Active learning to measure opinion and violence in French newspapers
Paul Guélorget, Guillaume Gadek, Titus B. Zaharia, Bruno Grilhères · Procedia Computer Science · 2021
News articles analysis may be oversimplified when restricted to detecting classes of interest already benefiting from trustworthy labeled datasets, like political affiliation or fakeness. Behind an apparent neutrality, an editorial slant may be embodied by favoring one-sided interviews, avoiding topics or choosing oriented illustrations. These challenges, seen as machine learning problems, would require a tedious annotation task. We introduce ReALMS, an active learning framework capable of quickly elaborating models which detect arbitrary classes in multi-modal text and image documents. Evidence of this capability is given by a case study on French news outlets: the detection of subjectivity, demonstrations and violence.