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变量（社交媒体回声室）如何与人类行为（极端主义）相互作用。虽然观察性研究可用于推断这两个参数之间的关联，但在控制其他因素的同时，很难隔离和概括回声室本身的影响。因此，本研究拟设计一个线上实验室实验，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。然后，我将对回声室对政治极端主义的潜在影响提出尖锐的假设。最后，我将在一个实验环境中测试这些假设。虽然将有几种方法来操作社会媒体回音室的概念，其中一种方法是模拟研究参与者在一个封闭的系统（新闻源）中接触到意识形态同质的媒体信息的情况。为了开发这种意识形态同质的新闻提要，我将首先从目标人群中选择一个Facebook用户样本，并使用离散的行为数据（如公开表达的偏好）推断他们的政治取向，因为它被认为比自我报告的数据更可靠，偏见更少（Bond和Messing，2015）。这应该导致所述用户的政治取向的全面拓扑结构，从非常自由到非常保守。良好的抽样框架对于最大限度地减少抽样误差至关重要（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：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（Digital Footprints）进行，这是一种允许研究人员收集私人Facebook数据的软件（用户数据、帖子和新闻源）通常无法使用Facebook的API获得，并得到用户的明确同意9。在提供无与伦比的访问用户的私人信息和平台上的实际行为的同时，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，样本可能不代表目标人群（Neuman，2014：259）。然而，这可以通过使用从公开的Facebook个人资料中收集的公共数据来补充数据集来解决。此外，Facebook的API不仅不如实时数据源稳定，而且其结构的文档仍然很少。因此，当在数据收集中回溯时，可能很难辨别数据模式是否由于用户界面或API结构本身的变化而发生（Bechmann and Vahlstrup，2015）。借鉴Facebook科学家（Bakshy，Messing和Adamic，2015）进行的一项研究中采用的方法，该研究着眼于社交网络用户的一部分如何对他们的提要中出现的新闻做出反应，然后我将根据他们在特定问题上的意识形态对齐对新闻文章进行分类。这将涉及跟踪哪些文章被每个类别的用户分享最多，并计算每个链接的政治“对齐分数”（同上）。使用该分数，链接将被分组为意识形态类别，并按主题进行排序。在这里，文本分类方法，如基于N-gram的文本分类将是有用的。为了使自己具备所需的技术技能，以正式确定这一研究设计，我计划审计OII的社会网络研究生课程的实验方法和可持续研究数据。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.