## 3 Dataset and Features  
### Social Network Dynamics and Dataset Features  
Information diffusion in social networks involves unique mechanisms that distinguish it from other network propagation processes. In this study, we focus on three critical aspects of social network dynamics:  
* First, social networks exhibit user-specific engagement patterns where individuals interact with content at varying frequencies and intensities. This creates natural delays between exposure to information and subsequent sharing actions. On platforms like Reddit, these delays manifest as time gaps between viewing a post and commenting on or upvoting it.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Model Type** & **Key Features** & **Application Domain** \\ \hline [PERSON] et al. (2003) [4] & Independent & Influence maximization, & \\ Cascade/Linear Threshold & greedy algorithm & Social influence \\ \hline [PERSON] et al. (2007) [5] & Cascading behavior & Empirical analysis, cascade & \\  & & patterns & \\ \hline [PERSON] et al. (2010) [6] & Delay-aware Independent & Parameter estimation, & \\ Cascade & model selection & Behavioral analysis \\ \hline [PERSON] \& [PERSON] (2012) & Continuous-time diffusion & Competing contagions, & Multiple information \\ [7] & Continuous-time diffusion & cooperation/competition & streams \\ \hline [PERSON] et al. (2021, 2024) & Population dynamics & Warning timeliness, & \\  [8,11] & & crisis focus & Emergency alerting \\ \hline [PERSON] et al. (2020) [9] & SEI (epidemiological) & Exposed state & General sharing \\ \hline [PERSON] \& [PERSON] & & \\ (2021) [1] & Time-fractional & Super-/sub-diffusion & Twitter/Digg \\ \hline [PERSON] et al. (2022) [12] & ODE-based & Spatial–temporal & Digg \\ \hline [PERSON] \& [PERSON] (2024) [2] & Reaction–diffusion & \begin{tabular}{c} Time delays, correction \\ mechanisms \\ \end{tabular} & \begin{tabular}{c} Time delays, correction \\ human spreading \\ \end{tabular} \\ \hline [PERSON] et al. (2023) & Fuzzy sign-aware & \begin{tabular}{c} Trust/distrust \\ relationships \\ \end{tabular} & Bitcoin network \\ \hline [PERSON] et al. (2024) [25] & Competing memes & Finite attention modeling & Content virality \\ \hline [PERSON] et al. (2024) [14] & SEIHR model & Attitude complexity & Rumor propagation \\ \hline [PERSON] et al. (2024) [10] & Complex social networks & \
\begin{tabular}{c} Network assortativity, \\ degree distribution \\ \end{tabular} & Financial markets \\ \hline [PERSON] et al. (2024) [22] & [PERSON] multi-information &
\begin{tabular}{c} Time-varying \\ attractiveness \\ \end{tabular} & Competitive information \\ \hline [PERSON] et al. (2025) [23] & UIU diffusion model & Network alignment & Multiplex networks \\ \hline [PERSON] et al. (2022) [24] & Fuzzy-based prediction & Multiplex interaction & Link prediction \\ \hline [PERSON] et al. (2025) [26] & Systematic review & Linguistic features & Social media content \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing information diffusion models and their limitations.  
Second, social networks feature hierarchical thread structures where discussions branch into nested conversations. This structural element creates unique pathways for information flow which are not present in simpler network models. Our Reddit dataset captures these complex thread hierarchies, with parent-child relationships between comments forming directed information pathways.
* Third, social platforms implement algorithmic content prioritization that affects information visibility. These algorithmic factors create uneven exposure patterns that influence diffusion speed and coverage. Our modeling approach accounts for these visibility effects through parameterized transmission probabilities.  
By examining both real Reddit networks and synthetic networks with controlled properties, we can isolate how these social media characteristics interact with different delay mechanisms to shape overall information coverage and spread patterns.