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Dynamics of conversations

Published: 25 July 2010 Publication History

Abstract

How do online conversations build? Is there a common model that human communication follows? In this work we explore these questions in detail. We analyze the structure of conversations in three different social datasets, namely, Usenet groups, Yahoo! Groups, and Twitter. We propose a simple mathematical model for the generation of basic conversation structures and then refine this model to take into account the identities of each member of the conversation.

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References

[1]
R. Albert and A.-L. Barabasi. Emergence of scaling in random networks. Science, 286:509--512, 1999.
[2]
L. Backstrom, R. Kumar, C. Marlow, J. Novak, and A. Tomkins. Preferential behavior in online groups. In Proc. 1st WSDM, pages 117--128, 2008.
[3]
L. Barabiási. The origin of bursts and heavy tails in human dynamics. Nature, 435, 2005.
[4]
S. Bikhchandani, D. Hirshleifer, and I. Welch. A theory of fads, fashion, custom, and cultural change in informational cascades. Journal of Political Economy, 100(5):992--1026, 1992.
[5]
B. Bollobas. Random Graphs. Cambridge, 2001.
[6]
B. Bollobas and O. Riordan. Mathematical Results on Scale-Free Random Graphs, pages 1--37. Wiley-WCH, 2002.
[7]
H.-C. Chen, M. Magdon-Ismail, M. Goldberg, and W. A. Wallace. Inferring agent dynamics from social communication network. In Proc. 9th WebKDD, 2007.
[8]
Z. Dezsö, E. Almaas, A. Lukács, B. Rácz, I. Szakadát, and A.-L. Barabási. Dynamics of information access on the web. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 73(6):066132, 2006.
[9]
R. Durrett. Random Graph Dynamics. Cambridge, 2006.
[10]
D. Fisher, M. Smith, and H. T. Welser. You are who you talk to: Detecting roles in usenet newsgroups. In Proc. 39th HICSS, 2006.
[11]
J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3):209--221, 2001.
[12]
B. Golub and M. O. Jackson. The power of selection bias in explaining the structure of observed Internet diffusions. Proc. National Academy of Sciences, To appear.
[13]
M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, 83(6):1420--1443, 1978.
[14]
T. E. Harris. The Theory of Branching Processes. Dover, 2002.
[15]
H. S. Heaps. Information Retrieval: Computational and Theoretical Aspects. Academic Press, 1978.
[16]
J. Kleinberg. Complex networks and decentralized search algorithms. In Proc. International Congress of Mathematicians, 2006.
[17]
R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In Proc. 12th KDD, pages 611--617, 2006.
[18]
R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, and E. Upfal. Stochastic models for the web graph. In Proc. 41st FOCS, pages 57--65, 2000.
[19]
J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. In Proc. 7th EC, pages 228--237, 2006.
[20]
J. Leskovec, L. Backstrom, and J. M. Kleinberg. Meme-tracking and the dynamics of the news cycle. In Proc. 15th KDD, pages 497--506, 2009.
[21]
J. Leskovec, L. Backstrom, R. Kumar, and A. w. Tomkins. Microscopic evolution of social networks. In Proc.14th KDD, pages 462--470, 2008.
[22]
J. Leskovec, J. M. Kleinberg, and C. Faloutsos. Graph evolution: Densification and shrinking diameters. TKDD, 1(1), 2007.
[23]
J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs. In Proc. 7th SDM, 2007.
[24]
J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. In Proc. 10th PAKDD, pages 380--389, 2006.
[25]
D. Liben-Nowell and J. Kleinberg. Tracing the flow of information on a global scale using Internet chain-letter data. Proc. National Academy of Sciences, 105(12):4633--4638, 2008.
[26]
M. McGlohon and M. Hurst. Community structure and information flow in usenet: Improving analysis with a thread ownership model. In Proc. 3rd ICWSM, 2009.
[27]
M. Mcglohon, J. Leskovec, C. Faloutsos, M. Hurst, and N. Glance. Finding patterns in blog shapes and blog evolution. In Proc. 1st ICWSM, 2007.
[28]
M. Molloy and B. Reed. A critical point for random graphs with a given degree sequence. Random Structures and Algorithms, 6(2/3):161--180, 1995.
[29]
R. Motwani and P. Raghavan. Randomized Algorithms. Cambridge, 1995.
[30]
T. C. Turner, M. A. Smith, D. Fisher, and H. T. Welser. Picturing usenet: Mapping computer-mediated collective action. Journal of Computer-Mediated Communication, 10(4), 2005.
[31]
A. Vazquez. Spreading dynamics on heterogeneous populations: multi-type network approach. Physical Review Letters, 74, 2006.
[32]
F. B. Viegas and M. Smith. Newsgroup crowds and authorlines: Visualizing the activity of individuals in conversational cyberspaces. In Proc. 37th HICSS, page 10, 2004.
[33]
D. J. Watts. A simple model of global cascades on random networks. Proc. National Academy of Sciences, 99(9):5766--5771, 2002.

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cover image ACM Conferences
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
July 2010
1240 pages
ISBN:9781450300551
DOI:10.1145/1835804
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]

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Publication History

Published: 25 July 2010

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Author Tags

  1. Twitter
  2. conversations
  3. graph models
  4. groups
  5. human response
  6. threads
  7. usenet

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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  • (2024)Characterizing the Structure of Online Conversations Across RedditProceedings of the ACM on Human-Computer Interaction10.1145/36869138:CSCW2(1-23)Online publication date: 8-Nov-2024
  • (2024)A Topology-Based Approach for Predicting Toxic Outcomes on Twitter and YouTubeIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339821911:5(4875-4885)Online publication date: Sep-2024
  • (2024)Analyzing Hate Speech Dynamics on Twitter/X: Insights from Conversational Data and the Impact of User Interaction PatternsHeliyon10.1016/j.heliyon.2024.e32246(e32246)Online publication date: May-2024
  • (2023)Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political DiscussionProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570487(33-41)Online publication date: 27-Feb-2023
  • (2023)Predicting continuity of online conversations on RedditTelematics and Informatics10.1016/j.tele.2023.10196579(101965)Online publication date: Apr-2023
  • (2023)Branching processes reveal influential nodes in social networksInformation Sciences10.1016/j.ins.2023.119201644(119201)Online publication date: Oct-2023
  • (2023)Hass im Netz – Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und AnalyseHandbuch Cyberkriminologie 110.1007/978-3-658-35439-8_13(261-292)Online publication date: 29-Aug-2023
  • (2022)Graph-Based Conversation Analysis in Social MediaBig Data and Cognitive Computing10.3390/bdcc60401136:4(113)Online publication date: 12-Oct-2022
  • (2022)Arguments at Odds—Dyadic Turn-Taking and Conflict Development in Consensus-Making GroupsSmall Group Research10.1177/1046496422111867454:4(551-589)Online publication date: 26-Aug-2022
  • (2022)Characterization of Emotional Contagion in Collaborative Decision Support Systems2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)10.1109/CIC56439.2022.00025(109-116)Online publication date: Dec-2022
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