Abstract:<p>Audio messaging and voice-based interactions are growing in popularity. Lexical features of a manually-curated dataset of real-world audio tweets, as well as text and video/image tweets from the same user accounts, are analyzed to explore how user-generated audio differs from text. The toxicity, sentiment, topic and length of audio tweet transcripts are compared with their accompanying text, date-matched text tweets from the same users and date-matched video/image tweets and their accompanying text. A… Show more
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