Natural language processing (NLP)
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.
NATURAL LANGUAGE PROCESSING (NLP) ACROSS LANGUAGES AND CULTURES:
Speakers of different languages express content differently, both due to language divergences (e.g., Chinese word order indicates grammatical meaning, whereas Korean relies heavily on suffixes for grammatical meaning) and cultural distinctions (e.g., discussions about a given topic may employ conformist or polite terms in one culture, but may employ emotive or antagonistic terms in another). FINS’s multilingual and multicultural NLP takes into account such distinctions for both analysis and synthesis of human language for a wide range of applications of interest to national security, including the detection of beliefs, stances, or concerns associated with heavily debated topics that might lead to harmful polarization or violence. Mapping NLP algorithms across languages and cultures and understanding both equivalences and distinctions among syntactic, lexical, and semantic levels of understanding is a key component toward supporting civil discourse across languages and cultures.
NLP IN SOCIAL MEDIA VIS A VIS MINORITY & GENDER REPRESENTATION:
Extensive use of online media comes with its own set of problems. One of the significant problems plaguing online social media is rampant mis/disinformation and the use of toxic language to silence minorities. Dr. Oliveira’s team uses AI and NLP to understand and attempt to predict human behavior. The techniques employed aim to identify what features personality are more likely to be associated with higher user engagement in deceptive Facebook posts and to understand misinformation on social media platforms, such as Twitter, as a function of tweet engagement, content, and veracity. They also study methods to identify subtle toxicity (e.g., benevolent sexism, sarcasm, etc.) in online conversations. The ability to identify these markers of engagement and harmful behavior can be used to build better systems that can shield people online. Besides studying social media, Dr. Oliveira’s team also looks into online news media. They use NLP to explore factors associated with gender bias and to identify influence cues of disinformation in news media texts. Further, AI is used to identify temporal features of users’ behaviors that can be used to distinguish them online. A particular research thrust showed that online users have unique computer usage behaviors which can be used to distinguish them easily. These results have a significant impact on continuous authentication-related research.
SOCIOLINGUISTIC COMPUTING FOR DETECTION OF FOREIGN INFLUENCE:
Foreign influence campaigns may attempt to inflict harm, often appealing to moral dimensions and identities, as a strategy to induce polarization in other societies. Our research explores potential indicators of influence attempts in language, for example, a sudden introduction of highly controversial and/or polarizing topics in online posts/messages. Language that reflects (and speaks to) the moral values of the target audience can increase in-group cohesion, but further contribute to polarization. Thus, social computing techniques leverage moral values expressed in language to enhance the detection of stances and concerns, as a step toward detection of influence and potentially harmful polarization.