Authors: Anika Seufert, Fabian Poignée, Michael Seufert, Tobias Hoßfeld
Abstract:
Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.
Cite
MLA:
Seufert, Anika, et al. "Share and Multiply: Modeling Communication and Generated Traffic in Private WhatsApp Groups." IEEE Access 11 (2023): 25401-25414.
BibTeX:
@article{info3-article-2023-2,
title={Share and Multiply: Modeling Communication and Generated Traffic in Private WhatsApp Groups},
author={Seufert, Anika and Poign{\'e}e, Fabian and Seufert, Michael and Ho{\ss}feld, Tobias},
journal={IEEE Access},
volume={11},
pages={25401--25414},
year={2023},
publisher={IEEE}
}
Authors: Anika Seufert, Fabian Poignée, Tobias Hoßfeld, Michael Seufert
Abstract:
The strict restrictions introduced by the COVID-19 lockdowns, which started from March 2020, changed people’s daily lives and habits on many different levels. In this work, we investigate the impact of the lockdown on the communication behavior in the mobile instant messaging application WhatsApp. Our evaluations are based on a large dataset of 2577 private chat histories with 25,378,093 messages from 51,973 users. The analysis of the one-to-one and group conversations confirms that the lockdown severely altered the communication in WhatsApp chats compared to pre-pandemic time ranges. In particular, we observe short-term effects, which caused an increased message frequency in the first lockdown months and a shifted communication activity during the day in March and April 2020. Moreover, we also see long-term effects of the ongoing pandemic situation until February 2021, which indicate a change of communication behavior towards more regular messaging, as well as a persisting change in activity during the day. The results of our work show that even anonymized chat histories can tell us a lot about people’s behavior and especially behavioral changes during the COVID-19 pandemic and thus are of great relevance for behavioral researchers. Furthermore, looking at the pandemic from an Internet provider perspective, these insights can be used during the next pandemic, or if the current COVID-19 situation worsens, to adapt communication networks to the changed usage behavior early on and thus avoid network congestion.
Cite
MLA:
Seufert, Michael, et al. "Pandemic in the Digital Age: Analyzing WhatsApp Communication Behavior before, during, and after the COVID-19 Lockdown." Humanities and Social Sciences Communications. 9, 140 (1–9) (2022).
BibTeX:
@article{info3-article-2022-14,
title={Pandemic in the Digital Age: Analyzing WhatsApp Communication Behavior before, during, and after the COVID-19 Lockdown},
author={Seufert, Anika and Poignée, Fabian and Hoßfeld, Tobias and Seufert, Michael},
journal={Humanities and Social Sciences Communications},
pages={140 (1-9)},
year={2022},
}
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.
Cite
MLA:
Seufert, Michael, et al. "Analysis of group-based communication in whatsapp." International Conference on Mobile Networks and Management. Springer, Cham, 2015.
BibTeX:
@inproceedings{seufert2015analysis,
title={Analysis of group-based communication in whatsapp},
author={Seufert, Michael and Schwind, Anika and Ho{\ss}feld, Tobias and Tran-Gia, Phuoc},
booktitle={International Conference on Mobile Networks and Management},
pages={225--238},
year={2015},
organization={Springer}
}
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.
Cite
MLA:
Seufert, Michael, et al. "Group-based communication in WhatsApp." 2016 IFIP networking conference (IFIP networking) and workshops. IEEE, 2016.
BibTeX:
@inproceedings{seufert2016group,
title={Group-based communication in WhatsApp},
author={Seufert, Michael and Ho{\ss}feld, Tobias and Schwind, Anika and Burger, Valentin and Tran-Gia, Phuoc},
booktitle={2016 IFIP networking conference (IFIP networking) and workshops},
pages={536--541},
year={2016},
organization={IEEE}
}
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.
Cite
MLA:
Seufert, Michael, et al. "Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging." 2018 14th International Conference on Network and Service Management (CNSM). IEEE, 2018.
BibTeX:
@inproceedings{8584984,
title={Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging},
author={M. {Seufert} and A. {Schwind} and M. {Waigand} and T. {Hoßfeld}},
booktitle={2018 14th International Conference on Network and Service Management (CNSM)},
pages={177-183},
year={2018},
organization={IEEE}
}