Aporophobia, a negative social bias against poverty and the poor, has been highlighted asan overlooked phenomenon in toxicity detec-tion in texts. Aporophobia is potentially im-portant both as a standalone form of toxicity,but also given its potential as an aggravatingfactor in the wider stigmatization of groups. Asyet, there has been limited quantification of thisphenomenon. In this paper, we first quantifythe extent of aporophobia, as observable in Red-dit data: contrasting estimates of stigmatisingtopic propensity between low–wealth contextsand high–wealth contexts via Bayesian estima-tion. Next, we consider aporophobia as a causalfactor in the prejudicial association of groupswith stigmatising topics, by introducing peoplegroup as a variable, specifically Black people.This group is selected given its history of be-ing the subject of toxicity. We evaluate theaggravating effect on the observed n–grams in-dicative of stigmatised topics observed in com-ments which refer to Black people, due to thepresence of low–wealth contexts. We performthis evaluation via a Structural Causal Mod-elling approach, performing interventions onsimulations via Bayesian models, for three hy-pothesised causal mechanisms.

Literature is to some degree a snapshot of the time it was written in and the societal attitudes of the time. Not all depictions are pleasant or in-line with modern-day sensibilities; this becomes problematic when the prevalent depictions over a large body of work are negatively biased, leading to their normalisation. Many much-loved and much-read classics are set in periods of heightened social inequality: slavery, pre-womens’ rights movements, colonialism, etc. In this paper, we exploit known text co-occurrence metrics with respect to token-level level contexts to identify prevailing themes associated with known problematic descriptors. We see that prevalent, negative depictions are perpetuated by classic literature. We propose that such a methodology could form the basis of a system for making explicit such problematic associations, for interested parties: such as, sensitivity coordinators of publishing houses, library curators, or organisations concerned with social justice

Environmental factors determine the smells we perceive, but societal factors factors shape the importance, sentiment and biases we give to them. Descriptions of smells in text, or as we call them ‘smell experiences’, offer a window into these factors, but they must first be identified. To the best of our knowledge, no tool exists to extract references to smell experiences from text. In this paper, we present two variations on a semi-supervised approach to identify smell experiences in English literature. The combined set of patterns from both implementations offer significantly better performance than a keyword-based baseline.