This project aims to measure and better understand attitudes towards immigration in Chile using Twitter data. The research team runs AI-enabled analyses on the publicly available data, categorising hashtags, phrases and URLs into positive and negative attitudes, which then are further computed to gain insights on the specific sentiments and connotations of the posts, such as empathy, threat, or anxiety. Finally, the results are embedded in a network analysis, showing the spatial and temporal relationships of the immigration-related statements on Twitter.
The key findings indicate that negative attitudes emerge from a reduced number of users, and they intensify through negative immigrant news, underlining arguments of job competition and stricter immigration regulation. Positive attitudes are expressed by a more diffused number of users and are predominantly expressed to emphasize support during specific migration-related events, such as natural disasters or violent conflict leading to an increase of migration numbers. Overall, this innovative data approach lifts research on public attitudes toward migration out of the predominant domain of qualitative research by developing a way to use Twitter data in an accurate and timely manner. The approach can be replicated for other contexts as well to better understand public immigration sentiments and can be particularly informative for regions with high Twitter usage.
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