RESUMEN
Networked social influence and strategic information manipulation are two social mechanisms fueling misinformation spread in online communities. However, it is unclear how these two mechanisms differ in their impacts. We conducted social network analyses on two online communities sharing misinformation concerning refugees in 2016 and COVID-19 in 2020. The results robustly showed that online misinformation spread is transitive and positively associated with members' embedded authority (i.e., the extent to which members' information is exclusively shared within the focal community). At the same time, strategic misinformation sharing by members of high community loyalty (i.e., targeted information sharing within the community) is less likely to gain momentum. The impact of bots on misinformation is contingent. Findings suggest that networked social influence is a more powerful driver of misinformation spread than strategic information manipulation. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
RESUMEN
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS$$