Micro-Influencers as Emerging Digital Opinion Leaders in Local Geographies

微影响者作为新兴的数字意见领袖在当地的地理位置：一个定量的方法来理解微影响者在Twitter上的背景下的作用在当今世界，不可否认的是，社交媒体在塑造社会，文化和经济方面发挥着至关重要的作用。尽管对社交媒体的研究越来越多，但我们对其影响以及如何有效地研究所产生的大量数据仍然知之甚少。因此，社会科学研究必须增加对社交媒体平台影响的理解，并设计新的方法来利用社交媒体数据来解决当代社会问题。该项目有助于增加关于微影响者的文献，并将提供第一个研究英国微影响者影响的例子。由于最近针对社交机器人的行动，对微影响者的研究尤为重要，因为微影响者可能会变得更有影响力。此外，这些发现可以为社交媒体舆论形成和互动的理论做出贡献，因为它将有助于理解社交关系是如何通过在线和离线互动共同创造的。该项目还旨在开发一个先进的定量方法框架，以分析社交媒体数据。特别是，机器学习，大数据方法和统计模型的集合，以识别和分析微观影响者，并结合二级数据来分析其影响力。因此，该方法将有助于改进在线和离线网络之间的自反关系的建模方法。它还提供了一个机会，发展我自己的研究能力，在处理和分析新兴的社交媒体数据，这可以通过在该领域的研究生涯进一步进展。最后，这些发现可以增加越来越多的关于社交媒体潜在危害和滥用的证据。决策者必须使用循证研究，以便为干预措施提供信息，减少社交媒体做法造成的仇恨、错误信息和数字鸿沟。此外，该研究可能会鼓励利用微观影响者进行积极利用。例如，提高政治参与和疫苗接种率。研究目的：1.利用社交媒体数据，使用和开发先进的定量和空间分析方法，以识别和分析微影响者及其追随者的网络2.调查微影响者在在线平台中日益增长的作用，以及他们如何塑造在线和物理社区

Micro-Influencers as Emerging Digital Opinion Leaders in Local Geographies: A Quantitative Approach to Understanding the Role of Micro-influencers on Twitter Context In today's world, it is undeniable that social media plays a vital role in shaping societies, cultures and economies. Despite growing research on social media, we still know relatively little about its impact, and how to effectively research the vast amounts of data generated. Thus, it is imperative that social science research works to increase understanding of the impact of social media platforms, and to devise new methods to utilise social media data to address contemporary societal concernsThe project contributes to the growing literature on micro-influencers and would provide the first example of research into the influence of micro-influencers in the UK. Research on micro-influencers is particularly critical due to recent action against social bots, as micro-influencers may become more influential. Additionally, the findings can contribute to theories of social media opinion formation and interaction, as it will aid understanding in how social ties are co-created by online and offline interactions. The project also aims to develop an advanced quantitative method framework to analyse social media data. In particular, a collection of machine-learning, big data methods and statistical models to identify and analyse micro-influencers, combined with secondary data to contextualise their influence. Thus, the approach will contribute to improving methods to model the reflexive relationship between online and offline networks. It also provides an opportunity to develop my own research capacity in handling and analysing emerging social media data, which can be progressed into further through a research career in the field. Finally, the findings can add to the growing body of evidence on the potential harms and misuse of social media. Policymakers must use evidence-based research in order to inform interventions to reduce hate, misinformation and digital divides caused by social media practices. Additionally, the research may encourage the utilisation of micro-influencers for positive utilisation. For example, to improve political participation and vaccination uptake. Research Aims: 1.Use and develop cutting edge advanced quantitative and spatial analysis methods with social media data to identify and analyse networks of micro-influencers and their followers 2.Investigate the growing role of micro-influencers within online platforms and how they shape online and physical communities