# A Framework to Understand Attitudes towards Immigration through Twitter
## 6 Discussion  
### Key Results  
Social media is a new, dynamic and open space to express opinions and feelings, collect data and better understand public perception of immigration and offers the opportunity to overcome key limitations of traditional data sources. Social media offers the potential to monitor online public opinion about migration in near-real time at unprecedented spatial and temporal granularity, and understand the actual extent of empathy or negativity in these opinions in raw format. To unlock these potentials, we developed a novel, reproducible and open framework to measure and monitor changes in immigration sentiment. Particularly, the proposed framework enables the classification of users' stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users' content and differences in engagement and content generation at a daily temporal scale.  
We presented evidence of the composition of the Chilean immigration sentiment network on Twitter. We found that a larger share (72%) of the user network displays positive and empathetic sentiment towards immigration, and that the proportion of the network associated with negative immigration attitudes was small (25%). However, we also found that users displaying negative anti-immigration profiles tended to produce content at a significantly greater rate, producing up to 50% more content per user than  
Figure 9: Daily difference in the number of tweets between _empathy_ and _threat_ groups. **Top**; our model, and **bottom**; the null model.  
users displaying positive immigration profiles. We find interesting the apparent contrast between public surveys held in Chile and our findings: reportedly, a majority of Chilean nationals agree with the following statements: \"the country should take more drastic measures to exclude illegal immigrants.\" However, the survey is measuring something different: agreement with a statement regarding a specific group of people rather than the expression of an attitude toward a phenomenon. Furthermore, the population under study is different, as the survey samples the population aiming at national representativeness. Thus, a future line of work should be the measurement of representativeness of the Twitter population, and the definition of a methodology to compare survey results with results from our work.  
We present a characterisation of the feature and psycholinguistic content of positive and negative immigration user profiles. Users with a pro-immigration profile often use neutral terms recognising migration as a process, and emotional content relating to social, positive emotions, communication, school, humans and family. By contrast, users with an anti-immigration profile tend to use terms denoting a difference between immigrants (_them_) and the native population (_us_), and emotional content relating to motion, other people, present, negative emotions, death, inhibition, anger, money, anxiety and work. We also found evidence of strong alignment between users with anti-immigration views and conservative political ideologies. Our temporal analysis also revealed pronounced peaks in daily user engagement and content generation activity in response to key migration-related events, particularly events featured in news media.