DISCOVERY OF HIDDEN MENTAL STATES USING EXPLANATORY LANGUAGE REPRESENTATIONS:

Taking the pulse of a given population in a health crisis (such as a pandemic) may predict how the public will handle restrictive situations and what actions need to be taken/promoted in response to emerging attitudes. Our research focuses on the use of explainable representations to gauge potential reactions to non-pharmaceutical interventions (such as masks) through NLP techniques to discover hidden mental states. A stance, which is a belief-driven sentiment, is extracted via propositional analysis (i.e., I believe masks do not help [and if that belief were true, I would be antimask]), instead of a bag-of-words lexical matching or an embedding approach that produces a basic pro/anti label. For example, the sentence I believe masks do not protect me is rendered as ~PROTECT(mask,me). We pivot off this explanatory representation to answer questions such as What is John’s underlying belief and stance towards mask wearing? Because a health crisis can lead to drastic global effects, it has become increasingly important to derive a sense of how people feel regarding critical interventions, especially as trends in online activity may be viewed as proxies for the sociological impact of such crises.