Matlab implementation of the RSC-Model which is described in the following paper:
RSC: Mining and Modeling Temporal Activity in Social Media
Alceu Ferraz Costa, Yuto Yamaguchi, Agma Juci Machado Traina, Caetano Traina Jr., and Christos Faloutsos
The 21st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015
Using RSC to generate 10,000 synthetic time-stamps:
> addpath(genpath('.')); % Add sub-folders to Matlab path.
> [ fGen, ~, paramGuess ] = rsc_model();
> Tsynth = fGen(paramGuess, 1e5);
We can use the plot_iat_hist function to plot the log-binned histogram of the synthetic inter-arrival times (IAT):
> plot_iat_hist(Tsynth);
The result should be similar to the following figure:
Instead of using the default parameters paramGuess returned by the 'rsc_model' function we can estimate (fit) the parameters using real data. The function load_reddit_data loads sample data from Reddit users:
> Tcell = load_reddit_data();
The function fit_model is used to estimate the parameters. We are using the RSC default parameters as a starting point for the fit algorithm:
> paramEst = fit_model(Tcell, @rsc_model, 'paramGuess', paramGuess);
The function plot_iat_hist_fit compares the log-binned histogram for real data against synthetic time-stamps. If both histograms are similar, then the RSC fit was successful:
> timeStampTotal = numel(cell2mat(Tcell));
> Tsynth = fGen(paramEst, timeStampTotal);
> plot_iat_hist_fit(Tcell, Tsynth);
The result should be similar to the following figure:
The sample dataset of Reddit users has some users that are bots. We can use the load_reddit_data function to get a grouping variable that tells whether the i-th entry of Tcell is a bot or a human:
> [ Tcell, ~, ~, ~, ~, userType] = load_reddit_data();
userType(idx) == 1 indicates that the time-stamp sequence in Tcell{idx} is from a bot. The function estimate_bot_likelihood returns a vector L where each entry L(idx) corresponds to the likelihood (i.e. the score) that the time-stamp sequence Tcell(idx) is from a bot:
% Split data into train and test subsets.
> CrossValIdxs = my_crossvalind('Kfold', userType, 2);
> TcellTest = Tcell(CrossValIdxs == 1);
> TcellTrain = Tcell(CrossValIdxs == 2);
> userTypeTrain = userType(CrossValIdxs == 2);
> [Ltest, Ltrain] = estimate_bot_likelihood(TcellTest, TcellTrain, userTypeTrain);
In order to classify users as bots or humans, we use a cost-sensitive approach in our paper. Assuming the costs FpCost = 10 and FnCost = 1 for false-positive (FP) and false-negative errors (FN), we can detect bot using the likelihood_thresh function as follows:
> FpCost = 10; FnCost = 1;
> Lthresh = likelihood_thresh(Ltrain, userTypeTrain, FpCost, FnCost);
> IsBot = LtrainL > Lthresh;
We can also use the print_conf_matrix function to print the confusion matrix:
> TP = sum(userTypeTrain == 1 & IsBot == 1);
> FP = sum(userTypeTrain == 0 & IsBot == 1);
> TN = sum(userTypeTrain == 0 & IsBot == 0);
> FN = sum(userTypeTrain == 1 & IsBot == 0);
> print_conf_matrix(TP, FP, TN, FN);
Predicted Class
.---------.--------.
| Pos. | Neg. |
.-------|---------|--------|
Actual | Pos. | 9 | 5 |
Class | Neg. | 1 | 498 |
`--------------------------
This repository includes a sample dataset with time-stamps of 1,036 Reddit
users in the sample_data/reddit/ directory.
We also include below links to download the complete datasets used in our paper. Each dataset has a README.md file with a more detailed description of the data.
- Reddit Dataset (20 Million time-stamps from 96,199 users);
- Twitter Dataset (16 Million time-stamps from 6,790 users);
This material is based upon work supported by FAPESP, CNPq, CAPES, STIC-AmSud, the RESCUER project funded by the European Commission (Grant: 614154) and by the CNPq/MCTI (Grant: 490084/2013-3), JSPS KAKENHI, Grant-in-Aid for JSPS Fellows #242322, the National Science Foundation under Grant No. CNS-1314632, IIS-1408924, ARO/DARPA under Contract Number W911NF-11-C-0088 and by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053.