Findings

6.2. Comparing Different Sources for WhatsApp Public Groups on Web 114

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Chapter 7

The Information Pathways through WhatsApp

In this chapter, we will investigate how the functionalities presents in WhatsApp and its network structure impact in the dissemination of information within this social media.

After discovering the universe of public groups being shared on the Web and the extensive use of WhatsApp to propagate political messages and news in public groups, one may wonder how it can be possible within an app that is mainly used for private communica-tion. In fact, in this chapter, we will observe that a piece of information in WhatsApp can navigate through a network and reach numerous users, and can become a popular trend between users. We will understand better how some features implemented by WhatsApp and how the format of groups can assist a message to get viral in such closed environment and how it differs from other platforms that use group-based communication.

Nowadays, many people prefer to use these messaging apps to directly speak with each other. This popularity, which is particularly seen in countries like Brazil and India, is partially explained by some distinctive traits of such kind of system. First, IMPs are cheaper tools than old Short Message Service (SMS) messages of mobile phones, which people were used to. Second, they offer multiple features that were absent in SMS, such as various forms of communication, including texting, voice and video calling, chat groups, and the possibility to send and forward messages to multiple users. On top of that, all messages are encrypted and hence anonymous, which provides more security to the communication. All those factors influenced in the shift of instant communication to this new IMP cyberspace.

However, the characteristics of these platforms are still not fully understood and their consequences are still unknown by their users and even researchers. One controversy raised by introduction of IMPs is about anonymity and virality (PATEL et al., 2019;

MAYER, 2019), in which many have concerns that harmful content of hate speech or fake news may spread within those platforms without any moderation, unable to find the authors nor even possible stop the sharing of this content.

Actually, for Brazilians who lived through the presidential elections of 2018 and 2022, who had a smartphone and participated in WhatsApp groups, it was possible to

116 observe the capacity for viralization and potential capillarity that content from those platform reach in society. Indians also suffered with real life mob lynchings caused by misinformation campaigns and rumors spread through WhatsApp. Family groups, work groups, friends, political groups and many other scenarios on WhatsApp have been flooded by the spread of misinformation.

WhatsApp itself realized this position of spreading of misinformation and rushed to self-regulate to mitigate this viral problem: after having increased the size of groups from 100 to up to 256 participants in 2016, WhatsApp decided to limit simultaneous message forwarding to five recipients at a time in 2019¹and, subsequently, identify and further limit the sharing of widely shared content, to just a single user after the COVID-19 pandemic in 2020². However, the company seems to have turned in the opposite direction with the creation of communities and increasing groups max size³, facilitating even more the wide spreading of unmoderated content. On Telegram, messages can only be forwarded to one chat at a time, but the size of groups is unlimited, which also helps to magnify the reach of messages.

The forwarding button combined with the large public groups are important tools in information propagation within IMPs. Using this forward method, a user can select a message from a conversation and promptly spread it to a large number of contacts or groups at once, while the existence of groups with hundreds of members, or even millions in Telegram, is a quick path that facilitates the mass communication.

Although we already know that messages in such platforms spread rapidly and broadly, we do not know if (and how) their dissemination can be controlled by the features of information diffusion provided by those systems. We lack of information of how exactly this spreading happens within this network, how a single message exchanged in a chat is amplified and circulate throughout WhatsApp.

In order to evaluate this massive viralization on WhatsApp, we perform a seep investigation about how the features of public groups and forwarding messages impact in the flow of content through this distinctive network topology, highlighting the influence of them in making a content getting viral. Furthermore, we analyzed data from more than 5,000 public WhatsApp groups and involved over 360K users from our collection methodology in Brazil (Chapter 5), as well data from India, and Indonesia shared in collaboration from the authors of GARIMELLA; TYSON, 2018; GARIMELLA et al., 2018.

We used this data to reconstruct the network of public groups on WhatsApp, in which users are connected when they have groups in common. We analyzed some network characteristics for each set of data, and evaluated the propagation of information

1<https://blog.whatsapp.com/more-changes-to-forwarding>

2<https://about.fb.com/news/2020/04/whatsapp-message-forward-limit/>

3<https://blog.whatsapp.com/reactions-2gb-file-sharing-512-groups>

117 through these networks using an epidemic model. Moreover, we expand this analysis by evaluating how information propagates in general Instant Messaging Platforms by exploring also the Telegram network. Besides a deeper analysis of the WhatsApp group network, we also compare it to the network composed by groups on Telegram. We also perform several simulations to assess how their sharing and topological features affect information dissemination.

More specifically, we study the anatomy of this emerging way of communication by analyzing their groups networks. Our goal is to answer the question of how the tools of the system contribute to the virality of (mis)information and whether limitations are capable of preventing the spread of content. In short, our goal is to understand the effects of the group-based network topology and the sharing features (forward and broadcast) of IMPs in the diffusion and spreading of (mis)information within their networks. More specifically, we aim to answer the following questions:

• What is the impact of public groups on the propagation of information in different Instant Messaging Platforms’ network structures?

• Is it possible to control the spread of false content with the forward limitations imposed by the application?

• What is the importance of the features and topological structure of the network to the dissemination of information on these platforms?

To address these questions, we use the Susceptible-Exposed-Infected (SEI) epidemi-ological model (LI; ZHEN, 2005) to the problem of estimating the virality of malicious messages in IMPs groups. In this model,Susceptible(S) is the initial condition in which the user did not have any contact with the false content; Exposed (E) are the people who received the false news through any of the groups they participate; Infected (I) is the final stage where a user who was exposed to the content shares this message in the network.

Since the platforms have different size of groups, restrictions on how many people a message can be forwarded, and have different network structures, we have included those characteristics in our proposed simulation model to better investigate the process of information diffusion. Thus, we adapted some parameters of the original Susceptible-Infected-Recovery (SIR) model to simulate the functioning of those IMPs networks using different configurations of forward and virality limits. In this work, he have added a new class of vector in the simulations: the “exposed”. They represent a prior stage of contamination in which represents those who only received a malicious messages in a group and, therefore, is exposed to that content. They only become a proper “infected”

users when they get in contact and re-share that content for other groups (which may mean that they believed in that content). To simplify the model, we firstly remove the

7.1. Virality Features of Instant Messaging Platforms 118

“recovery” as we do not have evidences of user “recovering” from sharing the content (particularly for misinformation), however, we also performed experiments exploring this possibility at the end of the work.

The rest of this chapter is organized as follows. Before start the analysis of the data, Section7.1 provides a discussion of the main characteristics of WhatsApp and Telegram platforms that contribute to information spreading on WhatsApp. In Section 7.3, there is the description of the dataset. In Section 7.4, we analyze the network structure of public groups on WhatsApp and Telegram. The experiments are in Section7.5, where we simulate the spread of misinformation within those networks under different circumstances via a Susceptible-Exposed-Infected (SEI) epidemiological model. Further evaluation of the data and more results for time spreading collected for three countries (India, Indonesia, and Brazil) on WhatsApp is presented in Section 7.5.5. Finally, Section 7.6 we present the final remarks of this piece of work.

7.1 Virality Features of Instant Messaging Platforms

Before we start to analyze the data collected and its implications, we must under-stand the instant messaging platforms’ (IMPs) operations and the differences among them and to other kinds of networks that contribute to its become a viral tool to disseminate content.

At the beginning of the Internet era, instant messengers such as ICQ or MSN Messenger were more focused on one-to-one communication, in which only two users exchanged messages directly to each other. After the turn of the millennium, a new trend in online communication emerged together with popular platforms such as Facebook and Twitter: the one-to-many model, in which users can broadcast information to users who follow their feed. Current IMPs models fit in a different category of group-based communication (SEUFERT et al., 2016), in which multiple users in these platforms join together in groups where all of them are able to send and receive information from each other. These groups compose communities in a form of many-to-many communication, but in a restricted environment compared to a full broadcast for the Web. With the widespread use of smartphones and easy access to the internet, mobile apps for IMPs such as WhatsApp and Telegram became increasingly popular, as they provide services that are cheaper and richer than SMS. More important to the context of this work, they are not limited to one-to-one or one-to-many conversations, as most of them provide group

7.1. Virality Features of Instant Messaging Platforms 119 conversation features.

Comprehending this evolution of communication paradigms and some of the fea-tures developed by these platforms is essential to the understanding of how services such as WhatsApp and Telegram turned into tools to disseminate fake news and became protag-onists in misinformation campaigns around the globe. While one-to-one communication is more private, individual, personal, encrypted, one-to-many communication tends to be public, in mass, and viral. The group-based communication, on the other hand, incorpo-rates the characteristics of both models. Messages on these IMPs can involve only two individuals, but, at the same time, can become viral and reach a massive number of users through their network.

These applications have key features that contribute to a message to disseminate in the network and then become viral. The groups’ creation allows hundreds of users to talk simultaneously, and each one of them has access to forwarding tools to quickly share the content to other users. In the forwarding process, the encrypted nature of these apps makes it hard to track the original source or author of a message. The users access the information that has been forwarded, but they may not distinguish if this received content is originated directly from one of their known contacts or if it comes from a more extensive path created by a totally unknown creator. Then, being a message received from a friend or a complete stranger, from the perspective of the final users, all messages are equally, and always received from a direct contact within a chat that this final user participates, even though if it has been already shared by multiple users in the network.

In addition, it may have no clue of how many times it had been shared before reaching the user’s phone.

Misinformation campaigns take advantage of this space that blends public and private information to disseminate false content misleading the users, where the real authors are still anonymous, hidden by the closed nature of the network. Then a question that remains with this aspect of IMP is that should an encrypted message be able to go viral?

Next, we discuss more specifically some of the features that contribute to this scenario, comparing the differences of how they are used by Telegram and WhatsApp.