TAXODIS - A Taxonomy of Online Disinformation

The Taxonomy of Online Disinformation (TAXODIS) structures a set of well-defined and analysed linguistic features of online disinformation discourse and is meant to help annotate training data to nourish machine learning and computational models. TAXODIS has been implemented as a SKOS vocabulary/thesaurus and is available as an RDFS resource (in TTL/Turtle format) at: https://hop.fzi.de/wordpress/wp-content/uploads/taxodis/v1.0/TAXODIS.ttl The (resolvable) namespace of the taxonomy is https://hop.fzi.de/taxodis/. PURPOSE The taxonomy can be used for the fine-grained annotation of disinformation datasets and for the publication of the annotation results as Linked (Open) Data. It can be used by automated detection systems to train classification models. STRUCTURE The taxonomy contains 66 concepts organized in a hierarchical structure. Its top concept is "disinformation characteristic", which describes characteristics that are indicative of disinformation in a piece of content. This top term has three narrower terms: "detection feature", which classifies the piece of content based on linguistic or stylistic features that are indicative of the detection of disinformation (e.g. length of the headline, lexical and contentual poorness, level of semantic incoherence, lack of new information, level of topicality, etc.), "categorization", which classifies the piece of content based on its theme or content type. e.g. social (theme), conspiracy theory (content type), and "veracity", which classifies the piece of content based on its veracity, e.g. mostly false, mixture, etc. The full hierarchy is shown in this figure. USAGE The taxonomy can be used together with existing, established vocabularies for the annotation of (disinformation) resources. We suggest the exploitation of the Web Annotation Data Model; a W3C recommendation for the structured representation of annotations that can be shared and reused across different platforms. This figure shows an example in which an article (instance of class oa:Target) is linked to two annotations: one which categorises the article as of social theme (taxodis:themsoc) and one which categorises the article as having "high" topicality level (taxodis:mobtopical). The intension (motivation) of both annotations is classification (oa:classifying). Notice that the first annotation is directly linked to the taxonomy term taxodis:themsoc through multiple instantiation (the term is an instance of both oa:Body and skos:Concept). This annotation method can be applied for all taxonomy terms that are under "categorisation" and "veracity", since these terms do not accept a degree value or level like the terms that are under "detection feature". This figure shows how we can link the annotated resource with rich (meta)data using another established vocabulary, namely schema.org. The annotated article is both an instance of oa:Target and an instance of schema:CreativeWork. This allows using properties of schema.org for providing more information about the article, such as its URL (instance of schema:URL), its publication date (instance of schema:DateTime), its headline (instance of schema:Text), its author (instance of schema:Person), and its content (instance of schema:Text). We can also link the article with entities of different types mentioned in it, such as persons, places, etc., using the property schema:mentions. In addition, we can link claims (instances of schema:Claim) to the articles using the property schema:appearance. A claim can be then linked to its text, video/audio (if any) and author (using the properties schema:text, schema:video/schema:audio, and schema:author, respectively), as well as with claim reviews (instances of schema:ClaimReview). In a similar way, a claim review can be linked with related data such as its author, URL, publication date, headline, review body, etc. Queries that can be answered using TAXODIS annotations include: Retrieve all resources classified as of social theme and which have a high level of emotional polarization Retrieve all resources with imposter content together with the values of all features that are under "psychology feature" Retrieve the number of resources per content type having high usage of emphatic wording Retrieve all resources published on a specific time period containing claims that have been reviewed and have received a veracity score "mostly false" Retrieve all resources mentioning a specific person which are mostly false, together with the values of all features that are under "detection feature" The first query of the above list is translated to SPARQL as follows: PREFIX taxodis: <https://hop.fzi.de/taxodis/> PREFIX oa: <http://www.w3.org/ns/oa#> PREFIX schema: <http://schema.org/> SELECT ?resourceUri ?resourceHeadline ?resourceAuthor WHERE { ?annot1 oa:hasTarget ?resourceUri ; oa:hasBody taxodis:themsoc . ?annot2 oa:hasTarget ?resourceUri ; oa:hasBody ?annot2Body . ?annot2Body oa:type taxodis:mobpolar ; rdf:value "high" . OPTIONAL { ?resourceUri schema:headline ?resourceHeadline } OPTIONAL { ?resourceUri schema:author ?resourceAuthor } } ANNOTATION PROTOCOL / GUIDELINES We do not provide specific annotation guidelines as of how to use the taxonomy, because such protocol is usually highly task and domain dependent. Thus, we leave to the specific community and teams the design of annotation guidelines according to their needs. It is worth noting that some taxonomy categories, such as "emotional polarization" versus "sensationalism", exhibit conceptual proximity that may challenge consistent annotation. In such cases, multiple labels to a single content item can be assigned, even within the same dimension where categories are not strictly mutually exclusive. For example, content may be simultaneously labeled as both "clickbait" and "propaganda" when relevant. This multilabel approach reflects the complex and often overlapping nature of disinformation phenomena. In the context of the DeFaktS project, a group of interested researchers (from sociology, computer science, and political science) and practitioners (from NGOs and industry) utilized an earlier version of the taxonomy to create labels for identifying different types of disinformation. The participants were provided with a handout of the taxonomy and applied it to social media posts derived from various platforms (mainly Telegram and X). This handout may also prove useful in similar manual annotation efforts. RELATED PUBLICATION (currently under review)在线虚假信息分类体系（TAXODIS）构建了一套经过严谨定义与充分分析的在线虚假信息话语语言特征框架，旨在为训练数据标注提供支撑，进而赋能机器学习与计算模型的研发。 TAXODIS 已被实现为简单知识组织系统（Simple Knowledge Organization System，简称SKOS）词汇表/叙词表，并以资源描述框架模式（Resource Description Framework Schema，简称RDFS）资源形式发布，格式为TTL/Turtle，获取地址为：https://hop.fzi.de/wordpress/wp-content/uploads/taxodis/v1.0/TAXODIS.ttl 该分类体系的可解析命名空间为：https://hop.fzi.de/taxodis/。 ## 核心用途 本分类体系可用于虚假信息数据集的细粒度标注，以及将标注结果以关联开放数据（Linked (Open) Data）形式发布；同时可供自动化检测系统用于训练分类模型。 ## 体系结构 本分类体系包含66个概念，采用层级结构组织。其顶层概念为「虚假信息特征」，用于描述可指示某一内容存在虚假信息的各类特征。该顶层概念下设三个下位概念： 1. 「检测特征」：基于可用于识别虚假信息的语言或文体特征对内容进行分类，例如标题长度、词汇与内容匮乏度、语义不连贯程度、新信息缺失度、话题时效性等级等； 2. 「分类标注」：基于内容的主题或内容类型对其进行分类，例如社交类（主题）、阴谋论（内容类型）； 3. 「真实性判定」：基于内容的真实性进行分类，例如大部分虚假、虚实混合等。 完整层级结构详见本图示。 ## 使用方式 本分类体系可与现有成熟词汇表配合，用于（虚假信息）资源的标注工作。我们建议采用万维网联盟（World Wide Web Consortium，简称W3C）推荐的Web标注数据模型（Web Annotation Data Model），该模型可实现标注内容的结构化表示，并支持在不同平台间共享与复用。 本图示展示了一个应用示例：一篇属于`oa:Target`类的文章，关联了两项标注：其一将该文章归类为社交主题（对应术语`taxodis:themsoc`），其二将该文章的话题时效性等级标注为「高」（对应术语`taxodis:mobtopical`）。两项标注的动机均为分类（`oa:classifying`）。需注意，第一项标注通过多重实例化直接关联至分类体系术语`taxodis:themsoc`——该术语同时为`oa:Body`与`skos:Concept`的实例。由于「分类标注」与「真实性判定」下的术语不支持如「检测特征」下的术语那样的程度值或等级，因此该标注方法可适用于这两类下的所有分类体系术语。 本图示同时展示了如何通过成熟词汇表schema.org，将标注资源与丰富的（元）数据进行关联。被标注的文章同时为`oa:Target`与`schema:CreativeWork`的实例，因此可借助schema.org的属性补充文章的更多信息，例如其URL（`schema:URL`的实例）、发布日期（`schema:DateTime`的实例）、标题（`schema:Text`的实例）、作者（`schema:Person`的实例）与正文内容（`schema:Text`的实例）。此外，还可通过属性`schema:mentions`将文章与文中提及的各类实体（如人物、地点等）进行关联。 此外，可通过属性`schema:appearance`将主张（`schema:Claim`的实例）关联至相关文章。主张可进一步通过对应属性关联至其文本、（若有的）音视频内容与作者（分别使用`schema:text`、`schema:video`/`schema:audio`与`schema:author`），同时还可关联至主张审核报告（`schema:ClaimReview`的实例）。同理，主张审核报告也可关联至其作者、URL、发布日期、标题与审核正文等相关数据。 ## 可支持的查询示例 借助TAXODIS标注，可实现以下类型的查询： 1. 检索所有被归类为社交主题且情感极化程度较高的资源； 2. 检索所有包含虚假伪装内容的资源，并附带「心理学特征」下的所有特征取值； 3. 统计各类内容类型下，使用强化措辞程度较高的资源数量； 4. 检索特定时间段内发布的、包含已审核且真实性评分为「大部分虚假」的主张的资源； 5. 检索所有提及特定人物且内容大部分虚假的资源，并附带「检测特征」下的所有特征取值。 以下为上述第一条查询的SPARQL实现代码： PREFIX taxodis: <https://hop.fzi.de/taxodis/> PREFIX oa: <http://www.w3.org/ns/oa#> PREFIX schema: <http://schema.org/> SELECT ?resourceUri ?resourceHeadline ?resourceAuthor WHERE { ?annot1 oa:hasTarget ?resourceUri ; oa:hasBody taxodis:themsoc . ?annot2 oa:hasTarget ?resourceUri ; oa:hasBody ?annot2Body . ?annot2Body oa:type taxodis:mobpolar ; rdf:value "high" . OPTIONAL { ?resourceUri schema:headline ?resourceHeadline } OPTIONAL { ?resourceUri schema:author ?resourceAuthor } } ## 标注协议与指南 本分类体系未提供具体的使用指南，因为标注协议通常高度依赖具体任务与领域。因此，我们将标注指南的制定交由对应社区与团队根据自身需求完成。需注意，部分分类类别（如「情感极化」与「耸动宣传」）存在概念邻近性，可能对标注一致性造成挑战。在此类场景下，即便在类别非严格互斥的同一维度下，也可为单个内容项分配多个标签。例如，若符合条件，内容可同时被标记为「标题党」与「宣传内容」。这种多标签标注方式契合了虚假信息现象的复杂性与重叠性本质。 在DeFaktS项目框架下，由社会学、计算机科学、政治学领域研究者及非政府组织与行业从业者组成的工作组，曾利用本分类体系的早期版本为各类虚假信息类型生成标签。参与者获赠了分类体系手册，并将其应用于来自多个平台（主要为Telegram与X）的社交媒体帖子。该手册同样可适用于类似的人工标注工作。 ## 相关出版物（目前处于审稿阶段）