1{2 "document_id": "lb_qasper_acb71edfecd0645219cfd258956141fe666557f8debad399",3 "family": "public_qasper",4 "split": "test",5 "document_hash": "700d0b98f22b5cef89e45118600cc262bbe5dd9626c05cf5425de82ef03f4839",6 "questions": [7 {8 "question_id": "q00",9 "qtype": "qasper",10 "question": "How is the political bias of different sources included in the model?",11 "answer": "By assigning a political bias label to each news article and training only on left-biased or right-biased outlets of both disinformation and mainstream domains",12 "aliases": [13 "we also account for political biases inherent to different news sources, referring to the procedure proposed in BIBREF2 to label different outlets. Overall we show that we are able to classify credible vs non-credible diffusion networks (and consequently news articles) with high accuracy (AUROC up to 94%), even when accounting for the political bias of sources (and training only on left-biased or right-biased articles). We observe that the layer of mentions alone conveys useful information for the classification, denoting a different usage of this functionality when sharing news belonging to the two news domains. We also show that most discriminative features, which are relative to the breadth and depth of largest cascades in different layers, are the same across the two countries."14 ],15 "answer_type": "qa_f1",16 "tolerance": 0.0,17 "supporting_fact_ids": [],18 "answer_position_frac": -1.019 }20 ],21 "facts": []22}