In democratic countries the latent ideology landscape is foundational to individual and collective political action; conversely, fringe ideology drives Ideologically Motivated Violent Extremism (IMVE). Therefore, quantifying ideology is a crucial first step to an ocean of downstream problems, such as; understanding and countering IMVE, detecting and intervening in disinformation campaigns, and broader empirical opinion dynamics modeling. However, online ideology detection faces two significant hindrances. Firstly, the ground truth that forms the basis for ideology detection is often prohibitively labor-intensive for practitioners to collect, requires access to domain experts and is specific to the context of its collection (i.e., time, location, and platform). Secondly, to circumvent this expense researchers generate ground truth via other ideological signals (i.e. hashtags, politicians, etc.), however, the bias this introduces has not been quantified and often this still requires expert intervention. In this work, we present an end-to-end ideology detection pipeline applicable to large-scale datasets. We construct context-agnostic and automatic ideological signals from widely available media slant data; show the derived pipeline is performant, compared to pipelines of common ideology signals and SOTA baselines; employ the pipeline for left-right ideology, and (the more concerning) detection of extreme ideologies; generate psychosocial profiles of the inferred ideological groups; and, generate insights into their morality and preoccupations.
翻译:在民主国家,潜伏的意识形态景观是个人和集体政治行动的基础;反之,边缘意识形态驱动了具有意识形态动机的暴力极端主义(IMVE),因此,量化意识形态是走向下游问题的海洋的关键第一步,例如:理解和反对IMVE,发现和干预不实信息运动,以及更广泛的经验观点动态模型。然而,在线意识形态探测面临两个重大障碍。首先,构成意识形态检测基础的地面真相往往令人望而却步,供从业人员收集、需要接触域专家,并且具体到收集的范围(即时间、地点和平台)。第二,为了绕过这一费用研究人员通过其他意识形态信号(即标签、政治家等)产生地面真相,但这一介绍的偏见尚未量化,而且往往还需要专家干预。在这项工作中,我们提出了一个适用于大型数据集的端对端对端的意识形态检测管道。我们从广泛获得的媒体倾斜数据(即时间、地点和平台)中构建了背景和自动的意识形态信号;显示衍生的管道正在形成地面事实真相,而其意识形态的根基和意识形态的直观,将利用共同意识形态的根基点和意识形态的根基点和直图。</s>