超越社交媒体分析：使用自结构增量机器学习理解人类行为和深层情感
Beyond Social Media Analytics: Understanding Human Behaviour and Deep Emotion using Self Structuring Incremental Machine Learning
摘要：
本文提出了一个概念框架，认为社会数据代表了人类社会行为，需求和认知的层次结构的表层，用于将社会数据转换为保留社会行为及其因果关系的表示。基于这个框架，我们建立了两个平台，分别从快节奏和慢节奏的社交数据中获取洞察力。对于快节奏，开发了一种自结构和增量学习技术，以自动捕获随着时间的推移的突出主题和相应的动态。开发了一种事件检测技术，用于使用诸如音量和情绪等多个指标自动监测那些识别出的主题路径，以发现社交行为的显著波动。该平台使用两个拥有超过100万条推文的大型数据集进行演示。分开的主题路径代表了每个实体的关键主题，并且与主题一致性措施相一致。确定的事件与新闻报道的当代事件进行了对比。其次，对于慢节奏的社交数据，开发了一套新的机器学习和自然语言处理技术，以自动捕获个人的自我披露信息，例如人口统计数据，情感和个人事件的时间轴。该平台在从在线支持团体收集的400多万条帖子的大型文本语料库上进行了试用。这被进一步扩展到转换成一个多维度的代表性的前列腺癌相关的在线支持小组的讨论，并调查了自我披露的生活质量的患者（和合作伙伴）对时间，人口统计学和临床因素。该扩展平台的功能已通过使用从10个前列腺癌在线支持小组收集的文本语料库进行了证明，其中包括609，960个前列腺癌讨论和22，233名患者。
英文摘要：
This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
