Before revision:  Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857 , while more broad theories performed measurably worse, with scores of 0.654 and 0.347. Despite this , analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. We were able to identify distinct increases in negative sentiment , theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time.
After revision:  Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857 ); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time , theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time.