Predicting social-media reactions towards refugees by incorporating geo-tagged personality data and distance matrices

During my proposed PhD, I plan to draw from two widely-used conceptual orientations (contact theory and personality theory) to better understand the nature of prejudice.

Xenophobic actions are most often observed in areas with low immigrant rates. In the literature this is explained by Intergroup Contact Theory, the phenomenon by which a lack of contact - direct or indirect - is associated with increased prejudice (Allport, 2006).

Another approach in explaining prejudice is based on personality differences. Prejudice can be accurately predicted by Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO). RWA is motivated by a heightened security-cohesion and found in people with low openness and high conscientiousness, whereas SDO is found in people who view the world as competitive and have low agreeableness (Sibley & Duckitt, 2009).

My PhD will combine both theories into one model, allowing us to calculate the importance of internal beliefs compared to actual interactions with the outgroup.

Social media data includes personality as well as geographic information about participants. Twitter hosts over 500 million tweets a day, and Facebook has half a million new comments every minute (Edwards, 2016). A fair amount of Twitter data is geo-tagged, and offers personality insights, too. According to Dr. Quercia and colleagues, e.g. over 88% of extraversion variance can be effectively predicted by analyzing Twitter users' social media behavior (Quercia, Kosinski, Stillwell & Crowcroft, 2011).

I decided to do my primary analysis in Germany, as a recent paradigm shift in its asylum politics led to an opening of clearly-located refugee centers and an influx of 1.7m refugees in 2017 alone (Federal Office for Migration and Refugees, 2018). Additionally, I grew up in Germany, allowing me to review primary literature in German and communicate with the involved political parties.

The basic idea is to check if distance between authors of biased tweets and the next refugee center is predictive of the sentiment of the tweets. Additionally, personality will be passively inferred by following the procedure outlined by Quercia and colleagues (Quercia et al., 2011)

I will collate Twitter profiles and posts featuring refugees and relevant terms in an automated manner. This large sample size limits manual categorization, and therefore semantic analysis will be used to classify tweets as positive or negative. A distance matrix will be created in order to calculate the distance between the refugee centers and the location of the tweet authors. The general hypothesis is that people living closer to refugee centers will have had contact with refugees at a higher rate, and thus tweet less negatively about refugees than previously.

Personality information will be collected by integrating large-scale surveys available to Dr. Rentfrow with passively assessing personality profiles by analyzing the Twitter author's profiles.

Both sets of information will be combined in a unified model, predicting the likelihood of a tweet being positive versus negative. The distance matrix, as well as the big-5 personality variables, will be included as explanatory variables. The resulting model will be the first to combine Intergroup Contact Theory with the Dual Process Model of Ideology - the two most prominent theories explaining prejudice.

A combined model improves our understanding of both theories and allows us to calculate the importance of internal beliefs compared to actual interactions with the outgroup. Furthermore, this innovative model enables us to check for interactions. This new model paves the way for alternative hypotheses. For instance, contact in general reduces prejudice but this may be different for people with high versus low openness. In this scenario, low openness will likely be associated with reduced chances of the contact having an effect on lowering prej