{"id":"W4404923341","doi":"10.1007/s42803-024-00092-3","title":"Botaganda: examining how bots shape political discourse on twitter through the lens of interaction alignment","year":2024,"lang":"en","type":"article","venue":"International Journal of Digital Humanities","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"York University; Government of Ontario","keywords":"Politics; Through-the-lens metering; Lens (geology); Sociology; Political science; Media studies; Physics; Optics; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002415113,0.00008272839,0.0001036807,0.0001313771,0.00009866803,0.001084872,0.000283178,0.00002989254,0.0003039957],"category_scores_gemma":[0.0002219082,0.00005375798,0.0001069003,0.00004580807,0.0002772706,0.002833034,0.00003424652,0.0001634221,0.00002146599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002042516,"about_ca_system_score_gemma":0.0001291879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002657429,"about_ca_topic_score_gemma":0.00001447481,"domain_scores_codex":[0.998533,0.00003767465,0.0003118049,0.00005679549,0.0009068083,0.0001539855],"domain_scores_gemma":[0.9991845,0.0002898067,0.0001826302,0.00005112716,0.0002510012,0.00004097302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005961796,0.00008407266,0.0001298854,0.00001169521,0.0002138216,0.00002538249,0.1032284,0.00002123524,0.00003468008,0.880945,0.007447358,0.007798795],"study_design_scores_gemma":[0.0006071875,0.0007241493,0.003489724,0.001240676,0.00006457364,0.0001514086,0.6223814,0.0001842177,0.0009223945,0.02885648,0.3410951,0.0002826499],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6791967,0.00009143644,0.0002633633,0.01717215,0.002577979,0.00007054192,0.00004107166,0.00002014592,0.3005666],"genre_scores_gemma":[0.9954898,0.00003968628,0.00001196472,0.0008005686,0.0009819102,4.848621e-7,0.000004010732,0.000006970141,0.002664619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8520885,"threshold_uncertainty_score":0.9999521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1416948169542027,"score_gpt":0.3883316628967648,"score_spread":0.2466368459425621,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}