  Socio-linguistic indicators of text, such as emotion or sentiment, are often
extracted using neural networks in order to better understand features of
social media. One indicator that is often overlooked, however, is the presence
of hazards within text. Recent psychological research suggests that statements
about hazards are more believable than statements about benefits (a property
known as negatively biased credulity), and that political liberals and
conservatives differ in how often they share hazards. Here, we develop a new
model to detect information concerning hazards, trained on a new collection of
annotated X posts, as well as urban legends annotated in previous work. We show
that not only does this model perform well (outperforming, e.g., zero-shot
human annotator proxies, such as GPT-4) but that the hazard information it
extracts is not strongly correlated with other indicators, namely moral
outrage, sentiment, emotions, and threat words. (That said, consonant with
expectations, hazard information does correlate positively with such emotions
as fear, and negatively with emotions like joy.) We then apply this model to
three datasets: X posts about COVID-19, X posts about the 2023 Hamas-Israel
war, and a new expanded collection of urban legends. From these data, we
uncover words associated with hazards unique to each dataset as well as
differences in this language between groups of users, such as conservatives and
liberals, which informs what these groups perceive as hazards. We further show
that information about hazards peaks in frequency after major hazard events,
and therefore acts as an automated indicator of such events. Finally, we find
that information about hazards is especially prevalent in urban legends, which
is consistent with previous work that finds that reports of hazards are more
likely to be both believed and transmitted.
