that may be harmful, hateful, or call for violence. Posts, comments, photos and videos are analyzed to detect any signs of extremism. Algorithms also contribute to enhancing content that promotes positive values, tolerance and understanding between individuals, which reduces the impact of extremist content. Algorithms are also constantly updated to be able to discover new methods used by extremists to spread their ideas and avoid detection. The results indicate that it is possible to make the most of these algorithms and use them to enhance electronic security and reduce digital threats.