![]() ![]() The emergence of digital technologies such as e-mail, online social networks and instant messages, has dramatically shifted the way we get and consume information and provides an unprecedented opportunity to novel investigations of the information aggregation in networks on a large scale. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information Instead, we find that conspiracy information from media outlets with left or right orientation triggers smaller cascades and is less viral than information from online social media platforms (e.g., Twitter and Imgur) whose political orientations are unclear. In contrast with previous assumption that misinformation is primarily driven by a small set of users, we find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. We further investigate user’s role during the growth of cascades. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives using the constructed features. ![]() We also find that conspiracy cascades are much more concerned with political and controversial topics. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, and is more viral and bursty than science information. We find that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments from an online social media platform, we investigate the propagation of two distinct narratives–(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable. With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life.
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