## 4 Data
### Gathering and Analyzing Social Context  
The model proposed needs to access the network of users. Since all datasets provide both tweet and user IDs, it would be possible to access Twitter's public API to retrieve the network. However, that approach has several disadvantages that stem from the fact that these datasets were originally captured circa 2010 [1], such as the fact that the relationships between users have likely changed and that many of the original tweets and users have been deleted or made private, making it impossible to fetch them. Alternatively, we decided to retrieve the follower network from a snapshot of the whole Twitter network in summer of 2009 [39]. Since the datasets used were gathered around the same time period as the snapshot, this should provide a more reliable list of followers than other methods. We refer to the the resulting network as _relations_. Upon realizing that the _relations_ network was rather sparse for the OMD and HCR datasets, we investigated an alternative to find hidden links between users: connecting users that followed similar people. To do so, we extracted the list of users followed by each author and we compared the \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Source** & **Users** & **Entries** & **Year** \\ \hline OMD [37] & Twitter & 893 & 1261 & 2009 \\ HCR [38] & Twitter & 277 & 1434 & 2011 \\ RTMind [2] & Twitter & 62 & 159 & 2013 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets used in the experiments. OMD, Obama–McCain Debate; HCR, Health Care Reform. list of followees for each pair of users in the dataset. Users that shared at least a given ratio of their followees were considered similar, and an edge between them was drawn. After evaluating different values for the threshold ratio, it was set to 15%, as it resulted in a degree similar to the RT Mind dataset. We refer to this network as _common_. To compare the two network variants, _relations_ and _common_, we used some basic statistics of each network, shown in Table 2. The table includes the average degree of each node in the network (i.e., mean number of edges per node), the ratio of users that have the same label as the majority of their neighbors in the network (majority agreement), the ratio of users that have the same label as all their neighbors (total agreement), and the ratio of users that do not have any neighbors (isolation ratio). The degree measures the density of the network. The majority and total agreement metrics are a measure of homophily in the network. The table also includes two measures of the balance in labels for user (user label ratio) and content (content label ratio). These two metrics were calculated by dividing the number of elements (i.e., users and content) with the most common label by the total number of elements. We observed that the RT Mind dataset was the most promising of all the networks, as its labels were balanced, it had high density and homophily, higher content count per user, and all of its users were connected. The OMD networks were the densest, but their agreement was very low and a fourth of its users not connected to others. Moreover, we observed that the _common_ extension of this dataset had a lower agreement ratio and fewer edges, whereas the isolation ratio remained the same as in the relations network. Lastly, the HCR dataset showed the lowest agreement of the datasets, and the relations network was almost non-existent. Although the common network significantly improved every metric, the majority agreement was still very low (0.29). This meant that the additional links were connecting users that were dissimilar, which negated the homophily assumption. In summary, we concluded that this particular strategy to extend social context did not work for these datasets. The statistics for the RT Mind dataset made it ideal for the evaluation of our proposed model. The results for the OMD dataset may indicate how the model would work in scenarios with a higher degree, but relatively low homophily.