New Frontiers in Migration Issues: A Data Science Perspective
Sunday, Jan. 3, 2021 3:45 PM - 5:45 PM (EST)
- Chair: Thierry Warin, HEC Montréal
Immigration-Related Discourses: Using Unstructured Data to Understand Perceptions of Facts and Their Evolution
AbstractIncreasingly, there are speeches in the public space that seem to ignore the facts. A parallel reality is created where opinions are reinforced by the sharing of opinions with like-minded individuals or groups. We seem to avoid the confrontation of contradictory ideas. Yet the confrontation of ideas is essential to build a common understanding of a given situation. This understanding is essential for policy makers. Any public policy or measure must be understood by citizens and businesses to be accepted. If the perception of the facts is wrong, it will be difficult for the citizen to understand why a proposed policy is adequate. The paper aims to uncover the influences behind a group's perceptions when those perceptions seem disconnected from reality or facts. The paper provides a better understanding of perceptions that are created within subgroups. In addition, the paper seek to understand the evolution of these perceptions over time, analyzing their genesis, patterns, influences, and sources behind these perceptions. Immigration is used as a topic to understand how discourse is created, how conversation evolves, and how facts or actions influence perceptions. There have been several events that have shaped discussions about immigration and its impact. There have also been several parallel discussions on immigration, making it a rich topic of study.
An NLP Perspective on the Refugee Crisis in Europe: The (Weak) Connection Between Political Media and Twitter Activity
AbstractSocial networks have started to be the subject of a lot of studies from social scientists. The fact that millions of people write, share and comment is interesting already in itself. Indeed, writing, sharing and commenting are the three essential elements of a conversation. As such, a conversation provides some interesting information about people's feelings, attitudes and behavior. The main rationale behind analyses based on social media and Twitter relies on "the wisdom of the crowds" effect. The assumption is that the aggregated judgment of several people is often better than the judgement of experts or the smartest forecaster (Hogarth 1978). In this case study, we attempt to map the conversations on Twitter about the European refugee crisis. Not only the data (content of the tweets), but also the metadata are interesting. Indeed, the content allows us to do a sentiment analysis. We can thus map positive and negative comments about the refugees. With the metadata, we can for instance map where the tweets originate based on their latitude and longitude. We can thus add a spatial dimension to the conversations. We also join a set of different attributes to the data and metadata of the tweets, such as the number of refugees in a country and the routes from their origin country to Europe.
Regional Migration in China: A Machine Learning Approach to the Hukou System
AbstractWe use the latest RUMIC survey with socioeconomic indicators, such as education, income, ethnicity, and hukou registration. The RUMiC survey also includes data on health indicators and outcomes. Based on this survey, we propose a Machine Learning protocol to extract a causal model highlighting the relevant and significant features, and their sequencing, in the explanation of health outcomes for regional migrants in China.
London School of Economics and Australian National University
Victoria University of Wellington
Ss. Cyril and Methodius University
- F2 - International Factor Movements and International Business
- R2 - Household Analysis