Publications

Authors: Anika Schwind, Michael Seufert

Abstract:
WhatsAnalyzer is a web-based service, which collects and analyzes chat histories of the mobile messaging application WhatsApp. Thereby, it leverages the e-mail export feature of WhatsApp to obtain the chat histories, which cannot be accessed otherwise due to encrypted storage on the mobile device and end-to-end encrypted transmission over the Internet. Thus, the major asset of the service is that real communication data can be collected without the bias introduced by observing or surveying participants. The collected communication data can be analyzed and provides valuable insights into the communication in WhatsApp and the resulting network traffic. To incentivize users to send chat histories, the privacy of users is respected by anonymizing all communication data. Moreover, some analyses of each chat history can be accessed on a web page by the sender of the chats.
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Analysis of Group-Based Communication in WhatsApp


Authors: Michael Seufert, Anika Schwind, Tobias Hoßfeld, Phuoc Tran-Gia

Abstract:
This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.
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Group-based communication in WhatsApp


Authors: Michael Seufert, Tobias Hoßfeld, Anika Schwind, Valentin Burger, Phuoc Tran-Gia

Abstract:
WhatsApp is a very popular mobile messaging application, which dominates todays mobile communication. Especially the feature of group chats contributes to its success and changes the way people communicate. The group-based communication paradigm is investigated in this work, particularly focusing on the usage of WhatsApp, communication in group chats, and implications on mobile network traffic.
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Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging


Authors: Michael Seufert, Anika Schwind, Marco Waigand, Tobias Hoßfeld

Abstract:
Communication groups in mobile messaging applications (MMAs) multiply the data transmissions, because every message has to be delivered to all members of the communication group. Thereby, they put a high load on mobile networks. As the number of recipients is still comparably small, the data-intensive user-generated content cannot be handled efficiently in large content delivery networks. However, small communication groups, such as groups of friends or teams, might often be in close proximity, which can be leveraged to locally deliver messages by applying edge caching or device-to-device (D2D) communication. In this work, a simulation study is conducted to investigate these potential traffic savings in the mobile network. It is based on a realistic communication model of the MMA WhatsApp and utilizes different models for human mobility. The user mobility and MMA communication are simulated for a single day in a small city to obtain the ratio of messages, which could be potentially transmitted locally when utilizing edge caching and D2D communication.
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