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.

通过使用神经网络从文本中提取社会语言学指标，研究证明，有关危险的陈述比有关好处的陈述更具可信度，政治自由派和保守派在分享危险信息的频率上存在差异，并且危险信息与道德愤怒、情绪、威胁词等指标相关，同时与恐惧情绪呈正相关、与喜悦情绪呈负相关。利用新的标注数据集和已有的标注数据集训练了一个新模型以检测与危险相关的信息，并应用于 COVID-19 相关帖子、2023 年哈马斯 - 以色列战争相关帖子和扩展的城市传奇集合数据，发现不同数据集中与危险相关的词汇以及政治派别之间的差异，该信息也作为重大危险事件的自动指示器。最后，发现城市传奇中关于危险的信息尤为普遍，与之前的研究结果一致，危险的报道更容易被人们相信和传播。