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

社交媒体的互动性导致每分钟产生的数据量惊人。在言论自由的借口下，一些用户被赋予了一个全新的武器水平，可以在电脑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、跟踪和量化侮辱性言论、基于仇恨的负面言论和偏执言论（基于有偏见的信息）。将开发“智能”算法，并将其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。到目前为止，目前的研究集中在这样一个事实上，即部分挑战是，目前，有效和准确地监测在线滥用所需的数据、工具、过程和系统并不完全可用，而且该领域受到术语、方法、法律和理论挑战的困扰。为了实现这一目的/目标，定义了以下目标：对当前社会和算法在识别和跟踪在线仇恨言论方面的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地图研究呈现。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。3)收集的（现有的和/或抓取的）数据将被预处理成代表特定领域仇恨言论的术语和本体的特征。4)将经典的机器学习技术，如分类和数据聚类应用于识别的数据集，形成项目评估的金标准。深度学习神经网络将被更详细地研究，因为这种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顺序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尚未扩展到本项目将使用的数据的纵向性质。研究的每个不同部分将首先写成一篇论文，在重要会议和期刊上发表，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。

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.