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

通过结合地理标记的个性数据和距离矩阵来预测社交媒体对难民的反应

• 批准号: 2268754
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2268754
• 关键词: Predicting; social; media; reactions; towards

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

在攻读博士学位期间，我计划从两个广泛使用的概念取向（接触理论和人格理论）中汲取经验，以更好地理解偏见的本质。仇外行动最常见于移民率低的地区。在文献中，这是由群体间接触理论解释的，这种现象表明缺乏直接或间接的接触与偏见增加有关（Allport，2006）。右翼权威主义（RWA）和社会支配取向（SDO）可以准确预测恐惧。RWA的动机是高度的安全-凝聚力，并且存在于低开放性和高开放性的人群中，而SDO则存在于将世界视为竞争性和低宜人性的人群中（Sibley & Duckitt，2009）.我的博士学位将联合收割机将两种理论结合成一个模型，这让我们可以计算出内部信念与外部群体实际互动的重要性。社交媒体数据包括个性，以及参与者的地理信息。Twitter每天发布超过5亿条推文，Facebook每分钟有50万条新评论（Edwards，2016）。相当数量的Twitter数据是地理标记的，也提供了个性见解。根据Quercia博士及其同事的研究，例如，通过分析Twitter用户的社交媒体行为，可以有效预测超过88%的外向性差异（Quercia，Kosinski，Stillwell & Crowcroft，2011）.我决定在德国进行初步分析，由于最近庇护政策的范式转变导致开放了位置明确的难民中心，仅在2017年就有170万难民涌入（联邦移民和难民办公室，2018年）。此外，我在德国长大，这使我能够用德语回顾主要文献，并与相关政党进行交流。基本的想法是检查有偏见的推文的作者与下一个难民中心之间的距离是否可以预测推文的情绪。此外，人格将通过遵循Quercia及其同事概述的程序被动地推断（Quercia等人，2011年）我将以自动化的方式整理Twitter上关于难民和相关术语的个人资料和帖子。这个大样本量限制了人工分类，因此语义分析将用于将推文分类为积极或消极。将创建一个距离矩阵，以计算难民中心和推文作者所在地之间的距离。一般的假设是，居住在难民中心附近的人与难民接触的频率更高，因此与以前相比，对难民的负面推文较少。性格信息将通过整合Rentfrow博士可用的大规模调查和通过分析Twitter作者的个人资料来被动评估性格概况来收集。这两组信息将在一个统一的模型中结合起来，预测一条推是正面还是负面的可能性。距离矩阵以及五大人格变量将作为解释变量。由此产生的模型将是第一个将联合收割机群体间接触理论与意识形态的双重过程模型-两个最突出的理论解释偏见。一个组合模型提高了我们对这两个理论的理解，并允许我们计算内部信念的重要性相比，实际与外群体的互动。此外，这种创新的模型使我们能够检查相互作用。这个新模型为替代假设铺平了道路。例如，接触通常会减少偏见，但这对于高开放性和低开放性的人来说可能是不同的。在这种情况下，低开放性可能与接触对降低prej有影响的机会减少相关联。