This study evaluated the intended and unintended effects of Instagram’s content moderation on #coronavirus for both the short- and long-term effects on misinformation and anti-Asian sentiment. We performed manual coding of images (N = 9648), and a series of supervised machine learning methods to classify three waves of comments (N = 22,676) published in 2020 on Instagram. Welch’s F tests were used to compare misinformation, emotions, toxicity, and identity attack across three time periods. The results showed that hashtag moderation had an intended effect in reducing misinformation, and an unintended effect in reducing anger, fear, toxicity, and identity attack. Images with people of East Asian descent were associated with more anger, fear, toxicity, and identity attack than images with people of other races. Prior to content moderation, misinformation was associated with identity attack. Stigmatization on social media, and content moderation of misinformation and hate speech are discussed.
Publication: New Media & Society
Co-authors: Derry Wijaya (BU Computer Science), Jiaxi Wu (COM ’23)