HateSpotting - Hybrid Sentiment Analysis to capture Hate Speech. Using Applied Intelligent Algorithms to spot localization of Hate-Speech bias across

HateSpotting - 混合情绪分析以捕获仇恨言论。

• 批准号: 2295525
• 金额: --
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2295525
• 关键词: HateSpotting; Hybrid; Sentiment; Analysis; capture

Social Media interactivity has led to a staggering amount of data being generated by the minute. Under the pretext of freedom of speech, some users have been given access to a whole new level of armament to harm to other people or victims behind the shield of a computer screen. This project aims to develop novel machine learning algorithms to spot, track and quantify slurs, hate-based negative and bigoted speech (based on biased information)."Intelligent" algorithms will be developed and applied to captured content data and after, to catch trends or localization of hate-speak. Current research has so far focused on the fact that part of the challenge is that, at present, the data, tools, processes and systems needed to effectively and accurately monitor online abuse are not fully available and the field is beset with terminological, methodological, legal and theoretical challenges.To realise this aim/goal the following objectives are defined:1. Conduct a detailed literature review on the current social and algorithmic advances in the identification and tracking of online hate-speech. Results from this part of the project will be presented as a systematic literature and mapping study, if appropriate.2) Identify potential existing data sources for the research. There are numerous Twitter and other social media datasets in existence, these need locating, collating and evaluating to see if they are appropriate for the research. In parallel, web scraping technologies will be investigated as a means for automatically creating a novel dataset, as a contingency plan, if no existing datasets are found.3) The data collected (existing and/or scraped) will be pre-processed into features that represent the terminology and ontology of domain specific hate-speech.4) Classic machine learning techniques, such as classification and data clustering are to be applied to the identified datasets to form a gold-standard for the project evaluation. Deep learning neural networks will be looked at in more detail as this type of technology is deemed to be highly appropriate for the project.5) The bulk of the research will look at extending work on trajectory based Bayesian Networks and Sequential Pattern Mining techniques for predicting hate-speech paths within forum based social media discussions. Current research in these fields has not been extended to the longitudinal nature of the data that will be used in this project.Each distinct section of research will be written up initially as a paper for publication at key conferences and journals, such as the Intelligent Data Analysis symposium. These papers will then form the core chapters of the final thesis.

社交媒体的互动性导致了每分钟产生惊人数量的数据。在言论自由的借口下，一些用户获得了一个全新的武器水平，在计算机屏幕的掩护下伤害其他人或受害者。该项目旨在开发新的机器学习算法，以发现，跟踪和量化诽谤，基于仇恨的负面和偏执言论（基于偏见信息）。“智能”算法将被开发并应用于捕获的内容数据，然后，捕捉仇恨言论的趋势或本地化。目前的研究集中在这样一个事实，即部分的挑战是，目前，有效和准确地监测在线滥用所需的数据，工具，流程和系统并不完全可用，该领域是困扰着术语，方法，法律的和理论的挑战。为了实现这一目的/目标，确定了以下目标：1.对当前识别和跟踪在线仇恨言论的社会和算法进展进行详细的文献综述。如果合适的话，这部分项目的结果将作为系统的文献和映射研究呈现。2）确定研究的潜在现有数据源。有许多Twitter和其他社交媒体数据集存在，这些需要定位，整理和评估，看看他们是否适合研究。与此同时，如果没有找到现有的数据集，将研究网络抓取技术，作为自动创建新数据集的一种手段，作为应急计划。（现有的和/或刮取的）将被预处理成表示领域特定仇恨言论的术语和本体的特征。4）经典机器学习技术，将对已确定的数据集采用诸如分类和数据聚类等方法，以形成项目评价的黄金标准。深度学习神经网络将被更详细地研究，因为这种类型的技术被认为非常适合该项目。5）大部分研究将着眼于扩展基于轨迹的贝叶斯网络和序列模式挖掘技术的工作，以预测基于论坛的社交媒体讨论中的仇恨言论路径。目前在这些领域的研究还没有扩展到本项目中使用的数据的纵向性质。每个不同的研究部分最初将被写成一篇论文，在关键会议和期刊上发表，例如智能数据分析研讨会。这些论文将构成最终论文的核心章节。