AUTNES Content Analysis of ORF TV Confrontations 2013 (SUF edition)

Full edition for scientific use. The AUTNES dataset on TV confrontations contains data collected from fifteen debates between pairs of top candidates in the 2013 national election campaign. The debates were televised by the Austrian national broadcasting company ORF between August 29th and September 24th, 2013. All statements spoken during the debates are part of the dataset. Natural sentences are the unit of analysis. The coding procedure applies the AUTNES relational approach of recording subjects, predicates, and objects to TV confrontations. The subject is defined as an actor that positions himself/herself towards an issue in the sentence. There are two types of objects: issues and object actors. The one issue that fits the content of the spoken sentence best is selected by coders from the AUTNES issue coding scheme. The issue predicate numerically records whether the subject’s position towards the issue is one of support, rejection/criticism, or conveys a neutral stance. Up to three object actors are recorded per sentence, each with their name (if an individual is present), organisational affiliation and political relevance, as well as their evaluation by the subject actor (positive, negative or neutral). In addition to the basic subject–predicate–object structure, we code character traits and party records for all subject and object actors as well as justifications for issue statements. Variables: Variables referring to the debate and coded sentence: case-ID; debate-ID; sentence-ID; sender (TV station broadcasting the debate); date of the debate; begin time and end time of the debate; duration of the debate; party affiliation and name of discussant 1 und discussant 2; name of the debate’s moderator; organisational affiliation and name of the sentence’s speaker (various actors besides the participants of the debate, i.e. journalists, experts, etc.); sentence; sentence length in words; sentence length in characters; variables referring to the subject actor: organisational affiliation and name of the subject actor; characteristics of the subject actor (attributes: competence, character, leadership, appearance, not classifiable); reference to subject actor’s record; record level (national level, regional, international, historical); subject actor established the record while in government or while in opposition; variables referring to issues: predicate (issue position of the subject actor: rejection/criticism, neutral, support); issue on which the subject actor positions himself/herself; issue should be regulated on EU level; subject actor’s justification of his/her issue position (economy, welfare state: expansive / protective, environment, security, education, governance, ethnic-national, religious, universalistic, not classifiable); variables referring to object actor: organisational affiliation and name of the object actor; political relevance of the object actor; predicate (relation of the subject actor towards the object actor) (rejection/criticism, neutral, support); relationship between subject actor and object actor is related to the coded issue; characteristics of the object actor (attributes: competence, character, leadership, appearance, not classifiable); reference to the object actor’s record; record level (national level, regional, international, historical); object actor established the record while in government or while in opposition.

供科研使用的完整版数据集。本电视辩论场景下的AUTNES数据集（AUTNES dataset）收录了2013年奥地利全国大选竞选期间15场顶尖候选人双人辩论的相关数据。该系列辩论于2013年8月29日至9月24日由奥地利国家广播电视公司（ORF）进行电视转播。所有辩论过程中的发言均被纳入本数据集。分析单元为自然语句。本次编码流程采用AUTNES关系型编码方法，针对电视辩论场景提取语句中的主语、谓语与宾语要素。主语被定义为在语句中针对某一议题表明自身立场的行为主体。宾语分为两类：议题与客体行为主体。编码人员需从AUTNES议题编码体系中，选取最贴合发言语句内容的单一议题。议题谓语以数值形式记录行为主体针对该议题的立场，包括支持、反对/批评与中立三种类型。每条语句最多可记录三个客体行为主体，每个主体需标注其姓名（若为个体）、所属机构、政治相关性，以及行为主体对其的评价（正面、负面或中立）。除基础的主谓宾结构外，本数据集还对所有主语与客体行为主体的性格特征、党派归属，以及议题发言的论证依据进行编码。
变量说明：
与辩论及编码语句相关的变量：案例编号（case-ID）、辩论编号（debate-ID）、语句编号（sentence-ID）、播出方（即转播辩论的电视台）、辩论日期、辩论开始与结束时间、辩论总时长、两位辩论者（discussant 1与discussant 2）的党派归属与姓名、辩论主持人姓名、发言者的所属机构与姓名（含辩论参与者之外的主体，如记者、专家等）、语句原文、语句词数、语句字符数；
与主语行为主体相关的变量：主语行为主体的所属机构与姓名、主语行为主体特征（属性包括：能力、性格、领导力、形象、无法归类）、主语行为主体的档案引用、档案层级（国家级、地区级、国际级、历史级）、主语行为主体提出该档案时处于执政党还是在野党地位；
与议题相关的变量：谓语（即主语行为主体的议题立场：反对/批评、中立、支持）、主语行为主体所表明立场的议题、议题应在欧盟层面进行规制、主语行为主体对其议题立场的论证依据（类别包括：经济、福利国家：扩张型/保护型、环境、安全、教育、治理、民族-国家、宗教、普世主义、无法归类）；
与客体行为主体相关的变量：客体行为主体的所属机构与姓名、客体行为主体的政治相关性、谓语（即主语行为主体对客体行为主体的态度关系：反对/批评、中立、支持）、主语与客体行为主体的关系与当前编码议题相关、客体行为主体特征（属性包括：能力、性格、领导力、形象、无法归类）、客体行为主体的档案引用、档案层级（国家级、地区级、国际级、历史级）、客体行为主体提出该档案时处于执政党还是在野党地位。