Feeling all the (Partisan) Feels: Exploring the Drivers of Affective Polarization at the Individual Level

这项研究的目的是捕捉一个特定的兴趣变量(社交媒体回音室)如何与人类行为(极端主义)相互作用。虽然观察性研究可以用来推断这两个参数之间的联系，但这将使我们很难分离和概括回音室本身的影响，同时控制其他因素。因此，我建议设计一个在线实验室实验。研究将分三个阶段进行：1.首先，我需要将解释变量和响应变量定义为可测量的因素，从而使其具有可操作性。然后，我将就回音室对政治极端的潜在影响提出尖锐的假设。最后，我将在实验环境中测试这些假设。虽然有几种方法可以将社交媒体回音室的概念付诸实施，但一种方法是模拟研究参与者在封闭系统(新闻馈送)中接触到意识形态上相同的媒体信息的情景。为了开发这种意识形态上的同质新闻馈送，我将首先从目标人群中选择一组Facebook8用户样本，并使用离散的行为数据(如公开表达的偏好)来推断他们的政治倾向，因为这些数据被认为比自我报告的数据更稳健、更少偏见(邦德和梅辛，2015)。这应该会导致上述用户的政治倾向的全面拓扑，从非常自由的到非常保守的。一个好的采样框架对于最小化采样误差至关重要(Neuman，2014：252)。然后，我将提取样本中包含的用户在指定时间段内共享的媒体内容的数据(例如：2016年美国总统大选等政治事件)。为了规避数据隐私问题，在这两种情况下，数据收集过程都将使用数字足迹-一种软件，研究人员可以在明确用户同意的情况下使用Facebook的API收集他们通常无法使用的Facebook私人数据(用户数据、帖子和新闻提要)9。在提供无与伦比的访问用户私人信息和平台实际行为的同时，这种收集技术防止了重要的限制。由于参与数据收集过程的自愿性质，样本可能不能代表目标人群(Neuman，2014：259)。然而，这可以通过用从公开可获得的Facebook个人资料中收集的公共数据来补充数据集来补救。此外，Facebook的API不仅不如实时数据馈送稳定，而且关于其结构的文档仍然很少。因此，当回顾数据收集时，可能很难区分数据模式是由于用户界面的变化还是由于API结构本身的变化而发生的(Bechmann和Vahlstrup，2015)。我借鉴了Facebook科学家(巴克西、梅辛和阿达米奇，2015)进行的一项研究所采用的方法，该研究考察了该社交网络的一部分用户对其提要中出现的新闻的反应，然后我将根据他们在特定问题上的意识形态一致性对新闻文章进行分类。这将涉及跟踪每一类用户分享最多的文章，并计算每个链接的政治“对齐分数”(同上)。使用这个分数，链接将被分组到意识形态类别中，并根据主题问题进行排序。这里，诸如基于N元语法的文本分类之类的文本分类方法将是有用的。为了使自己具备使这项研究设计正式化所需的技术技能，我计划对OII社交网络研究生课程中的实验方法和访问研究数据进行审计。研究的第二阶段将是测试这种媒体刺激对态度极端主义的影响。

The aim of this research is to capture how a particular variable of interest (social media echochamber) interacts with human behavior (extremism). While an observational study may be used to infer association between these two parameters, it would make it difficult to isolate and generalise the impact of echo chambers themselves, while controlling for other factors. Therefore, I propose instead to design an online laboratory experiment.The research will proceed in three stages:1. I will first need to operationalise both explanatory and response variables by defining them into measurable factors.2. I will then formulate sharp hypotheses on the potential effects of echo chambers on political extremism.3. Finally, I will test these hypotheses in an experimental setting.While there will be several ways to operationalise the concept of social media echo chamber, one way would be to simulate a situation in study participants are exposed to ideologically homogenous media messages inside a closed system (news feed). To develop such ideologically homogenous news feeds, I will first select a sample of Facebook8 users from a target population and infer their political orientations using discrete behavioral data such as publicly expressed preferences, as it is thought more robust and less biased then self-reported data (Bond and Messing, 2015). This should lead to a comprehensive topology of said users' political orientations, ranging from very liberal to very conservative. A good sampling frame will be crucial to minimise sampling error (Neuman, 2014: 252). I will then extract data on media content shared by users included in the sample over a specified period of time (e.g: a political event such as the 2016 U.S. presidential election).To circumvent data privacy issues, in both cases the data collection process will be carried out using Digital Footprints - a software that lets researchers collect private Facebook data (user data, posts and news feeds) normally unavailable to them using the Facebook's API, with explicit user consent9. While providing unrivalled access to users' private information and actual behaviour on the platform, this collection technique prevents important limitations. Due to the voluntary nature of participation in the data collection process, the sample may be non-representative of the target population (Neuman, 2014: 259). However, this could be remedied by supplementing the dataset with public data gleaned from publicly available Facebook profiles. Moreover, Facebook's API is not only less stable than live data feed, but there is still little documentation of its structure.When going back in time in data collection, it might thus be difficult to discern if data patterns occur due to changes in the user interface or in the API structure itself (Bechmann and Vahlstrup, 2015). Drawing on the methodology employed in a study conducted by Facebook scientists (Bakshy, Messing and Adamic, 2015), which looked at how a subset of the social network's users reacted to the news appearing in their feeds, I will then classify news articles according to their ideological alignment on specific issues. This will involve tracking which articles were most shared by each category of users and calculate a political "alignment score" for each link (ibid.) Using that score, links will be grouped into ideological categories and sorted by topical issues. Here, text classification methods such as N-gram based text categorisation will be useful. To equip myself with the required technical skills to formalise this research design, I plan to audit both the Experimental Approaches and Accessing Research Data from the Social Web graduate courses at the OII. The second stage of the research will be to test the effect of this media stimulus on attitude extremism.