Understanding the Influence of Politicians' Financial Interests using Natural Language Processing and Network Analysis

使用自然语言处理和网络分析了解政客经济利益的影响

• 批准号: 2726775
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2726775
• 关键词: Understanding; Influence; Politicians; Financial; Interests

This project aims to examine the influence of financial interests on the actions of politicians. My central hypothesis is that politicians with significant interests in certain industries will support legislation that would be beneficial for these industries. Research questions I intend to explore include: Can we predict political decisions based on the financial interests of Members of Parliament (MPs)? How do politicians' interests affect their publicly voiced opinions? And to what extent do lobbyists and corporations benefit from their financial relationships with legislators? Whilst the UK ranks among the world's most open governments, McKay and Wozniak (2020) argue that the searchability and overall usability of published UK lobbying data can be categorized as low. This project will leverage recent advances in natural language processing (NLP) to convert unstructured textual information into a quantitative dataset regarding the financial interests of UK MPs. I will then use this new dataset-which will be made public-to assess the above topics and other important questions about the fairness and representativeness of outside interests' influence over the Members and policies of Parliament.The project combines two distinct disciplines-political science and computer science. My proposed supervision team brings together an expert on lobbying, Prof Amy McKay (Politics), an expert on data science and network analysis, Prof Hywel Williams (Computer Science), and an expert on NLP as applied to politics, Dr Travis Coan (Politics and Q-Step), to help me to develop and apply a robust method for systematically evaluating lobbying and financial interests.Despite increasing masses of information regarding potential threats to unbiased, representative policymaking, few scholars have leveraged NLP to evaluate the influence of financial interests over policymaking and public discourse. Exceptions are Kluver (2009), who demonstrates that a tool known as Wordfish (Slapin 2008) can be used to locate lobby groups on a policy spectrum, and Boussalis and Coan (2016), who use text-mining to evaluate signals from conservative political groups and determine that promulgation of climate scepticism is increasing, not decreasing. The potential of NLP to contribute meaningfully to the contradictory literature on the influence of financial interests in policymaking is therefore considerable.The proposed research would involve an application of Language Representation Models (e.g. the BERT architecture developed by Devlin et al. 2018) to UK parliamentary records, such as the Register of Members' Financial Interests, to generate structured data that can be quantitatively evaluated.The resulting dataset of could be combined with other data, such as Tweets, to explore the effect of these interests. Further extensions could use network analysis to investigate relationships between politicians and companies (Porter et al. 2009). A bipartite network linking MPs and corporations could be developed to analyse policymaking communities in the UK, as has been done in the US (Porter et al. 2007; Ward et al. 2011).To help answer the research questions I have identified various sources: - Register of Members' Financial Interests: This register contains complex unstructured data from Parliament regarding payments made to MPs. The interests are categorized in ten topics including employment, donations and shareholdings. This will serve as my primary dataset.- Votes in Parliament- Cabinet Ministers' Meetings, Hospitality, Gifts and Overseas Travel- The Electoral Commission's database of donations, election spending and party accounts- Listing of Publishable Central Government Tender- Twitter API / Parliament HansardI will secure approval by the College ethics committee for this project. Generated data should be validated before publication.

该项目旨在研究经济利益对政治家行为的影响。我的中心假设是，在某些行业拥有重大利益的政治家将支持有利于这些行业的立法。我打算探讨的研究问题包括：我们能否根据国会议员（MP）的经济利益来预测政治决策？政客的利益如何影响他们公开表达的观点？游说者和企业在多大程度上从与立法者的财务关系中受益？尽管英国跻身世界上最开放的政府之列，但 McKay 和 Wozniak（2020）认为，已发布的英国游说数据的可搜索性和整体可用性可以归类为较低。该项目将利用自然语言处理 (NLP) 的最新进展，将非结构化文本信息转换为有关英国议员经济利益的定量数据集。然后，我将使用这个将公开的新数据集来评估上述主题以及有关外部利益对议会议员和政策影响的公平性和代表性的其他重要问题。该项目结合了两个不同的学科——政治学和计算机科学。我提议的监督团队汇集了游说专家 Amy McKay 教授（政治学）、数据科学和网络分析专家 Hywel Williams 教授（计算机科学）以及 NLP 应用于政治的专家 Travis Coan 博士（政治学）和 Q-Step），帮助我开发和应用一种稳健的方法来系统地评估游说和经济利益。尽管关于公正、代表性决策的潜在威胁的信息越来越多，但很少有学者利用 NLP评估经济利益对政策制定和公共话语的影响。例外的是 Kluver (2009)，他证明了一种名为 Wordfish (Slapin 2008) 的工具可用于在政策范围内定位游说团体，以及 Boussalis 和 Coan (2016)，他们使用文本挖掘来评估来自保守派政治的信号团体并确定气候怀疑论的传播正在增加，而不是减少。因此，NLP 对经济利益对政策制定影响的矛盾文献做出有意义的贡献的潜力是相当大的。拟议的研究将涉及将语言表示模型（例如 Devlin 等人 2018 年开发的 BERT 架构）应用于英国议会记录，例如会员的财务利益登记册，以生成可以定量评估的结构化数据。生成的数据集可以与其他数据（例如推文）结合起来，以探索这些利益的影响。进一步的扩展可以使用网络分析来调查政治家和公司之间的关系（Porter et al. 2009）。可以开发一个连接议员和企业的双向网络来分析英国的政策制定群体，就像美国所做的那样（Porter 等人，2007 年；Ward 等人，2011 年）。为了帮助回答研究问题，我确定了各种来源： - 议员经济利益登记册：该登记册包含议会提供的有关向国会议员付款的复杂非结构化数据。这些兴趣分为就业、捐赠和持股等十个主题。这将作为我的主要数据集。- 议会投票- 内阁部长会议、接待、礼品和海外旅行- 选举委员会的捐款、选举支出和政党账户数据库- 可发布的中央政府招标清单- Twitter API / 议会 HansardI将获得学院伦理委员会对该项目的批准。生成的数据应在发布前进行验证。