# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 2 Related Work  
In Table 1 we report a summary, in chronological order, of a sample of previous work about information propagation on Twitter. Table 1 is composed of 14 columns, and each row represents a previous study, reported in the column **Study**. The second column named **Target** describes the paper scope with the acronyms reported in the table footnotes; then the columns **Dataset**, **Dataset Size** and **Dataset Collection Date** describe the datasets used. The following eight columns refer to a series of features which have been evaluated (X) or not (-) in the corresponding work. Finally, the column **Method** reports the acronym of the model proposed by the paper in the respective row. As Table 1 shows, the majority of works has focused on predicting retweet engagement (REP) in terms of total number of retweets. Other studies conducted a joint analysis between tweet information cascade (TIC) and REP, assuming that the retweet chains can be deducted from tweet content. \begin{table} \begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Target***} & \multirow{2}{*}{**Dataset***} & \multirow{2}{*}{**Dataset***} & \multirow{2}{*}{**Dataset***} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Table**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Users**} & \multirow{2}{*}{**Social**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Users**} \\  & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\ \[PERSON] et al. [12] & REP & YT, D & YT, K- & 2007-2008 & - & - & X & - & - & - & & & LR \\ [PERSON] et al. [13] & TIC-REP & TW & - & X & X & X & - & - & - & - & X & X & F+EM \\ [PERSON] [14] & TIC & TW & 33K & 2012 & - & - & - & - & - & - & - & X & FCM \\ [PERSON] et al. [7] & RB & TW & 54M U & 2006-2009 & - & X & X & - & - & - & - & X & SVM, NB \\ [PERSON] et al. [15] & RB & TW & 22M T & 2009 & X & - & X & - & - & - & - & - & CHR \\ [PERSON] et al. [16] & TIC & TW & 362M T & 2011 & - & - & - & - & - & - & X & - & PI \\ [PERSON] et al. [17] & TIC-REP & TW & 52 & - & - & - & X & - & - & - & - & HB \\ [PERSON] et al. [11] & TIC & TW & 11M T & 2012 & - & - & X & - & - & - & X & - & - & H \\ [PERSON] et al. [18] & TIC & TW & 55K & - & - & X & - & - & - & - & X & - & [PERSON] \\ [PERSON] et al. [19] & TIC-REP & TW & 320M U & 2011 & - & - & X & X & - & - & - & X & NEWER \\ [PERSON] et al. [20] & REP & N & 32B & 2011 & - & - & X & - & - & - & - & - & SPEMMC \\ [PERSON] et al. [21] & TIC-REP & SW & 164 & - & - & - & X & - & - & - & - & RPP \\ [PERSON] et al. [22] & TIC-REP & TW & 166K & 2011 & - & - & X & - & - & - & - & TDeH \\ [PERSON] et al. [23] & TIC & TW & 17K & 2013 & - & X & - & - & X & X & - & GcdMove \\ [PERSON] et al. [24] & REP & SW, FC & 33K & & & & - & - & X & - & - & - & DH \\ [PERSON] et al. [25] & TIC-RB & SW & 69M U & 2013-2014 & X & - & X & - & X & - & X & BN \\ [PERSON] et al. [26] & TW & 35K & 2014-2016 & X & - & - & - & - & - & - & BPM \\ [PERSON] et al. [27] & TIC-KR & TW & 8M T & 2015-2018 & - & - & - & - & - & - & - & SmartInf \\ [PERSON] et al. [28] & REP & TW & 20K & 2016 & - & X & - & - & - & - & - & NPP \\ [PERSON] et al. [29] & TIC & TW & 30K TW, 2016 & - & - & X & - & - & - & - & - & ANN \\ [PERSON] et al. [30] & TIC & AM & 35K AM & 1996-2000 & - & - & X & - & - & - & - & - & EP+H \\ [PERSON] et al. [31] & KR-TIC & SW & 50K M & - & - & - & X & - & X & - & - & RL2R \\ in **This work** & TIC & TW & 16K & 2020 & - - & - & X & \(\chi\) & X & - & - & - & W-KM \\ \hline \hline \end{tabular}   
* *For instance, [13; 15] derived the retweet propagation paths assuming that the user's \"ScreenName\" reported in the text, such as \"_RT@ user ScreenName_\" indicates the user whose message the current user has read. However, as argued by [16], this assumption is mostly inaccurate.