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In democratic countries, the ideology landscape is foundational to individualand collective political action; conversely, fringe ideology drivesIdeologically Motivated Violent Extremism (IMVE). Therefore, quantifyingideology is a crucial first step to an ocean of downstream problems, such as;understanding and countering IMVE, detecting and intervening in disinformationcampaigns, and broader empirical opinion dynamics modeling. However, onlineideology detection faces two significant hindrances. Firstly, the ground truththat forms the basis for ideology detection is often prohibitivelylabor-intensive for practitioners to collect, requires access to domain expertsand is specific to the context of its collection (i.e., time, location, andplatform). Secondly, to circumvent this expense, researchers generate groundtruth via other ideological signals (like hashtags used or politiciansfollowed). However, the bias this introduces has not been quantified and oftenstill requires expert intervention. This work presents an end-to-end ideologydetection pipeline applicable to large-scale datasets. We constructcontext-agnostic and automatic ideological signals from widely available mediaslant data; show the derived pipeline is performant, compared to pipelines ofcommon ideology signals and state-of-the-art baselines; employ the pipeline forleft-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.

Detecting Extreme Ideologies in Shifting Landscapes: an Automatic & Context-Agnostic Approach