Likely stories: artificial intelligence, media bias and marginalised audiences

能够列举出用于操纵个人和群体的技术和分布模型吗？在社会结构层面上，是否存在一种机制，可以通过分析在线个人资料行为，以及隐含的和实际的元数据（不仅通过社交图谱，还通过交流内容）来识别？在其他社区中，是否存在语言上、时间上或交际上的指纹来识别袜子玩偶账户？这些在线身份是否可以通过机器智能和模式识别技术识别或浮出水面？是否存在一种围绕在线社交的元数据分类法，可以帮助理解或描述参与者的类型？该研究旨在通过分析发布的在线数据（使用Twitter、Reddit和其他带有社交元素的大型在线平台等来源）和分析参与者的互动、联系、频率和语言模式来确定这些标准，并使用数据科学技术来确定这些信息背后是否有任何有意义的推论。不同的模型和方法将在相同的数据上运行，然后与人类观察者如何看待这些类型的互动以及他们如何解释它们进行比较。这些数据还可用于验证这些方法对在线身份和具有已知意图的通信的效用。由此，我们可以开始确定可能的分类和有用的标准，通过这些分类和标准，我们可以开始评估在线消息与行为特征类型之间的相关性，以提供训练集，并可能为基于机器的工具提供基础，以帮助个人或平台提供商拨打有关在线社区和个人特征的意图和健康状况的电话。

Can the techniques and distribution models for astroturfing individuals and communities of manipulation be enumerated? Are there mechanisms at the social structural level that can be identified though analysis of online profile behaviour, and of implied and actual metadata - not only through the social graph, but the content of communication? Are there linguistic, time-based or communicative fingerprints that identify sock-puppet accounts within other communities? Can these online identities be recognised or surfaced through machine intelligence and pattern recognition techniques? Is there a taxonomy of metadata around online social communications that can help understand or profile types of participant?The research will aim to identify these criteria by analysing published online data (using sources such as Twitter, Reddit, and other large-scale online platform with a social element) and analysing the interactions, connections, frequency and linguistic patterns of participants to establish if there are any meaningful inferences behind this information, using datascience techniques. Various models and approaches will be run on the same data, and also then compared with how human observers see these types of interaction and how they interpret them. This data can also be used to verify the utility of these approaches against online identities and communications with known intents. From this, we can start to identify likely taxonomies and useful criteria by which we can begin to make assessments about the correlation of online messages with types of behavioural profile to feed a training set and potentially provide the underpinning for machinebased tools to assist individuals or platform providers in making calls about the intent and health of online communities and individual profiles.