  The ability to predict the size of information cascades in online social
networks is crucial for various applications, including decision-making and
viral marketing. However, traditional methods either rely on complicated
time-varying features that are challenging to extract from multilingual and
cross-platform content, or on network structures and properties that are often
difficult to obtain. To address these issues, we conducted empirical research
using data from two well-known social networking platforms, WeChat and Weibo.
Our findings suggest that the information-cascading process is best described
as an activate-decay dynamical process. Building on these insights, we
developed an Activate-Decay (AD)-based algorithm that can accurately predict
the long-term popularity of online content based solely on its early repost
amount. We tested our algorithm using data from WeChat and Weibo, demonstrating
that we could fit the evolution trend of content propagation and predict the
longer-term dynamics of message forwarding from earlier data. We also
discovered a close correlation between the peak forwarding amount of
information and the total amount of dissemination. Finding the peak of the
amount of information dissemination can significantly improve the prediction
accuracy of our model. Our method also outperformed existing baseline methods
for predicting the popularity of information.
