skip to main content
10.1145/2872427.2882979acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

The Lifecycle and Cascade of WeChat Social Messaging Groups

Published: 11 April 2016 Publication History

Abstract

Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However, research community still knows little about the formation and evolution of groups in the context of social messaging their lifecycles, the change in their underlying structures over time, and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform the largest standalone messaging communication service in China with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifecycle, and develop a separability model by taking into account a broad range of group-level features, showing that long-term and short-term groups are inherently distinct. We also found that the lifecycle of messaging groups is largely dependent on their social roles and functions in users' daily social experiences and specific purposes. Given the strong separability between the long-term and short-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from group-level perspective, we investigate the individual-level attributes of group members and study the diffusion process by which groups gain new members. By considering members' historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.

References

[1]
WeChat group chat features. www.wechat.com/en/features.html#group.
[2]
WeChat wiki. en.wikipedia.org/wiki/WeChat.
[3]
E. Adar, L. Zhang, L. A. Adamic, and R. M. Lukose. Implicit structure and the dynamics of blogspace. In Workshop on the weblogging ecosystem, pages 16989--16995, 2004.
[4]
A. Anderson, D. Huttenlocher, J. Kleinberg, J. Leskovec, and M. Tiwari. Global diffusion via cascading invitations: Structure, growth, and homophily. In WWW, pages 66--76, 2015.
[5]
S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51):21544--21549, 2009.
[6]
L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD, pages 44--54. ACM, 2006.
[7]
E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. The role of social networks in information diffusion. In WWW, pages 519--528, 2012.
[8]
C. Buntain and J. Golbeck. Identifying social roles in reddit using network structure. In Proceedings of the companion publication of the 23rd international conference on World wide web companion, pages 615--620. ACM, 2014.
[9]
B. S. Butler. Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information systems research, 12(4):346--362, 2001.
[10]
M. Cha, A. Mislove, B. Adams, and K. P. Gummadi. Characterizing social cascades in flickr. In Proceedings of the first workshop on Online social networks, pages 13--18, 2008.
[11]
J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? In WWW, pages 925--936. ACM, 2014.
[12]
K. Church and R. de Oliveira. What's up with whatsapp?: comparing mobile instant messaging behaviors with traditional sms. In Mobile-CHI, pages 352--361. ACM, 2013.
[13]
N. Ducheneaut, N. Yee, E. Nickell, and R. J. Moore. The life and death of online gaming communities: a look at guilds in world of warcraft. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 839--848, 2007.
[14]
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. Liblinear: A library for large linear classification. The Journal of Machine Learning Research, 9:1871--1874, 2008.
[15]
M. Girvan and M. E. Newman. Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12):7821--7826, 2002.
[16]
S. Goel, A. Anderson, J. Hofman, and D. Watts. The structural virality of online diffusion. Preprint, 22:26, 2013.
[17]
M. Gomez Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1019--1028. ACM, 2010.
[18]
P. W. Holland and S. Leinhardt. Transitivity in structural models of small groups. Comparative Group Studies, 1971.
[19]
J. Hopcroft, O. Khan, B. Kulis, and B. Selman. Natural communities in large linked networks. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 541--546. ACM, 2003.
[20]
S. R. Kairam, D. J. Wang, and J. Leskovec. The life and death of online groups: Predicting group growth and longevity. In WSDM, pages 673--682. ACM, 2012.
[21]
I. Kloumann, L. Adamic, J. Kleinberg, and S. Wu. The lifecycles of apps in a social ecosystem. In WWW, pages 581--591, 2015.
[22]
M. Kubat, S. Matwin, et al. Addressing the curse of imbalanced training sets: one-sided selection. In ICML, volume 97, pages 179--186, 1997.
[23]
H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In WWW, pages 591--600. ACM, 2010.
[24]
J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1):5, 2007.
[25]
J. Leskovec, M. McGlohon, C. Faloutsos, N. S. Glance, and M. Hurst. Patterns of cascading behavior in large blog graphs. In SDM, volume 7, pages 551--556. SIAM, 2007.
[26]
Y. Li, K. He, D. Bindel, and J. E. Hopcroft. Uncovering the small community structure in large networks: A local spectral approach. In WWW, pages 658--668, 2015.
[27]
M. E. Newman. Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2):321--330, 2004.
[28]
G. Palla, A.-L. Barabási, and T. Vicsek. Quantifying social group evolution. Nature, 446(7136):664--667, 2007.
[29]
N. Park, K. F. Kee, and S. Valenzuela. Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6):729--733, 2009.
[30]
B. Ribeiro. Modeling and predicting the growth and death of membership-based websites. In WWW, pages 653--664. ACM, 2014.
[31]
D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In WWW, pages 695--704. ACM, 2011.
[32]
E. Sun, I. Rosenn, C. Marlow, and T. M. Lento. Gesundheit! modeling contagion through facebook news feed. In ICWSM, 2009.
[33]
Y. Sun, J. Tang, J. Han, C. Chen, and M. Gupta. Co-evolution of multi-typed objects in dynamic star networks. IEEE TKDE, 26(12):2942--2955, 2014.
[34]
J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg. Structural diversity in social contagion. Proceedings of the National Academy of Sciences, 109(16):5962--5966, 2012.
[35]
D. J. Watts. A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences, 99(9):5766--5771, 2002.
[36]
L. Weng, F. Menczer, and Y.-Y. Ahn. Virality prediction and community structure in social networks. Scientific reports, 3, 2013.
[37]
J. Yang, X. Wei, M. S. Ackerman, and L. A. Adamic. Activity lifespan: An analysis of user survival patterns in online knowledge sharing communities. In ICWSM, 2010.

Cited By

View all
  • (2024) WeChat use and social participation among community‐dwelling adults with severe mental disorders: A mixed‐methods study International Journal of Social Welfare10.1111/ijsw.12671Online publication date: 11-Apr-2024
  • (2023)Examining direct sales as a violation of friendship expectations on WeChatCogent Social Sciences10.1080/23311886.2023.22502079:2Online publication date: 21-Aug-2023
  • (2023)Predicting Information Diffusion Using the Inter- and Intra-Path of Influence TransitivityInformation Sciences10.1016/j.ins.2023.119705(119705)Online publication date: Sep-2023
  • Show More Cited By

Index Terms

  1. The Lifecycle and Cascade of WeChat Social Messaging Groups

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    WWW '16: Proceedings of the 25th International Conference on World Wide Web
    April 2016
    1482 pages
    ISBN:9781450341431
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    • IW3C2: International World Wide Web Conference Committee

    In-Cooperation

    Publisher

    International World Wide Web Conferences Steering Committee

    Republic and Canton of Geneva, Switzerland

    Publication History

    Published: 11 April 2016

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. group formation
    2. information diffusion
    3. online community
    4. social messaging

    Qualifiers

    • Research-article

    Funding Sources

    • Natural Science Foundation of China
    • National High-tech R&D Program
    • National Basic Research Program of China
    • US Army Research Office
    • National Social Science Foundation of China

    Conference

    WWW '16
    Sponsor:
    • IW3C2
    WWW '16: 25th International World Wide Web Conference
    April 11 - 15, 2016
    Québec, Montréal, Canada

    Acceptance Rates

    WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)53
    • Downloads (Last 6 weeks)4
    Reflects downloads up to 14 Sep 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024) WeChat use and social participation among community‐dwelling adults with severe mental disorders: A mixed‐methods study International Journal of Social Welfare10.1111/ijsw.12671Online publication date: 11-Apr-2024
    • (2023)Examining direct sales as a violation of friendship expectations on WeChatCogent Social Sciences10.1080/23311886.2023.22502079:2Online publication date: 21-Aug-2023
    • (2023)Predicting Information Diffusion Using the Inter- and Intra-Path of Influence TransitivityInformation Sciences10.1016/j.ins.2023.119705(119705)Online publication date: Sep-2023
    • (2023)The Unbelieving Minority: Singapore’s Anti-Falsehood Law and Vaccine ScepticismMobile Communication and Online Falsehoods in Asia10.1007/978-94-024-2225-2_3(27-43)Online publication date: 12-Jun-2023
    • (2022)Identifying User Relationship on WeChat Money-Gifting NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303080734:8(3814-3825)Online publication date: 1-Aug-2022
    • (2022)On Time-optimal (k, p)-core Community Search in Dynamic Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00109(1396-1407)Online publication date: May-2022
    • (2021)From Symbols to Embeddings: A Tale of Two Representations in Computational Social ScienceJournal of Social Computing10.23919/JSC.2021.00112:2(103-156)Online publication date: Jun-2021
    • (2021)All in One Group: Current Practices, Lessons and Challenges of Chinese Home-School Communication in IM Group ChatProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445436(1-12)Online publication date: 6-May-2021
    • (2021)Efficient Top-k Vulnerable Nodes Detection in Uncertain GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3094549(1-1)Online publication date: 2021
    • (2021)Structure-Aware Parameter-Free Group Query via Heterogeneous Information Network Transformer2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00203(2075-2080)Online publication date: Apr-2021
    • Show More Cited By

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media