Abstract
BackgroundHashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings.Hashtag communities in timeWe build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics.Modeling topic-dynamicsWe consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.
Highlights
Hashtags are widely used for communication in online media
In [14], we introduced a random-walk approach for hashtag community detection and a subsequent memory-based matching scheme on temporal networks
To account for this, we extend the existing models by a recency ranking and gain a deeper understanding of the complex dynamics of the ever-changing usage of hashtags
Summary
As a condensed version of information, they characterize topics and discussions For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage These features can be reproduced with a novel model of dynamic rankings. We solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctua‐ tions and instabilities of the static community detection. By incorporating higher orders of memory [13] in a method proposed in [14], long-term developments can be tracked reliably
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