# Predicting Reputation in the Sharing Economy with Twitter Social Data
## 4 Prediction Model
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Coef** & **Std Err** & \(\mathbf{P}>|\mathbf{z}|\) \\ \hline
**Intercept** & \(-7.8458\) & 0.657 & **<0.001** \\
**Account creation date** & \(2.957\times 10^{-9}\) & \(4.77\times 10^{-10}\) & **<0.001** \\ Tweets count & \(1.867\times 10^{-7}\) & \(2.2\times 10^{-6}\) & 0.932 \\
**Bad words ratio** & \(2.8823\) & \(1.246\) & **0.021** \\ Times added to list & \(-0.0013\) & \(0.001\) & 0.350 \\ Favourites count & \(8.7\times 10^{-7}\) & \(2.59\times 10^{-6}\) & 0.737 \\ Avg. retweeted per tweet & \(-4.387\times 10^{-6}\) & \(7.57\times 10^{-6}\) & 0.562 \\ Avg. favourited per tweet & \(0.0037\) & \(0.002\) & 0.056 \\ Most frequent tweeting hours & \(0.0052\) & \(0.005\) & 0.259 \\ Followers count & \(-4.28\times 10^{-5}\) & \(3.63\)e-05 & 0.238 \\ Friends count & \(6.774\times 10^{-5}\) & \(3.84\times 10^{-5}\) & 0.078 \\
**Tweets average length** & \(-0.0078\) & \(0.001\) & **<0.001** \\ Followers’ followers avg. count & \(-1.384\times 10^{-6}\) & \(2.21\times 10^{-6}\) & 0.531 \\
**Followers’ friends avg. count** & \(8.631\times 10^{-6}\) & \(3.86\times 10^{-6}\) & **0.025** \\ Followers’ tweets avg. count & \(-4.663\times 10^{-6}\) & \(4.21\times 10^{-6}\) & 0.268 \\
**Friends’ followers avg. count** & \(4.773\times 10^{-8}\) & \(1.16\times 10^{-8}\) & **<0.001** \\ Friends’ friends avg.count & \(1.236\times 10^{-6}\) & \(3.46\times 10^{-6}\) & 0.721 \\ Friends’ tweets avg. count & \(3.512\times 10^{-6}\) & \(2.23\times 10^{-6}\) & 0.115 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GLM regression summary. Predictors statistically significant at the 5% level are shown in bold. used in risk assessment to evaluate the performance of the models as a simple and intuitive method for evaluating count data. They are built similarly to Lorenz curves: first, the samples are sorted by the predicted target variable on the \(x\)-axis, with higher values on the left and lower values on the right. In this case, users with a higher predicted probability of receiving a bad rating are on the left of the graph. The \(y\)-axis represents the amount of the _accumulative percentage of the target variable_, in this case, the percentage of all bad ratings. For each user percentile, the cumulative percentage of bad reviews for those users is plotted, resulting in a curve that will be better the bigger the area under it. The 45-degree line is the line of the _random model_, which does not have any predictive power at the task of identifying users with a higher probability of getting a bad rating. For example, when the random model is used to sort the users, 50% of users will have 50% of all the bad ratings. The top line is the _perfect model_, one that gives the higher probability of a bad rating to the ones that end up receiving the bad reviews. Therefore, the line will increase very steeply at first until 100% of the target variable is quickly reached [93]. A metric that summarizes the performance of the model is the AUC, also known as the lift index, understood as the area between the model gain curve and the random model curve. After training and obtaining the output of the model with a 10 fold cross-validation strategy, we generated the Gains Curve displayed in Figure 4. The AUC is 9.83%, which proves that the model has a certain level of predictive power at the task of separating users by their probability of receiving a bad rating: a random model would have an AUC very close to 0%, and a model worse than a random model would have a negative AUC. While the insurance industry is very opaque about their risk assessment models' performance, there are some examples in the literature of models from which we can compare with, for example at the insurance [94] (19,9% AUC) or marketing [95] industries (from 15.6% to 28.9% AUC). As shown in Figure 4, a Random Forest Regressor (RFR) model is included as a baseline with an AUC of 1.52%.