The spread of vaccine-related disinformation online poses an increasingly urgent threat to public health, as evidenced by the outbreak of preventable illnesses like measles and pertussis in parts of the United States and Europe. Despite increasingly sophisticated algorithms for the automatic detection and removal of online disinformation, censorship does not solve the root problem of why people are susceptible to misinformation campaigns online, and risks fanning the flames of conspiracy thinking. (“If they’re silencing us, we must be right!”).

To address the root problem, my study aims to shed light on anti-vaccine discourse online using the tools of Natural Language Processing and Machine Learning. In particular, we aim to improve today’s state-of-the-art algorithms for online discourse by encoding a greater degree of linguistic sophistication. We hypothesize that improved sensitivity to one feature in particular--- causal explanations—will yield important insights into the logic of vaccine conspiracies.
Effektiv start/slut dato01/07/201921/12/2019


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