‘They have my kundli ’: Understanding urban upper-middle-class Indians’ beliefs about social media algorithms

Abstract

This study examines urban upper-middle-class Indian social media users’ knowledge of, attitudes toward, and practices with social media algorithms. Interviews (N = 30) reveal they primarily use Instagram and have a basic understanding of algorithms, with algorithmic awareness and knowledge functioning as forms of cultural capital. They also articulate folk theories about how algorithms operate, some of which reflect a relational rather than individually-centered view of algorithmic personalization. Furthermore, they express mixed attitudes toward algorithms, including a positive view of algorithms as smart technologies they can control to view global trends, and a critical view of algorithms as manifestations of platform power associated with surveillance and advertising. These views are informed by long-standing cultural beliefs equating technology with globalization and aspiration, contemporary concerns about social media’s effects, and participants’ social status. This study therefore expands our understanding of how context shapes algorithmic sensemaking, presenting insights from a critical Global South setting.

Keywords

algorithms India Instagram social media

Introduction

Algorithms are technically any ‘encoded procedures for transforming input data into a desired output, based on specified calculations’ (Gillespie, 2014: 167). However, the term algorithms is colloquially used to refer to the technologies that sort, filter, and recommend content on digital media platforms, particularly on social media (Narayanan, 2023). Algorithms have become central to our experiences in digitally mediated spaces, leading to an increased interest in how users experience and make sense of these technologies (Bucher, 2018; Cotter, 2019; Kant, 2020; Siles, 2023). This user-centered research is important because the technical specificities of these proprietary algorithms are not well known (Bucher, 2018), making algorithms ‘productive and powerful by way of the meanings ascribed to them in concrete contexts’ (Lomborg and Kapsch, 2019: 747). However, the contexts scholars have focused on are largely situated in the Global North, with little attention paid to users in the Global South (Hargittai et al., 2020; Siles, 2023). This perpetuates the myth of data universalism, the assumption that algorithms are experienced in the same way across cultures and geographies (Milan and Treré, 2019).

To address this issue, I interview a sample of urban Indian social media users, the majority of whom are from upper-middle-class backgrounds, to examine their knowledge of, attitudes toward, and practices with social media algorithms. In doing so, I draw on existing research on the cognitive, affective, and behavioral dimensions of how people relate to algorithms (Hargittai et al., 2020; Oeldorf-Hirsch and Neubaum, 2023b; Swart, 2021). I also consider how these dimensions are informed by the way technology and social media are viewed in the Indian context, including how urban high-status Indians have historically equated new technologies with aspiration, modernity, and globalization (Fernandes, 2006; Sukumar, 2019), and contemporary concerns about social media’s effects (Sarwatay and Raman, 2021). Furthermore, I highlight how social status shapes the way urban upper-middle-class Indians discuss awareness and knowledge of algorithms, and how cultural norms inform their beliefs about algorithmic personalization. This study therefore responds to calls for more contextually- and culturally-specific research on how users experience algorithms in the Global South (Siles, 2023). It also addresses a gap in the research on social media use in India, which has largely focused on the messaging application WhatsApp rather than platforms where users engage with algorithmic feeds (Malhotra, 2025).

Literature review

Users and algorithms

Researchers focus on various aspects while examining how users relate to digital media algorithms. Some examine how users make sense of algorithms while using digital platforms for specific purposes, such as news consumption (Fletcher and Nielsen, 2019) or media streaming (Cole, 2024). Others look at how specific user groups engage with algorithms, including social media influencers (Cotter, 2019), gig workers (Bonini and Treré, 2024), and socially marginalized groups (Alper et al., 2023). There is also research on how everyday digital media users more generally perceive and experience algorithms, with scholars focusing on three main dimensions – awareness and knowledge of algorithms (cognitive dimension), attitudes toward algorithms (affective dimension), and practices and behaviors related to algorithms (behavioral dimension) (Oeldorf-Hirsch and Neubaum, 2023b; Swart, 2021). These dimensions serve as a consistent organizing framework for this study, starting with the literature I draw on.

Cognitive dimension – awareness and knowledge

Early research on users’ understanding of algorithms focused on basic awareness (Oeldorf-Hirsch and Neubaum, 2023b), defined as ‘knowing that a dynamic system is in place that can personalize and customize the information that a user sees or hears’ (Hargittai et al., 2020: 771). Recent scholarship indicates that such basic awareness is common (Swart, 2021). However, demographic factors can impact awareness, with younger and more educated users demonstrating greater algorithmic awareness (Oeldorf-Hirsch and Neubaum, 2023a), although some scholars challenge the idea of an intergenerational divide (Dogruel et al., 2022).

Beyond basic awareness, researchers examine users’ algorithmic knowledge, which encompasses knowledge of what algorithms are, how they work, the data and information they rely on, and the impact they have (Cotter and Reisdorf, 2020). Most research on algorithmic knowledge draws on the concept of folk theories, intuitive theories users have about algorithms (DeVito et al., 2017). DeVito et al. (2017) distinguish between operational folk theories – theories about the specific ways in which algorithms operate and the criteria they rely on to filter content, and abstract folk theories – broader ideas about the impact algorithms have. An example of an operational folk theory is the personal engagement theory, the idea that algorithms base content recommendations on users’ online activity (scrolling, clicking, liking, commenting, etc.) (DeVito et al., 2017; Dogruel, 2021; Eslami et al., 2016). Meanwhile, an example of an abstract folk theory is the perception that algorithms are tools for platforms to surveil users and maximize revenue (Dogruel, 2021). It is important to note that research on folk theories does not evaluate whether users’ algorithmic knowledge is correct; rather, it outlines their perceptions of how algorithms work and the impact these technologies have.

Affective dimension – attitudes and evaluations

Affect is the ability to affect and be affected and is associated with embodied emotional experiences such as ‘mood, passion, emotion, intensity, and feeling’ (Anderson, 2006: 734). Thus, researchers who focus on the affective dimension of users’ experiences with algorithms examine people’s embodied feelings and sensations in response to these technologies (Bucher, 2018; Cotter, 2022). Some of this work draws on the concept of algorithmic imaginaries, defined as people’s thinking ‘about what algorithms are, what they should be, how they function and what these imaginations, in turn, make possible’ (Bucher, 2017: 40). While this concept is somewhat similar to folk theories, in conceptualizing algorithmic imaginaries, Bucher (2018: 94) emphasizes the importance of understanding people’s ‘affective encounters’ with algorithms.

A common focus in research on affect and algorithms is the valence of people’s attitudes toward digital media algorithms (Hargittai et al., 2020). Scholars find that users display mixed attitudes, including positive attitudes, as algorithms are viewed as time-saving guides that recommend relevant content; neutral attitudes, where algorithms are viewed as objective and mechanical; and negative attitudes, such as being irritated at algorithms or associating these technologies with surveillance, consumption, and discrimination (Lomborg and Kapsch, 2019; Swart, 2021; Ytre-Arne and Moe, 2020). Research also focuses on affective reactions to algorithms, wherein users associate them with mystical powers (Natale, 2019), particularly when they recommend deeply personal or revelatory content (Cotter et al., 2022).

Behavioral dimension – autonomy and practices

Researchers also focus on what people do with algorithms (Oeldorf-Hirsch and Neubaum, 2023b; Swart, 2021). This is important because users enact algorithms and ‘forge and sustain specific realities through sets of practices [emphasis added]’ (Siles, 2023: 6). Furthermore, users’ knowledge of algorithms is connected to their practices with these technologies (Cotter, 2022).

Much of the research on user practices with algorithms is through the lens of what De Certeau (1984) labels as tactics – everyday responses to situations within existing power structures. For example, studies outline how everyday users employ tactics to exert some level of autonomy over powerful algorithms, such as avoiding engaging with certain content to minimize its visibility (Van der Nagel, 2018) or actively engaging with certain content to ensure they are served similar content (Siles et al., 2020). Researchers also highlight how social media influencers engage in tactics to maximize their visibility (Cotter, 2019). Overall, despite some examples of resistance (Karizat et al., 2021), research finds that users employ tactics that allow them to work with rather than against algorithms (Dogruel et al., 2022; Lomborg and Kapsch, 2019).

Given that this research is grounded in broader questions about user autonomy and algorithmic power, some researchers also examine how users make sense of these issues. Users express mixed views about the extent to which they feel they have autonomy in relation to algorithms (Dogruel et al., 2022; Kant, 2020). Furthermore, scholars challenge the idea that algorithms unidirectionally exert power on users, arguing that the power relationship between users and algorithms is recursive (Bonini and Treré, 2024; Bucher, 2018; Siles, 2023).

A cultural understanding of algorithms

While the research outlined above details how users make sense of and experience algorithms on digital media platforms, ‘there has been a tendency to treat knowledge of (in the form of imaginaries, folk theories, beliefs, literacies, etc.), affect, and practices with algorithms as universal categories that stand outside of culture and history’ (Siles, 2023: 180). Siles argues that researchers should address this issue by adopting a culturally- and historically-situated approach to studying users and algorithms. He exemplifies this in his research in Costa Rica. For instance, he details how users expect algorithms to recommend content that aligns with local cultural values that emphasize maintaining the status quo. Simultaneously, they view algorithms as pathways to participate in global consumer culture, echoing a historical trend of interest in ideas and values from the United States. Drawing on Siles’s approach, I similarly situate my examination of urban Indian social media users’ knowledge of, attitudes toward, and practices with algorithms within the broader context of how technology and social media have been historically and culturally viewed in India.

Technology and social media in India

India’s relationship to technology has evolved post-independence, with initial bouts of skepticism making way for an embrace of information and communication technologies (ICTs) in the 1990s (Sukumar, 2019). The urban middle-class that emerged in this period associates ICTs with aspiration, social status, modernity, and globalization (Chopra, 2008; Fernandes, 2006). This view is informed by how political leaders, journalists, and international development agencies have consistently framed ICTs as positive catalysts for progress and development (Pal, 2008).

Given this study’s focus on social media algorithms, it is also important to specifically look at how social media is perceived and used in India. Despite increased access to mobile phones everywhere, meaningful access to social media remains higher among educated and financially comfortable urban populations (Deshbandhu and Sahni, 2022). Meanwhile, research on social media in India has primarily focused on the highly popular messaging application WhatsApp, including how it is an indispensable tool for social connection and conducting business (Maddox and Kanthawala, 2022), and its use for spreading false information (Nizaruddin, 2021). However, this research does not capture how Indians relate to social media algorithms, as content on WhatsApp is typically not algorithmically curated (Malhotra, 2025).

A limited body of scholarship does focus on how Indians engage on algorithmic social media platforms. Studies highlight how young Indians use TikTok (before it was banned in India) for playful engagement (Sarwatay et al., 2022) and how people use Facebook to extend their immediate network and find romantic connections (Arora, 2019). Caste and class hierarchies also shape how algorithmic platforms are perceived, with TikTok associated with lower-caste and lower-class content creators from across the country, and Instagram with urban, English-speaking upper-caste and upper-class creators (Sarker, 2023; Verma, 2021). Furthermore, research outlines how popular media frames the use of these platforms through a moral panic lens, associating it with distraction, danger, and immorality, particularly among young Indians (Sarwatay and Raman, 2021). This contrasts with the long-standing aspirational view of ICTs outlined above. However, as researchers have paid less attention to how Indians relate to algorithms, it is unclear how these seemingly contrasting views might inform their knowledge of, attitudes toward, and practices with social media algorithms. I address this issue through conducting interviews with a sample of urban Indian social media users, most of whom are from upper-middle-class backgrounds, to examine the following research questions:

RQ1: How do urban upper-middle-class Indian social media users make sense of their awareness and knowledge of algorithms, including folk theories about how these technologies operate?

RQ2: How do urban upper-middle-class Indian social media users make sense of their affective attitudes toward algorithms and the impact they have?

RQ3: How do urban upper-middle-class Indian social media users make sense of their autonomy and practices in relation to algorithms?

Method

Recruitment and sampling

As part of a larger project on social media use among urban Indians, I conducted in-depth interviews with 30 Indian social media users between May and August of 2024. After the university IRB deemed the study exempt, I engaged in purposive and snowball sampling. This involved posting a flyer on social media (see Appendix A in Supplemental Material), reaching out to personal connections in Delhi, and asking interview participants to refer other social media users in their networks. This resulted in a sample of participants mostly located in Delhi (N = 24), though a few resided in other urban centers (Mumbai, Bengaluru, Nashik, and Bhopal, N = 6). While the sample is diverse in age, gender, and profession (see Appendix B in Supplemental Material for demographic details and participant pseudonyms), it does primarily include urban upper-middle-class Indians. Participants were given a 500 rupees ($7) e-gift card as compensation.

Interview process

Participants completed an online informed consent form and a brief online questionnaire on demographics and social media use. Then they participated in an interview with me that lasted about 1 hour on average, conducted in a mix of English and Hindi (I am fluent in both languages). While most interviews occurred via Zoom as this was participants’ preferred modality, two Delhi-based interviews were conducted in-person. Participants were asked about their general use of social media, their beliefs regarding how social media recommends content, and specific questions about their knowledge of, attitudes toward, and practices with social media algorithms (see Appendix C in Supplemental Material for full interview protocol). Following Hargittai et al. (2020), the word algorithm was only included in the last set of questions to avoid prompting, giving participants the opportunity to demonstrate their basic algorithmic awareness by bringing up the word themselves. They were also encouraged to discuss any social media platform they regularly use.

Analysis

Interviews were audio-recorded and automated transcription software was used to generate initial transcripts. A research assistant fluent in English and Hindi checked these transcripts for accuracy and translated the parts in Hindi into English. The final transcripts included both the Hindi words and their English translations, allowing me to retain Hindi terms when translation might not fully capture culturally specific meanings (e.g. kundli and karma). Transcripts were then analyzed through codebook thematic analysis, wherein a deductive theory-driven approach is combined with reflexive thematic analysis (Braun and Clarke, 2022). To initially organize my data, I developed a codebook comprising four broad categories informed by user-centered research on algorithms, particularly Swart’s (2021) algorithmic literacy framework: Awareness and Understanding (RQ1), Folk Theories (RQ1), Attitudes and Effects (RQ2), and Agency and Control (RQ3). Data were initially coded into these categories, reflecting a deductive approach. Next, I analyzed data within each category through a more reflexive and iterative approach. This involved generating initial codes, combining codes into themes, identifying overlaps across themes and categories, and critically reflecting on the role of context, culture, and my positionality. I also employed data verification techniques, including maintaining an audit trail and peer debriefing with an independent qualitatively trained researcher (Lincoln and Guba, 1985). Appendix D in the Supplemental Material includes the codebook and an exemplar of the analytic process in the form of a step-by-step account of my analysis of participants’ algorithmic folk theories for RQ1.

Findings

Before addressing each research question, it is important to note that Instagram was the platform most participants mentioned when discussing their use of social media. This may be because the sample predominantly consisted of digitally confident upper-middle-class urban Indians, and Instagram is viewed as a suitable platform to use among this population (Sarker, 2023; Verma, 2021). However, it should be acknowledged that participants also discussed social media algorithms more generally, especially when asked broader questions about the impact of algorithms on society. Furthermore, some participants compared Instagram to other platforms, such as Facebook, YouTube, and X (formerly known as Twitter). These comparisons are discussed wherever relevant.

RQ1: Awareness and knowledge

Basic awareness

Participants demonstrated a basic awareness that algorithms exist and are used to personalize the content they view on social media. Without prompting, most used the term algorithm while describing how social media platforms curate content. Some, like Kanav, also explicitly reflected on their awareness: ‘Of course, I’m aware of the algorithm and what it’s doing to my timeline. And I know how it works on Insta[gram]’.

Even though participants of diverse ages and professions demonstrated basic algorithmic awareness, many believed that older or less educated people lack such awareness. For example, Nandika said: ‘The older generation, I don’t think is aware. The younger generation is a lot [more] aware’. Meanwhile, Kori argued that education was more important than age: ‘The well-educated older generation will know that there is AI, it listens to us. But the ones who are not well-educated, I don’t think they will know’. Some participants also distinguished themselves from a generalized mass audience, noting that the latter lacks basic algorithmic awareness. For example, Karan said that ‘for them [the masses], the concept of an algorithm doesn’t exist because it’s just beyond their comprehension’, while Ruchi stated that ‘masses don’t know [about algorithms]’. In comparing their own algorithmic awareness to a naïve generalized mass audience, these participants invoke what Hall et al. (2023) refer to as ontological narratives of social distinction. They frame themselves as savvy social media users compared to an unaware mass audience that is implicitly framed as inferior in social status. This finding is also likely an artifact of the sample mainly consisting of English-speaking urban upper-middle-class Indians, who may make this distinction to reinforce their own privileged social position and status.

Folk theories

Participants demonstrated their knowledge of algorithms through articulating folk theories about how these technologies work (DeVito et al., 2017). They outlined multiple, non-mutually exclusive theories.

Personal engagement

Echoing existing research (Eslami et al., 2016), most participants theorized that a social media platform’s algorithm recommends them content based on their personal engagement and activity on the platform. As Vinay put it, ‘it keeps an account of your uses, what you scroll, what you like, dislike, and sets up its algorithm for posting such similar content’. Similarly, Nandika said,

It will be a combination of what I’ve posted with the hashtags, the kind of stories I’m opening when I’m scrolling through, or the kind of things that I’m reacting to. I’m assuming it’s Artificial Intelligence gauging from that what it is that they want to show.

Overall, participants believed that the more they engage with certain content, the more the algorithm recommends similar content.

Friend engagement

Some participants believed that algorithmic content recommendation is not just shaped by their personal engagement, but also by the engagement of their connections on social media. Ruchi described this while expressing how it makes her concerned about her own social media use: ‘When you are searching for something, then there is a light fear in my mind. The way other people’s search comes in my feed, my search will also go in other people’s feed’. Participants also noted that social connections impact their algorithmic recommendations by sending them content they would otherwise not engage with. As Ishaani put it, ‘if my friend is sending me a video which is funny, it will completely change the algorithm. Because I’ve seen that video, the entire search becomes haywire’.

Off-platform engagement

According to participants, their algorithmic recommendations on one platform are also shaped by their engagement on other platforms. Many, like Rani, discussed how search activity on Google can impact algorithmic recommendations on Instagram: ‘The minute you start searching for something, whether you search it through your search engine, it invariably finds its way back to Instagram’.

Some also expressed concerns about their private messages on WhatsApp impacting algorithmic recommendations on Instagram, given Meta owns both platforms. Maira said that ‘sometimes it’s very creepy, because over WhatsApp, I’m sharing anything and everything. There’s certain things that I wouldn’t want everybody to read. And then after [I] send a message, I get similar kinds of messages on Instagram’. These beliefs align with the algorithmic thinking theory – the idea that algorithms on different platforms are connected and communicate with each other (Dogruel, 2021).

Some participants were also concerned that conversations they have offline shape their online algorithmic recommendations. For instance, Sonam said,

I have a very strong feeling it’s listening to whatever is going on in my real life and showing me content around it. It’s no longer even a surprise or shock. Because you just have a random conversation about a topic and Instagram starts showing Reels on it.

Other researchers have similarly found that social media users believe that platforms listen to their offline conversations (Segijn et al., 2024).

RQ2: Affective attitudes and evaluations

Participants expressed both positive and negative affective attitudes toward algorithms, drawing on culturally-situated discourses about social media’s effects.

Positive attitudes

Algorithms as smart mind-readers

Some participants described algorithms in anthropomorphic terms, drawing on the narrative that algorithms are smart. Leela said, ‘I think Instagram is a very brainy person, whatever it is. It understands everyone’s taste’. This aligns with research on how users describe digital media algorithms as person-like beings (Siles et al., 2020). Some also emphasized the notion that algorithms can read users’ minds and recommend content they want to see. As Manu said, ‘sometimes we will be thinking and suddenly you open it, and it comes. Then you’re saying, “oh, what is that magic? It’s reading my mind.”’ Geeta compared this to astrology and said Instagram can predict content she wants to see because ‘they have my kundli (Vedic birth chart)’. While most participants framed this perceived mind-reading ability in positive terms, a few did link it to the folk theory about algorithms having access to private conversations (see RQ1 findings), demonstrating the overlap between algorithmic folk theories and affective attitudes toward algorithms. In fact, the RQ2 findings could also be viewed through the lens of abstract algorithmic folk theories, as these findings present participants’ broader views about algorithms’ positive and negative societal impact (DeVito et al., 2017).

Algorithms as global knowledge providers

Participants also framed algorithms as expanding users’ sphere of knowledge by recommending diverse content from different parts of the world. For example, Cyan stated that ‘algorithms are a nice way to get to know about a wider variety of views in a wider geographical area’. Leela described how the Instagram algorithm connects her to global fashion trends she can incorporate in her clothing boutique: ‘I have so much help in my boutique. Latest trends coming from Milan, coming from Paris’. Comparing algorithmic feeds to reverse chronological feeds, she said, ‘we won’t use Insta[gram] for 8-10 people. We want to see the whole world’. These quotes echo research on how urban Indians associate technology with globalization (Chopra, 2008; Fernandes, 2006). They also demonstrate how Global South populations often view algorithms as facilitating their participation in global conversations (Siles, 2023). Meanwhile, participants like Sonam stated that the Instagram algorithm can expand not just one’s global but also one’s local horizons: ‘I want to know more of India, [about] people I have never met before. [Instagram’s algorithm is] pushing that and I like that’.

Algorithms as productivity enhancers

Participants also drew on the narrative that algorithms enhance individual productivity. For example, Karan stated that they do so by filtering large volumes of content, saving users time:

If there are tens of thousands or millions of pieces of content, then there’s no way that I’ll be able to sift through them myself to see what I want to see. So, someone doing it for me is a great piece of help.

Others linked algorithms to productivity by highlighting how these technologies connect them to professional resources. Samara, an aspiring actor, said, ‘the [Instagram] algorithm has also pointed me towards wonderful resources. It has pointed me towards casting agents, casting agencies’. This emphasis on productivity is likely informed by the popular narrative that ICTs facilitate professional and economic development, particularly in the Global South (Arora, 2019).

Negative attitudes

Some participants also drew on narratives where algorithms are associated with negative consequences. This was particularly the case for younger participants.

Algorithms as echo chamber creators

In contrast to the idea that algorithms connect users to diverse knowledge sources, some participants mobilized the narrative that algorithms produce information echo chambers by privileging content that aligns with an individual’s worldview. As Kanav said, ‘algorithm is what creates echo chambers, right? You're just seeing what you feel like and what is aligned with your opinion’. Similarly, Samyra said that algorithms result in her not being ‘exposed to other points of view on certain issues’. These quotes reflect the persistent discourse that holds social media algorithms responsible for echo chambers, despite empirical evidence suggesting otherwise (Barberá, 2020).

Algorithms as harmfully addictive

Participants also invoked the discourse that algorithms are responsible for negatively impacting users’ well-being. Samara attributed this to algorithms keeping users on social media for long hours, saying that ‘algorithms are built to make you keep wanting more’, and ‘you will never run out of content’ as algorithms ‘keep pumping it to you’. Some, like Tejas, claimed that this is why ‘algorithms can be very addictive’. A few participants also framed algorithms as negatively impacting users’ mental health by amplifying content that makes people unfavorably compare their lives to others. Ishaani exemplified this narrative while discussing Instagram: ‘It will show you all the places that has parties, all the people who are partying’. She said that this results in people having the ‘fear of missing out’. These accounts reflect the dominance of discourse that causally links social media use to mental health issues, even as research suggests a more complex relationship (Valkenburg et al., 2022).

Algorithms as consumption drivers

Some participants framed algorithms as technologies designed primarily to serve the commercial interests of social media platforms and advertisers, rather than to benefit users. As Matblore put it, ‘it’s all designed to drive business to the owners’. Similarly, Rani said that ‘everything is to induce that 3AM or 5AM shopping’. Overall, these participants drew on the narrative that platforms like Instagram exist to surveil users and deploy algorithms to maximize profits.

Mixed evaluations and comparisons

It is also important to acknowledge that individuals often expressed both positive and negative attitudes toward algorithms. Tejas exemplified this duality:

If [the] algorithm is engineering the way you see similar posts, similar accounts, you can genuinely learn new perspectives, new things, from different people. But it can also cause brain rot if you just rely on [the] algorithm and do not engage in your own research.

Furthermore, while the findings outlined above primarily detail participants’ attitudes toward the Instagram algorithm, some also compared Instagram’s algorithm to other platforms. Many believed that Instagram’s algorithm is better than Facebook’s, while some evaluated YouTube’s algorithm most favorably. Meanwhile, some believed that X’s algorithm has declined in quality after Elon Musk’s takeover. These comparisons reflect Siles’s (2023) argument that people often make sense of algorithms by comparing them across different platforms.

These comparisons were also informed by participants’ broader beliefs about different platforms, particularly the kinds of content and audiences each platform is typically associated with. For instance, Instagram’s algorithm was viewed positively because participants believed that it recommends content they associate the platform with, such as posts about niche hobbies, travel, and fashion. Some female participants who run small businesses like clothing boutiques also emphasized how Instagram’s algorithm helps them market their products to a high-status audience, especially compared to other platforms. While discussing clientele for her clothing business, Ruchi said that it is important to consider which ‘class you are targeting’ and Instagram ‘has better people’, while Facebook is for ‘the masses’. These beliefs about Instagram reflect its perception as an exclusive haven for high-status urban Indians (Sarker, 2023; Verma, 2021). Meanwhile, especially male participants evaluated YouTube’s algorithm positively because they felt that it recommends content they associate the platform with – long-form educational videos on topics like geopolitics and technology. As Mickey noted, YouTube is ‘very educative’. Conversely, X’s algorithm was viewed negatively because participants believed that it amplifies the negative content the platform has become known for, namely polarizing political posts that are ‘really toxic’, as Cyan put it. Overall, these accounts illustrate how broader beliefs about platforms inform comparative evaluations of their algorithms.

RQ3: Autonomy and practices

How participants felt about social media algorithms also informed their mixed views on the balance between user autonomy and algorithmic power, as well as the practices they described adopting to acquire a degree of control over algorithms.

Algorithmic power

Some participants believed that users have little autonomy in relation to social media algorithms. This was particularly true for younger participants. Mohit exemplified this view: ‘As a user, I don’t think I have any kind of power over algorithm[s]’. Noor made a similar argument, stating that ‘we definitely do not have as much control. You feel like you have control because it’s assigned for you, but not really’. This view was common among participants who expressed a more negative attitude toward algorithms (see RQ2 findings). Some also associated this lack of control with specific platforms. In particular, Elon Musk’s takeover of X contributed to some participants feeling like they ‘have less control now’, as Pranay put it.

User autonomy

In contrast, a few participants framed algorithms as neutral, underscoring how users possess complete autonomy to shape the content they see on social media. For example, Leela said that platforms ‘will not show it[content] by making a special choice’, while Ruchi said ‘that is our choice, what you want to see or what we don’t want to see. It is never ever forced on us’. Karan believed that the cultural salience of karma – the belief that personal actions impact one’s fate – explains this emphasis on user autonomy. He noted that ‘we have the concept of karma. It’s your action that leads to whatever happens to you’ and ‘you’re made to realize you are responsible for your own actions’. Thus, an emphasis on karma in Indian culture may inform the view that instead of blaming algorithms, users should recognize that their personal actions influence the content they see on social media.

Algorithmic and user control

Instead of exclusively emphasizing either algorithmic power or user autonomy, most participants expressed a more balanced view. They recognized that social media platforms exert power through algorithmic curation while simultaneously highlighting how users can exert some control over these algorithms. Samyra encapsulated this view: ‘The platform is pretty powerful in that sense. But we’re equally as powerful, if not more. We hold the power to tailor posts according to what we would like to see’. Many also argued that exercising such control requires algorithmic awareness, illustrating the overlap between the behavioral and cognitive dimensions of Swart’s (2021) framework. For example, Kanav positioned himself as being more algorithmically aware than others, noting that ‘I don’t think lots of people are aware [of] how their timelines are being designed for them’ (see RQ1 findings for more examples of participants underscoring their own algorithmic awareness). He added that those who possess such awareness have greater control over algorithms as they know ‘steps that users can take to design their timeline’. Examples of such steps are detailed below.

User practices

Participants mentioned that one way they impose some control over social media algorithms is through indicating to these algorithms the kind of content they do not want to see. This can be done through simply ignoring certain content. As Seema put it, ‘we have control [over] what we skip and what we watch for more time’. It can also be done through platform features, particularly the ‘not interested’ button on Instagram. However, not everyone believed that such features work. For example, Tejas stated that ‘even after you have chosen that you’re not interested, don’t show this content, show this account less often. You still get to see that’.

Participants also discussed the practice of indicating to the algorithm the kind of content they do want to see. This involves consciously viewing and liking certain content. As Kanav stated, ‘aggressively liking or watching stuff also helps because it sends a signal that you want to see this only’.

While these practices can help users feel a sense of control, some participants also recognized that engaging in them requires constant awareness and effort. Samyra expressed this succinctly: ‘You need to be an active user to construct your feed and take control of the algorithm. Only then can you equalize the power balance’. Overall, these practices involve working with rather than against algorithms (Dogruel et al., 2022; Lomborg and Kapsch, 2019).

Discussion

Interviews with mainly urban upper-middle-class Indian social media users reveal that they are aware of social media algorithms and hold multiple folk theories about how they operate, particularly on Instagram. This includes theories about how these technologies track their online and offline activity to recommend personalized content. Participants also express mixed attitudes toward algorithms, including a positive view of algorithms as smart technologies that connect them to global knowledge and useful resources, and a negative view of algorithms as technologies that create echo chambers, harm mental health, and encourage consumerism. In terms of their autonomy and behaviors in relation to algorithms, some primarily emphasize either algorithmic or user power, but most believe that both users and algorithms exert influence and describe practices for working with rather than against algorithms.

These findings reveal how users in a specific Global South context make sense of algorithms. To expand how we understand the relationship between users, culture, and algorithms, it is important to critically reflect on how these findings are informed by the participants’ social positions and cultural values. In terms of the former, a key aspect to consider is that the sample primarily consisted of upper-middle-class Delhi-based participants. Given that Instagram is generally viewed as a haven for upper-class and upper-caste Indians (Sarker, 2023; Verma, 2021), participants’ choice to primarily discuss Instagram can be viewed as a way for them to signal their privileged social status. Participants also reinforce their status by framing themselves as algorithmically aware and savvy social media users compared to algorithmically unaware lower status ‘masses’, with the term ‘masses’ used by different participants to refer to rural populations, people with low literacy, and low-earning workers such as street hawkers. In doing so, participants operationalize algorithmic awareness and knowledge as forms of cultural capital – cultural resources that confer social status and power (Bourdieu, 1986) – thereby distinguishing themselves from rural communities and people with lower education, class, and caste status. By highlighting how algorithmic awareness and knowledge function as cultural capital, this study expands existing understandings of these concepts, which have primarily been framed as individual skills and literacy in research situated in the Global North (Oeldorf-Hirsch and Neubaum, 2023b). Here, it is also vital to acknowledge that in India, cultural capital is particularly intertwined with caste, an influential dimension of the country’s social hierarchy. While I did not specifically ask participants about caste, future research can more directly examine its role in algorithmic sensemaking, including centering the voices of marginalized lower-caste Indians. Furthermore, it should be acknowledged that my own social position as an urban upper-middle-class Indian may have encouraged participants to distinguish between our shared in-group and a homogeneous out-group of low-status ‘masses’. Nonetheless, a key contribution of this study is demonstrating that algorithmic awareness and knowledge are not merely skills individuals do or do not possess; rather, they can function as resources for reinforcing caste, class, and status hierarchies within the Indian context.

Participants’ social status similarly informs the discourses they invoke while discussing their affective attitudes toward algorithms. The positive view of algorithms as facilitating global connections and enhancing productivity reflects the prevalence of cultural discourses that equate new technologies with progress, globalization, and aspiration, especially among middle- and upper-middle-class Indians (Chopra, 2008; Fernandes, 2006; Pal, 2008). The mobilization of these discourses may be viewed as another way for participants to signal their social status. Concurrently, younger participants in particular draw on narratives that associate algorithms with polarization, harmful mental health outcomes, and surveillance capitalism, and express a desire to scale back their social media use. This may reflect the influence of moral panic-inflected journalistic coverage of social media’s impact on young Indians (Sarwatay and Raman, 2021). It may also be a result of the sample primarily consisting of financially comfortable people with unfettered access to digital technologies. Only such populations have the privilege to even consider disconnecting from social media to protect themselves from these perceived negative consequences (Helsper, 2021). In contrast, marginalized groups have less choice and often require access to digital technologies to get by in their day-to-day lives. Furthermore, it is important to acknowledge that since the interviews took place during or shortly after the final phase of the 2024 Indian general election, participants’ views on algorithmic surveillance and platform power may have been influenced by these technologies recommending election-related content. Although I asked participants about election-related social media content as part of my broader research project, this topic falls outside the scope of the present study.

These affective attitudes toward algorithms also vary across platforms, informed by participants’ broader views about each platform. These views reflect what Gershon (2010) calls media ideologies – the meanings users ascribe to different media channels. When participants assign positive meaning to a platform and its algorithm recommends content that aligns with this meaning, they evaluate the algorithm positively. For example, Instagram’s algorithm is viewed positively, provided it enables female participants, in particular, to consume travel and fashion content and market products to upper-class clientele. Meanwhile, especially among male participants, YouTube’s algorithm is evaluated positively for recommending informative content. These evaluations are informed by status considerations as platforms are viewed positively when they facilitate accumulation of economic capital through selling products and cultural capital through consuming globally oriented, informative content. Furthermore, while women’s focus on fashion and men’s emphasis on productive knowledge may be viewed by some as reflecting gendered beliefs, participants’ discussion of these topics likely stems from an attempt to match what they perceive to be my interests as an urban upper-middle-class Indian. Moreover, female participants using Instagram for entrepreneurial business could also be read as challenging traditional gender norms. Overall, these findings expand our understanding of how users feel about algorithms, revealing how these feelings are impacted by their social positions and media ideologies.

Contextual- and cultural-specificity likewise informs the algorithmic folk theories that participants subscribe to. First, the belief that Instagram’s algorithm recommends content based on WhatsApp messages reflects how WhatsApp continues to be viewed as central to all forms of online communication in India (Maddox and Kanthawala, 2022). Second, the friend engagement theory – the idea that algorithms consider the activity of one’s social network while recommending content – represents a relational view of algorithmic personalization. This view may be informed by the salience of interdependence among interpersonal connections in Indian culture, aligning with collectivistic cultural logics (Tuli and Chaudhary, 2010; Verma et al., 2024). The finding that algorithmic personalization is viewed by some users as a relational process is a significant contribution to the literature, which typically treats personalization as an individually-centered process (i.e. a user’s own activity shapes their feed) (Kant, 2020). This study therefore illustrates the value of examining algorithmic personalization through a culturally- and contextually-specific lens. Furthermore, participants’ relational and interdependent algorithmic folk theories demonstrate how folk theories are imbued with cultural values (Siles, 2023).

Cultural values similarly inform how some participants discuss user autonomy and algorithmic power. While most participants view the power relationship between users and algorithms as recursive, aligning with existing research (Dogruel et al., 2022; Lomborg and Kapsch, 2019), a few participants express alternative views that can be analyzed through culturally-specific concepts. For example, the belief among a small number of participants that users should take responsibility for shaping their social media feeds can be understood through the lens of karma – the idea that one’s fate is determined by one’s own actions and intentions (White et al., 2018). At the same time, karma’s association with fate and destiny may also inform the belief that algorithmic recommendations are predestined, as suggested by a participant’s comparison of algorithms to her kundli (predictive astrological birth chart based on past karma). These coexisting interpretations of karma help explain how algorithms can be seen both as neutral systems reflecting individual responsibility and as mystical predictors of fate. This ultimately demonstrates how cultural epistemologies can shape understandings of algorithmic prediction. Similarly, the culturally-specific concept of jugaad, which means quick workaround or hack, can be used to understand participants’ practices with algorithms, such as liking or intentionally ignoring specific content to influence algorithmic recommendations. While these workarounds help participants acquire some control over algorithms, they simultaneously involve providing platforms with more data and power, reflecting how jugaad can ultimately reinforce neoliberal values like individual solutions and consumption (Rai, 2019).

Overall, this study provides contextually- and culturally-specific insights into the cognitive, affective, and behavioral dimensions of how urban upper-middle-class Indians relate to social media algorithms. While using these dimensions as an organizing framework risks implying a strict separation between them, this study’s findings show that the dimensions are overlapping and interrelated, as Swart (2021) also acknowledges. This study also prompts scholars to recognize that these dimensions do not operate in a vacuum and are shaped by contextual aspects such as people’s cultural values and social positions. Furthermore, this emphasis on culture and context demonstrates what Siles (2023) refers to as the impact of algorithms in and as culture. Siles challenges the distinction between algorithms in culture, the idea that algorithms are an external force that acts upon culture, and algorithms as culture, the notion that algorithms are enacted through user practices within specific cultural contexts (Seaver, 2017). He contends that ‘when the use of algorithmic platforms is examined empirically, both processes are simultaneous’ (Siles, 2023: 181). As outlined above, urban upper-middle-class Indians describe how algorithms impact Indian culture in positive and negative ways while themselves enacting algorithms through practices informed by their social and cultural background.

Limitations and conclusion

These insights should be viewed within the context of the study’s limitations. First, while the sample was diverse in gender, age, and profession, most participants came from upper-middle-class backgrounds and had unrestricted access to technology. Thus, these findings cannot be generalized to the broader Indian population. Second, I did not collect structured data on participants’ caste, education level, or household income, limiting systematic examination of variations across the sample. Third, conducting interviews meant contending with social desirability concerns, including the possibility that participants overstated their knowledge of algorithms to impress the interviewer (Hargittai et al., 2020). Furthermore, my own positionality as an urban upper-middle-class Indian likely shaped participants’ responses. For example, our overlapping backgrounds may have encouraged participants to assume a shared interest in Instagram and content on the platform that caters to high-status audiences. Finally, since most interviews were conducted online, it may have hindered establishment of rapport with participants.

Despite these limitations, this study makes important contributions and opens avenues for future research. First, future research can empirically test some participants’ claims that older and less educated Indians have lower levels of algorithmic awareness. Second, research can examine how attitudes toward algorithms are informed by the hype surrounding generative AI. Participants used the terms AI and algorithms interchangeably, indicating that users may link the two despite their technical distinction. Finally, future research in different parts of the world can build on my approach and present contextually- and culturally-situated insights into how diverse populations relate to algorithms.

Supplemental Material

sj-docx-1-nms-10.1177_14614448261453943 – Supplemental material for ‘They have my kundli’: Understanding urban upper-middle-class Indians’ beliefs about social media algorithms

Supplemental material, sj-docx-1-nms-10.1177_14614448261453943 for ‘They have my kundli’: Understanding urban upper-middle-class Indians’ beliefs about social media algorithms by Pranav Malhotra in New Media & Society

Footnotes

ORCID iD

Pranav Malhotra

Ethical considerations

The University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board deemed this study (study ID: HUM00253592) as ‘Exempt and Not Regulated’.

Consent to participate

Participants gave informed consent through an online Google form wherein they were informed about the study.

Consent for publication

Not applicable.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by internal funding from the University of Michigan Department of Communication and Media.

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data that support the findings of this study are available on request from the author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Supplemental material

Supplemental material for this article is available online.

Author biography

Pranav Malhotra (Ph.D., University of Washington, USA) is an Assistant Professor at the University of Michigan Department of Communication and Media. His research focuses on how the affordances of social and mobile media intersect with cultural and relational norms to influence how people engage with information and each other in mediated spaces.

References

Alper

Rauchberg

Simpson

, et al. (2023) TikTok as algorithmically mediated biographical illumination: Autism, self-discovery, and platformed diagnosis on #autisktok. New Media & Society 27(3): 1378–1396. https://doi.org/10.1177/14614448231193091

Anderson

(2006) Becoming and being hopeful: Towards a theory of affect. Environment and Planning D: Society and Space 24(5): 733–752. https://doi.org/10.1068/d393t

Arora

(2019) The Next Billion Users: Digital Life beyond the West. Harvard University Press.

Barberá

(2020) Social media, echo chambers, and political polarization. In: Couldry

Woolley

Heyes

(eds) Social Media and Democracy: The State of the Field, Prospects for Reform. Oxford University Press, pp.34–56.

Bonini

Treré

(2024) Algorithms of Resistance: The Everyday Fight against Platform Power. MIT Press.

Bourdieu

(1986) The forms of capital. In: Richardson

(ed.) Handbook of Theory and Research for the Sociology of Education. Greenword Press, pp.241–259.

Braun

Clarke

(2022) Thematic Analysis: A Practical Guide. Sage.

Bucher

(2017) The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society 20(1): 30–44. https://doi.org/10.1080/1369118X.2016.1154086

Bucher

(2018) If. . . Then: Algorithmic Power and Politics. Oxford University Press.

10.

Chopra

(2008) Technology and Nationalism in India: Cultural Negotiations from Colonialism to Cyberspace. Cambria Press.

11.

Cole

(2024) Exploring users’ algorithmic knowledge and reflexivity in a music streaming context: A critical realist approach. Convergence: The International Journal of Research into New Media Technologies 30(6): 2075–2089. https://doi.org/10.1177/13548565241277379

12.

Cotter

(2019) Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media & Society 21(4): 895–913. https://doi.org/10.1177/1461444818815684

13.

Cotter

(2022) Practical knowledge of algorithms: The case of BreadTube. New Media & Society 26(4): 2131–2150. https://doi.org/10.1177/14614448221081802

14.

Cotter

Reisdorf

(2020) Algorithmic knowledge gaps: A new horizon of (digital) inequality. International Journal of Communication 14: 745–765.

15.

Cotter

DeCook

Kanthawala

, et al. (2022) In FYP we trust: The divine force of algorithmic conspirituality. International Journal of Communication 16: 2911–2934.

16.

De Certeau

(1984) The Practice of Everyday Life. University of California Press.

17.

Deshbandhu

Sahni

(2022) Repurposing a WhatsApp group: How a fantasy cricket group transformed into a site of care and support during India’s second wave of Covid-19. Mobile Media & Communication 11(2): 271–293. https://doi.org/10.1177/20501579221137998

18.

DeVito

Gergle

Birnholtz

(2017) ‘Algorithms ruin everything’: # RIPTwitter, folk theories, and resistance to algorithmic change in social media. In: Proceedings of the 2017 CHI conference on human factors in computing systems, Denver, CO, 6–11 May, pp.3163–3174. Association for Computing Machinery.

19.

Dogruel

(2021) Folk theories of algorithmic operations during Internet use: A mixed methods study. The Information Society 37(5): 287–298. https://doi.org/10.1080/01972243.2021.1949768

20.

Dogruel

Facciorusso

Stark

(2022) ‘I’m still the master of the machine’. Internet users’ awareness of algorithmic decision-making and their perception of its effect on their autonomy. Information, Communication & Society 25(9): 1311–1332. https://doi.org/10.1080/1369118X.2020.1863999

21.

Eslami

Karahalios

Sandvig

, et al. (2016) First I ‘like’ it, then I hide it: Folk theories of social feeds. In: Proceedings of the 2016 CHI conference on human factors in computing systems, San Jose, CA, 7–12 May, pp.2371–2382. Association for Computing Machinery.

22.

Fernandes

(2006) India’s New Middle Class: Democratic Politics in an Era of Economic Reforms. University of Minnesota Press.

23.

Fletcher

Nielsen

(2019) Generalised scepticism: How people navigate news on social media. Information, Communication & Society 22(12): 1751–1769. https://doi.org/10.1080/1369118X.2018.1450887

24.

Gershon

(2010) The Breakup 2.0: Disconnecting Over New Media. Cornell University Press.

25.

Gillespie

(2014) The relevance of algorithms. In: Gillespie

Boczkowski

Foot

(eds) Media Technologies: Essays on Communication, Materiality, and Society. MIT Press, pp.167–194.

26.

Hall

N-A

Chadwick

Vaccari

(2023) Online misinformation and everyday ontological narratives of social distinction. Media, Culture & Society 46(3): 572–590. https://doi.org/10.1177/01634437231211678

27.

Hargittai

Gruber

Djukaric

, et al. (2020) Black box measures? How to study people’s algorithm skills. Information, Communication & Society 23(5): 764–775. https://doi.org/10.1080/1369118X.2020.1713846

28.

Helsper

(2021) The Digital Disconnect. Sage.

29.

Kant

(2020) Making It Personal: Algorithmic Personalization, Identity, and Everyday Life. Oxford University Press.

30.

Karizat

Delmonaco

Eslami

, et al. (2021) Algorithmic folk theories and identity: How TikTok users co-produce knowledge of identity and engage in algorithmic resistance. Proceedings of the ACM on Human-Computer Interaction 5(CSCW2): 1–44. https://doi.org/10.1145/3476046

31.

Lincoln

Guba

(1985) Naturalistic Inquiry. Sage.

32.

Lomborg

Kapsch

(2019) Decoding algorithms. Media, Culture & Society 42(5): 745–761. https://doi.org/10.1177/0163443719855301

33.

Maddox

Kanthawala

(2022) The revolution will be forwarded: Interrogating India’s WhatsApp imaginary. Journal of Communication Inquiry 47(3): 249–267. https://doi.org/10.1177/01968599221095177

34.

Malhotra

(2025) Toward conceptualizing bounded social media places. Political Communication. Epub ahead of print 29 May. https://doi.org/10.1080/10584609.2025.2512342

35.

Milan

Treré

(2019) Big data from the south(s): Beyond data universalism. Television & New Media 20(4): 319–335. https://doi.org/10.1177/1527476419837739

36.

Narayanan

(2023) Understanding Social Media Recommendation Algorithms. Knight First Amendment Institute, Columbia University. Available at: https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms

37.

Natale

(2019) Amazon can read your mind: A media archaeology of the algorithmic imaginary. In: Natale

Pasulka

(eds) Believing in Bits: Digital Media and the Supernatural. Oxford University Press, pp.19–36.

38.

Nizaruddin

(2021) Role of public WhatsApp groups within the Hindutva ecosystem of hate and narratives of ‘CoronaJihad’. International Journal of Communication 15: 1102–1119.

39.

Oeldorf-Hirsch

Neubaum

(2023a) Attitudinal and behavioral correlates of algorithmic awareness among German and US social media users. Journal of Computer-Mediated Communication 28(5): 1–12. https://doi.org/10.1093/jcmc/zmad035

40.

Oeldorf-Hirsch

Neubaum

(2023b) What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. New Media & Society 27(2): 681–701. https://doi.org/10.1177/14614448231182662

41.

Pal

(2008) Computers and the promise of development: Aspiration, neoliberalism and ‘technolity’ in India’s ICTD enterprise. Paper presented at Confronting the Challenge of Technology for Development: Experiences from the BRICS. University of Oxford.

42.

Rai

(2019) Jugaad Time: Ecologies of Everyday Hacking in India. Duke University Press.

43.

Sarker

(2023) Of friendship, love, and community: Dalit girlhood on TikTok. In: Yadav

Kadavath

(eds) The Digital Popular in India: Mainstreaming the Marginal. Springer, pp.79–101.

44.

Sarwatay

Raman

(2021) Everyday negotiations in managing presence: Young people and social media in India. Information, Communication & Society 25(4): 536–551. https://doi.org/10.1080/1369118X.2021.1988129

45.

Sarwatay

Lee

Kaye

DBV

(2022) Exploring children’s TikTok cultures in India: Negotiating access, uses, and experiences under restrictive parental mediation. Media International Australia 186(1): 48–65. https://doi.org/10.1177/1329878X221127037

46.

Seaver

(2017) Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society 4(2): 1–12. https://doi.org/10.1177/2053951717738104

47.

Segijn

Strycharz

Turner

, et al. (2024) Conversation-related advertising and electronic eavesdropping: Mapping perceptions of phones listening for advertising in the United States, the Netherlands, and Poland. Social Media + Society 10(4): 1–18. https://doi.org/10.1177/20563051241288448

48.

Siles

(2023) Living with Algorithms: Agency and User Culture in Costa Rica. MIT Press.

49.

Siles

Segura-Castillo

Solís

, et al. (2020) Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South. Big Data & Society 7(1): 1–15. https://doi.org/10.1177/2053951720923377

50.

Sukumar

(2019) Midnight’s Machines: A Political History of Technology in India. Penguin Random House India Private Limited.

51.

Swart

(2021) Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Society 7(2): 1–11. https://doi.org/10.1177/20563051211008828

52.

Tuli

Chaudhary

(2010) Elective interdependence: Understanding individual agency and interpersonal relationships in Indian families. Culture & Psychology 16(4): 477–496. https://doi.org/10.1177/1354067X10380157

53.

Valkenburg

Beyens

Meier

, et al. (2022) Advancing our understanding of the associations between social media use and well-being. Current Opinion in Psychology 47: 1–11. https://doi.org/10.1016/j.copsyc.2022.101357

54.

Van der Nagel

(2018) ‘Networks that work too well’: Intervening in algorithmic connections. Media International Australia 168(1): 81–92. https://doi.org/10.1177/1329878X18783002

55.

Verma

Bhattacharya

Singh

(2024) Family dynamics and intergenerational relation in interdependent society: An Indian perspective. In: Chandra

Blair

(eds) Contemporary Perspectives in Family Research. Emerald, pp.97–125.

56.

Verma

(2021) Cultural cringe: How caste and class affect the idea of culture in social media. Feminist Media Studies 21(1): 159–161. https://doi.org/10.1080/14680777.2021.1864879

57.

White

CJM

Norenzayan

Schaller

(2018) The content and correlates of belief in karma across cultures. Personality and Social Psychology Bulletin 45(8): 1184–1201. https://doi.org/10.1177/0146167218808502

58.

Ytre-Arne

Moe

(2020) Folk theories of algorithms: Understanding digital irritation. Media, Culture & Society 43(5): 807–824. https://doi.org/10.1177/0163443720972314

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.22 MB

0.00 MB