# Predicting Reputation in the Sharing Economy with Twitter Social Data
## 4 Prediction Model  
Our primary research question (RQ1) is if social network footprints can be used for predicting bad user behavior on sharing economy sites. We base our assumptions in the existing research about the capabilities of Social Media for inferring personal traits. To explore such a hypothesis, we have designed the following experiment: predicting the proportion of bad ratings for each user by only using social features extracted from their Twitter profiles as predictors (Figure 2c). Given the polarization of reputation systems [15], we will define a bad rating as one with a value of less than half of the maximum, in this case, one or two stars out of five. In our dataset, there are 1922 bad ratings out of a total of 149,195. We can make the following assumptions: (i) the majority of the Wallappo users are not fraudulent since reviews with low ratings are infrequent; and (ii) there is a certain probability of a transaction to go wrong. If this happens, the fault for such an unpleasant transaction can lie in one or both participants in the transaction. Based on this, we can then model the rate of reviews with low ratings in a certain user profile with a Poisson distribution where infrequent events, bad reviews, can occur with a certain probability. Such probability will be different for each user, and we are particularly interested in what are the user characteristics that relate to such probability. In fact, we want to predict such probability \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & **Features** & & \\ \hline Profile & Account creation date & & \\ Behavior & Tweets count, Favourites count, Most frequent tweeting hours, Tweets average length & Bad words ratio \\ Social & Times added to list, Avg. retweeted per tweet, Avg. favourited per tweet, Followers count, Friends count, Followers’ followers avg. count, Followers’ friends avg. count, Followers’ tweets avg. count, Friends’ friends avg. count, Friends’ tweets avg. count \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Twitter features. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Mean** & **std** & **Min** & **Max** \\ \hline Account creation date & - & - & 2006-12-26 & 2017-03-14 \\ Tweets count & 7086.63 & 16,277.20 & 0 & 556,756 \\ Bad words ratio & 0.02 & 0.02 & 0 & 0.46 \\ Times added to list & 9.76 & 42.45 & 0 & 2735 \\ Favourites count & 3066.26 & 11,826.47 & 0 & 399,054 \\ Avg. retweeted per tweet & 1397.67 & 5039.60 & 0 & 365,745.75 \\ Avg. favourited per tweet & 0.49 & 9.77 & 0 & 906.60 \\ Most frequent tweeting hours & 15.24 & 6.83 & 0 & 23 \\ Followers count & 556.66 & 5519.25 & 0 & 458,789 \\ Friends count & 476.43 & 2035.52 & 0 & 258,934 \\ Tweets average length & 88.96 & 26.75 & 0 & 179.53 \\ Followers’ followers avg. count & 12,194.23 & 30,909.87 & 0 & 1,990,020 \\ Followers’ friends avg.count & 6945.60 & 12,969.62 & 0 & 441345.57 \\ Followers’ tweets avg. count & 7120.12 & 8148.27 & 0 & 327347.40 \\ Friends’ followers avg. count & 941,730.90 & 1,921,375.52 & 0 & 105,297,483 \\ Friends’ friends avg.count & 4323.63 & 8205.70 & 0 & 477,768.33 \\ Friends’ tweets avg. count & 17,643.89 & 12,186.34 & 0 & 391,207 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Twitter features statistics. by using only a set of user characteristics that are present on Twitter, as a way of predicting user behaviors at Wallapop by only using social data. We could think about using a Poisson regression to model the count of bad ratings for each user. However, while Poisson distributions have a variance equal to the mean, in our data, the count of bad ratings suffers from overspreading: the 0.099 and the variance is 0.131. For that reason, we will model the data using the Negative Binomial distribution, which, as a gamma mixture of Poissons, can be used to model count data with overdispersion [87].