Modeling information diffusion in social media: data-driven observations

Adriana Iamnitchi*, Lawrence O. Hall, Sameera Horawalavithana, Frederick Mubang, Kin Wai Ng, John Skvoretz

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was "to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution." In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.
Original languageEnglish
Article number1135191
Number of pages19
JournalFrontiers in Big Data
Volume6
DOIs
Publication statusPublished - 17 May 2023

Keywords

  • social media
  • forecasting
  • data-driven
  • Twitter
  • Reddit
  • YouTube
  • LINK PREDICTION

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