Uninstalling WhatsApp in India is equivalent to losing a virtual limb. Everything between the daily good morning and pre-sleep message check involving communication is likely to be routed through the application. It is the one platform everyone with a smartphone in India is assumed to be on, and so crucial that Facebook (oops, I meant Meta) displayed a message on startup stating their new policies wouldn’t endanger their privacy – so that millions of Indians would stop jumping ship to Signal and Telegram. The issue has been forgotten since, and the new T&Cs were postponed so that their most popular app wouldn’t suffer (Bansal, 2021). I tried to use Signal as well, but WhatsApp has nearly 200 million Indian users. The people close to me, like my parents, were going to continue using WhatsApp. And without it, I was virtually crippled. But I wondered why they trusted and used the app so much.
My research tries to answer a question asked often – why is there so much misinformation on WhatsApp? Many Indians place a great deal of faith in the platform and the content shared on it, including the innumerable fake viral forwards (Bansal, 2021). By asking if certain people were more likely to believe the information passed on the platform, I wanted to understand if the factors behind the phenomena could be utilized to peddle misinformation (or disinformation) to millions of users.
I looked at Indian users from the ages of thirty-five to sixty and of the economic strata categorized as ‘middle-class’ by the National Council for Applied Economic Research (Salve, 2015). And analysed empirical evidence on how social roles, relationships, and other characteristics were created on the network keeping in context the references, resources and positions the individuals held. I learnt that WhatsApp had an incredibly high level of social propagation, which is measured by the number of people who were recommended to install and use it by their acquaintances, friends and relatives (i.e., their contacts). These ‘recommendations’ are a mode of social influence – in this case, you had to migrate and use WhatsApp to continue to communicate with them. And why wouldn’t anyone? WhatsApp allowed them to send real-time text, audio, geo-location-based and video messages to individuals or groups for free (Church & de Oliveira, 2013, p. 354).
The recommendations were encouraged by a rise in the general economic capacity of the middle-class and their exposure to the IT boom of the 2000s (Venkatraman, 2017, p. 199). Within a few years of WhatsApp’s launch, these users were sending a lot more messages than they used to when they were using Short Messaging Service (SMS), as the app was perceived to be a modern tool that allowed for social, natural, and widespread chatting (Church & de Oliveira, 2013, p. 360). It provided access to its services by creating a unique profile linked to a phone number. As the smartphone was the only possible primary interface for its usage, it was also able to feed off the psychosocial effects of notifications (Rosenfeld et al., 2018, p. 650). In other words, it made current messaging practices more frequent, conversational, group-oriented, and socially exclusive – as they depended on a platform that is ‘instant’ and only accessible to people who could afford smartphones.
Generation X’s ability to afford and understand smartphones not only led to a rise in WhatsApp usage but also allowed them to communicate and reflect their social roles to a wider audience for validation (Venkatraman, 2017, p. 207). The desire for validation is likely a result of the high degree of homophily experienced by WhatsApp users – homophily being the tendency of people to seek out or be attracted to individuals like themselves. That is, the relationships we cultivate on any network tend to be stronger when made with people who are similar to us. The strength of the relationship is likely to correspond to the level of similarity. This affected the nature and frequency of the online interactions between the users, as the finite resources used to form and maintain said the relationship would create a subconscious bias. The information processed in such a relationship was important, or at least more believable for them. The effect may even be magnified in groups of similar people (Xiang et al., 2010, p. 982).
As a primarily private platform that functions around one’s contacts, WhatsApp not only increases interactions, but it increases them with people we care about – helping validate behaviours, opinions etc. that we already perceive as right. Some papers have also suggested that the presence of a record of social interactions, which WhatsApp’s interface provides, can increase the trust with which future information is processed (Xiang et al., 2010, p. 981).
Algorithmic models developed by researchers have also applied predictive behavioural analysis to parameters of interest, such as age and gender, to show that various demographics across regions had significantly different methods and patterns of WhatsApp usage in almost all individual and group messages – without breaching the participants’ privacy or using content from the messages that were sent or received as inputs (Singla & Richardson, 2008, p. 656). Despite not having access to private information, the study found that many individual and group usage profiles differed on such a noticeable scale that they were able to use data analytics to predict the users’ gender, age, and group activity accurately (Rosenfeld et al., 2018, p. 666). Although the results of these studies were not derived from an Indian demographic, they can be applied to users across the globe due to the scale on which they were conducted and the nature of their findings, which puts aside matters of nationality – as it is also personal information and not considered by the algorithm (Rosenfeld et al. 2018, p. 666). They are useful if only to emphasize that there is a difference between demographics.
The last two paragraphs and the studies they contain are important to understand the behaviour of the demographic, and the susceptibility it has given rise to. Many suburban and metropolitan areas are undergoing massive infrastructural and socio-economic changes, and in online spaces, traditional cultures meet modern tenets. So far, we see that the relationships these adults formed on WhatsApp tended to be private and well-liked. The creation of a hybrid role to configure local and global roles (or traditional and modern, in this case) on one locus (WhatsApp) ‘retraditionalizes’ those local roles so that the application may be used with ease by exhibiting a newly modified set of behaviours (Callero, 2008, p. 1985). The new traditions act as anchors to help WhatsApp become a part of the user’s life and act as a catalyst for the role’s development.
Indeed, after multiple interviews and online visual analysis of citizens belonging to a particular ethnic group and the middle-class economic strata, a researcher had conclusive evidence that fifty-eight per cent of the people he interviewed had formed relationships with people they considered to be friends or relatives, usually from their ethnic communities. Most online interactions occurred with people they already knew in real life. Another possible factor would be the usage of one WhatsApp account for both professional and personal matters, which may blur the difference between the two spheres (Venkatraman, 2017, p. 205) and the social roles one’s co-workers and family members play (Venkatraman, 2017, p. 203). These connections are also indicative of specific sociological factors that influence how online conversations are formed, the two most visible being caste and class (Venkatraman, 2017, p. 197).
It also suggests that the reassertion of such social biases happens on social frameworks such as WhatsApp. Individuals of higher cultural or economic strata, perhaps with greater resources, share discourse with confidence and skill that makes what they are sharing seems unimpeachable. A person interacting with another user, or a group could forward a message to as many as twenty individuals or groups, who would then be able to do the same. Misinformation campaigns have been run at massive scales, and several fake news reports and stories continue to be forwarded through what has been termed ‘WhatsApp University’ – although a five-forward maximum was issued by Facebook in 2021 (Bansal, 2021). Those who cannot afford smartphones still attempt to learn about and use social networks that are available on non-mobile platforms, such as Facebook, but on such platforms, the ‘lower’ classes refrain from discourse and merely talk about the things they accept – not why others should accept it as well (Venkatraman, 2017, p. 201).
In other words, when the local interacts with the global online (Venkatraman, 2017, p. 199), the new role is developed in adherence to the norms of the userbase and by subsequently accepting the information presented on the platform – to feel welcome or accepted during the role’s development. For example, when a user makes a profile for the first time and is ‘added’ to chats or groups (Silver, 1996, p. 2). The usage of WhatsApp is not only a socio-economical, but also a socio-cultural role formed, maintained, and shared by its users. It is their subjective construction and belongs to a social environment (Callero, 2008, p. 1975). This role can also be deployed as a tool during specific situations and endorsed by the group as a whole to be legitimized or accepted, such as viral forwards or disinformation (Callero, 2008, p. 1977).
By becoming a social platform that has extended beyond individual or dyadic communication, WhatsApp is writing new norms for group interactions and impacting existing norms of social conformance, especially regarding dissent. Platforms that have done the same before have imbibed societal norms, which in India are tightly organised by the overlapping principles of kinship, age, gender, class and caste (Venkatraman, 2017, p. 206). These factors are being reflected on WhatsApp to trick and polarize users into picking sides on various socio-political and socio-cultural issues.
In conclusion, Generation X established patterns of homophily after heavy app usage, which placed them in specific communities built from their contacts – individuals who legitimized the information they already possessed. This seems to be an unintended side effect of the attempt to craft a chronologically hybrid social role in response to the advent of modern messaging networks. The mutualism between these relationships and the innate cultural biases they had was encouraged when they were surrounded by similar people – an effect of the private social network’s interface and their relationships in the real world. Furthermore, the interface also encouraged frequent content-based interaction that also reinforced their readiness to believe in the information that they were sending and receiving. These behavioural patterns are vulnerabilities that can be used to create and share content that reinforces their own beliefs through spurious and fabricated sources, and they are particularly visible among the chosen demographic. Further examination may yield solutions to prevent misinformation or worse.
References:
Silver, I. (1996). Role Transitions, Objects, and Identity. Symbolic Interaction, 1(19), 1-20.
Callero, P. (2008). The Globalization of Self: Role and Identity Transformation from Above and Below. Sociology Compass, 2(6), 1972-1988.
Singla, P., & Richardson, M. (Eds.). (2008). Yes, There is a Correlation – From Social Networks to Personal Behaviour on the Web. Proceedings of the 17th International World Wide Web Conference (WWW-2008).
Xiang, R., Neville, J., & Rogati, M. (2010). Modeling Relationship Strength in Online Social Networks. Proceedings of the 19th International World Wide Web Conference (WWW-2010).
Church, K., & de Oliveira, R. (2013). What’s up with WhatsApp? Comparing Mobile Instant Messaging Behaviors with Traditional SMS. MobileHCI ’13: Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services.
Venkatraman, S. (2017). Social Media in South India. UCL Press.
Rosenfeld, A., Sina, S., Sarne, D., Avidov, O., & Kraus, S. (2018). WhatsApp usage patterns and prediction of demographic characteristics without access to message content. Demographic Research, 39, 647-670.
Salve, P. (2015, October 29). Not 264 Million, Middle Class Is 24 Million. The Wire. https://thewire.in/economy/not-264-million-middle-class-is-24-million-report
Bansal, V. (2021, May 27). WhatsApp’s Fight with India Has Global Implications. Wired. https://www.wired.com/story/whatsapp-india-traceability-encryption/
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Abhiram Kuchibhotla is a graduate of the Manipal Centre for Humanities and writes fiction and non-fiction drawn from the web of Indian society. His pieces have been published in the IWP anthology Exodus, Verse of Silence, RIC Journal and Chaicopy.