Philipp Lorenz Talk

The September talk series continues full steam ahead. This week, you have a chance to see Philipp Lorenz talk about dynamics of topics in online social media. Philipp is a PhD student at TU Berlin’s Institut für Theoretische Physik in the Nonlinear Dynamics and Control: empirical networks and neurodynamics group. Phillip’s work focuses on temporal communities of hashtags, modeling the rise and fall of online topics, threshold models with repost and recovery, and more. Details of the talk below

  • Date: Tuesday, September 19th
  • Time: 2pm
  • Location: DTU, Building 321, 1st floor lab-space
  • Title: Capturing and modeling the dynamics of online topics

Abstract: Online media have a huge impact on public opinion, economics and politics. Every day, billions of posts are created and comments are written, covering a broad range of topics. Especially the format of hashtags, as a discrete and condensed version of online content, is in our focus. Here we present a pipeline, consisting of methods from static community detection as well as novel approaches for tracing the dynamics of topics in temporal data. We build co-occurrence networks from hashtags with timestamped edges. On static snapshots we infer the community structure and solve the resulting bipartite matching problem, by taking into account higher order memory. The results are robust to temporal fluctuations and instabilities of the static community detection.

The resulting dynamics in various datasets and for different observables, such as the community sizes or the likes they gather, as a proxy for the popularity of a topic, we observe universal behavior. Despite their versatility we find that in all datasets the distributions of gains and losses in popularity are fat-tailed, indicating occasional but large and sudden changes in public interest.

We hypothesise that only a few mechanisms may govern this behavior:

  • Gaining interest follows the rule of preferential attachment .
  • Saturation of the limited attention span decreases its fame.
  • discrete ranking leads to a competition between threads.

With these ingredients, we are able to design a class of models, which can reproduce the qualitative dynamics and the quantitative distributions of dynamical properties in the empirical observations. The model parameters and the required configuration for a given dataset is informational with respect to the sociological and psychological mechanisms that drive the dynamics of popularity in different contexts.

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