{"id":"W2963261759","doi":"10.48550/arxiv.1804.02318","title":"How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint (computer-aided design); Computer science; Social media; Limiting; Affect (linguistics); Quality (philosophy); Character (mathematics); Time constraint; Content (measure theory); Upper and lower bounds; Information retrieval; World Wide Web; Mathematics; Psychology; Engineering; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002237095,0.0003116743,0.0003596519,0.001014992,0.003126441,0.003681418,0.000726794,0.001400665,0.005534769],"category_scores_gemma":[0.02948959,0.0002490643,0.0003340782,0.001688492,0.001968737,0.003929197,0.00195628,0.001522242,0.001205234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001504992,"about_ca_system_score_gemma":0.0005027604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0058616,"about_ca_topic_score_gemma":0.007709339,"domain_scores_codex":[0.9973187,0.00131008,0.0001501725,0.0003771695,0.0005364833,0.000307436],"domain_scores_gemma":[0.9688524,0.02166108,0.003954638,0.002544546,0.001448112,0.001539241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006910522,0.002084139,0.5673623,0.0008431486,0.0003136188,0.00775257,0.171839,0.005609569,0.05801647,0.03044317,0.01889132,0.1299343],"study_design_scores_gemma":[0.0003827692,0.001239754,0.6912748,0.0002439386,0.0001850277,0.002092672,0.1472288,0.01612137,0.01728032,0.02873678,0.09479636,0.0004174199],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863615,0.00009729686,0.0009984871,0.001106197,0.00003658203,0.00004501693,0.0003979914,0.00006430555,0.01089265],"genre_scores_gemma":[0.9961352,0.0000563965,0.001077219,0.0002870685,0.00004601338,0.00008427171,0.0003149795,0.00008591087,0.001912891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0058616,"threshold_uncertainty_score":0.01851565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128971759894889,"score_gpt":0.2224812009398462,"score_spread":0.1095840249503573,"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."}}