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Towards understanding the lifespan and spread of ideas: epidemiological modeling of participation on Twitter

Authors:

Sai Santosh Sasank Peri,

Angela Liegey Dougall,

George SiemensAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 197 - 202

https://doi.org/10.1145/3375462.3375515

Published: 23 March 2020 Publication History

Abstract

How ideas develop and evolve is a topic of interest for educators. By understanding this process, designers and educators are better able to support and guide collaborative learning activities. This paper presents an application of our Lifespan of an Idea framework to measure engagement patterns among individuals in communal socio-technical spaces like Twitter. We correlated engagement with social participation, enabling the process of idea expression, spread, and evolution. Social participation leads to transmission of ideas from one individual to another and can be gauged in the same way as evaluating diseases. The temporal dynamics of the social participation can be modeled through the lens of epidemiological modeling. To test the plausibility of this framework, we investigated social participation on Twitter using the tweet posting patterns of individuals in three academic conferences and one long term chat space. We used a basic SIR epidemiological model, where the rate parameters were estimated through Euler's solutions to SIR model and non-linear least squares optimization technique. We discuss the differences in the social participation among individuals in these spaces based on their transition behavior into different categories of the SIR model. We also made inferences on how the total lifetime of these different twitter spaces affects the engagement among individuals. We conclude by discussing implications of this study and planned future research of refining the Lifespan of an Idea Framework.

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Digital Library

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Cited By

Bonifazi GCorradini EUrsino DVirgili L(2023)Modeling, Evaluating, and Applying the eWoM Power of Reddit PostsBig Data and Cognitive Computing10.3390/bdcc70100477:1(47)Online publication date: 9-Mar-2023
https://doi.org/10.3390/bdcc7010047
Khalil MWong JEr EHeitmann MBelokrys GWise AMartinez-Maldonado RHilliger I(2022)Tweetology of Learning Analytics: What does Twitter tell us about the trends and development of the field?LAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506914(347-357)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1145/3506860.3506914
Chen BShui HHåklev S(2022)Orchestrating the flow and advancement of knowledge artifacts in an online classInstructional Science10.1007/s11251-022-09596-350:6(903-931)Online publication date: 2-Sep-2022
https://doi.org/10.1007/s11251-022-09596-3
Show More Cited By

Index Terms

Towards understanding the lifespan and spread of ideas: epidemiological modeling of participation on Twitter
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social networking sites
    2. Decision support systems
      1. Data analytics

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Published In

cover image ACM Other conferences

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

Copyright © 2020 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2020

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National Science Foundation

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LAK '20

LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Bonifazi GCorradini EUrsino DVirgili L(2023)Modeling, Evaluating, and Applying the eWoM Power of Reddit PostsBig Data and Cognitive Computing10.3390/bdcc70100477:1(47)Online publication date: 9-Mar-2023
https://doi.org/10.3390/bdcc7010047
Khalil MWong JEr EHeitmann MBelokrys GWise AMartinez-Maldonado RHilliger I(2022)Tweetology of Learning Analytics: What does Twitter tell us about the trends and development of the field?LAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506914(347-357)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1145/3506860.3506914
Chen BShui HHåklev S(2022)Orchestrating the flow and advancement of knowledge artifacts in an online classInstructional Science10.1007/s11251-022-09596-350:6(903-931)Online publication date: 2-Sep-2022
https://doi.org/10.1007/s11251-022-09596-3
Kauk JKreysa HSchweinberger S(2021)Understanding and countering the spread of conspiracy theories in social networks: Evidence from epidemiological models of Twitter dataPLOS ONE10.1371/journal.pone.025617916:8(e0256179)Online publication date: 12-Aug-2021
https://doi.org/10.1371/journal.pone.0256179

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