# A Framework to Understand Attitudes towards Immigration through Twitter
## 5 Results
### Attitude Identification and Classification  
To answer our question RQ1, we performed an exhaustive search in the dataset for hashtags and words that could be strongly associated with each attitude (see Table 1 for more examples). For instance, the hashtags #bienvenidosachile (welcome to Chile) and #inmigrantesilegales (illegal immigrants) can be directly mapped to each attitude. We found more hashtags in the _empathy_ group than in the _threat_ group. When tagging threat users, we included words that are not normally associated with threats or negative comments against immigrants. For instance, \"turistas\" (_tourists_) is not a word commonly associated with a threat attitude. However, in our dataset, tourists tended to be used to express anger about people entering the country on a tourist visa and overstaying.  
In total, 3.1 K accounts were tagged in the empathy group, and 1.2 K accounts were tagged in the threat group (see Table 2). The attitude classifier presented good performance, with high precision for both attitudes (0.95 in _empathy_, 0.81 in _threat_) and high recall (0.88 in both). Such good performance is expected, due to a tagging strategy based on a perfect separation of accounts from each attitude. However, this strategy works well identifying a tendency towards attitudes because the keywords used for tagging are excluded from the learning process [17].  
Figure 4 presents the top-50 features according to their importance in classification. These features include: retweets to accounts that were be automatically tagged with each attitude, mentions to then-president [PERSON] (@mbachelet), attitude-relevant terms such as cesantia (_unemployment_), delincuentes (_criminals_), inmigracion (_immigration_), migrantes (_migrants_), and haiitanos (_Haiitanos_); and mentions to migrant-support institutions as @sjmchile (Jesuit Center for Migration). The remaining features in the top-50 include interactions with authors of viral tweets, and terms relating to specific content, such as a quote by [PERSON].  
We then used the classifier to propagate attitudes to the rest of the dataset (88% of the total dataset). Table 3 reports the resulting classification. Considering the labelling and propagation processes, a total of 71.98% user profiles were classified in _empathy_, 3.24% in _undisclosed_, and 24.78% in _threat_. This is an unexpected outcome. We expected the distribution of user profiles to be negatively biased given the social divisive nature of immigration in Chile. However, this may imply that negative users are more vocal than positive users and hence there is a perception for negative comments to dominate the public discussion around immigration issues in Chile. Table 3 reveals this is the case with the threat group generating considerably more content per user (with a ratio of 3.5 tweets per user, for the empathy attitude group, versus 6.7 tweets per user, for the threat attitude group) than the empathy group, particularly quotes and replies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attitude** & **Precision (mean)** & **Precision (std.)** & **Recall (mean)** & **Recall (std.)** & **Labelled Accounts** \\ \hline Empathy & 0.95 & 0.04 & 0.88 & 0.13 & 3118 \\ Threat & 0.81 & 0.16 & 0.88 & 0.12 & 1233 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance (precision and recall) of the user profile classifier based on 10-fold cross-validation.