Internet social networks have become a ubiquitous application allowing people to easily share text, pictures, and audio and video files. Popular networks include WhatsApp, Facebook, Reddit and LinkedIn. We present an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. In order to better understand people's use of the network, we provide an analysis of over 6 million messages from over 100 users, with the objective of building demographic prediction models using activity data. We performed extensive statistical and numerical analysis of the data and found significant differences in WhatsApp usage across people of different genders and ages. We also inputted the data into the Weka data mining package and studied models created from decision tree and Bayesian network algorithms. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. We also noted differences in users' group behavior and created prediction models, including the likelihood a given group would have relatively more file attachments, if a group would contain a larger number of participants, a higher frequency of activity, quicker response times and shorter messages. We were successful in quantifying and predicting a user's gender and age demographic. Similarly, we were able to predict different types of group usage. All models were built without analyzing message content. We present a detailed discussion about the specific attributes that were contained in all predictive models and suggest possible applications based on these results.
Much recent work has been dedicated to the study of how people use WhatsApp and the role of this new application in social communication. Most works to date have analyzed peoples' behavior through conducting surveys and targeted interviews. For example, work by Church and Oliveira conducted an online study asking targeting questions of users which were aimed at understanding differences between WhatsApp and SMS usage @cite_14 . Pielot et. al @cite_11 created a survey focusing on the question whether people expected an answer to their WhatsApp and SMS messages within several minutes. O'Hara et. al interviewed 20 WhatsApp users for nearly an hour each, asking them semi-structured questions aimed at determining the nature of relationships forged with the people with whom they communicated @cite_22 . Mudliar and Rangaswamy @cite_20 spent over 350 hours observing 109 students, as well as conducted surveys to understand gender differences within Indian students' use of WhatsApp. All of these studies can be characterized as being formed through a desire to answer specific questions by conducting targeted surveys and interviews.
@cite_14 @cite_22 @cite_20 @cite_11
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In this paper, we present a study of WhatsApp, an instant messaging smartphone application. Through our interviews with participants, we develop anthopologist Tim Ingold's notion of dwelling, and discuss how use of WhatsApp is constitutive of a felt-life of being together with those close by. We focus on the relationship "doings" in WhatsApp and how this togetherness and intimacy are enacted through small, continuous traces of narrative, of tellings and tidbits, noticings and thoughts, shared images and lingering pauses; this is constitutive of dwelling. Further, we discuss how an intimate knowing of others in these relationships, through past encounters and knowledge of coming together in the future, pertain to the particular forms of relationship engagements manifest through the possibilities presented in WhatsApp. We suggest that this form of sociality is likely to be manifest in other smartphone IM-like applications. Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6 accuracy and a precision for fast attendance of 81.2 We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender. In this paper, we present results from an online survey with 1,510 participants and an interview study with 31 participants on (secure) mobile instant messaging. Our goal was to uncover how much of a role security and privacy played in people’s decisions to use a mobile instant messenger. In the interview study, we recruited a balanced sample of ITsecurity experts and non-experts, as well as an equal split of users of mobile instant messengers that are advertised as being more secure and or private (e.g., Threema) than traditional mobile IMs. Our results suggest that peer influence is what primarily drives people to use a particular mobile IM, even for secure private IMs, and that security and privacy play minor roles.