Towards Explaining Subjective Ground of Individuals on Social Media (2022)

Abstract: Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual’s theory of mind and behavior from text is far from being resolved. This research proposes a neural model—Subjective Ground Attention—that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one’s previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual’s subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual’s subjective orientation towards abstract moral concepts.

## introduction for the last few years, large-scale language models have shown substantial performance gains in many different sub-fields of natural language processing @xcite . researchers have hypothesized that such language models contain knowledge of linguistic characteristics, logical inference, and real world events in their parameters, and this knowledge can be fine-tuned and adapted to downstream tasks @xcite . the recent success of commonsense reasoning, for instance, shows that language model parameters can be used as a knowledge base while they comprehensively learn commonsense patterns @xcite . although deeper and larger language models have led machines to better comprehend how the real world works, understanding an individual's perspective and behavior from text is yet far from being resolved. humans perceive daily situations and events differently, and employ certain biases figure 1 : an example of a reddit post and its comments crawled from r/amitheasshole. the author describes a situation of not giving up on the pet over the health of significant other's daughter. the author's behavior is unacceptable to the first redditor (red arrow), while the second redditor (blue arrow) has an opposite view. @xcite and social expectations @xcite when they evaluate the given event and social behavior of others @xcite . an individual's process of attributing, evaluating, and explaining an event has been widely investigated by cognitive and social psychologists for the past few decades @xcite , yet its application to neural language models is outside the mainstream natural language processing research. this paper proposes a neural model, subjective ground attention, that learns cognitive models of individuals and explains their subjective judgments on situations that are posted on social media. we analyze a reddit community, r/amitheasshole, where users submit posts asking whether their behaviors are justifiable, and other users leave comments with their judgments. figure 1 shows an example of a reddit post where an author describes a situation, and different redditors provide their subjective judgment through comments. the research hypothesis is that people comprehend and account for others' situations based on their subjective ground, a maxim that plays a cen-tral role in human moral judgments @xcite , which can be represented from their previous activities. to investigate this hypothesis, we formulate a task to predict the redditors' judgment given diverse situations. each redditor is represented by their subjective ground which is estimated from their previous comments. using clustering methods, the model selects a set of the most relevant subjective ground to a given situation. the model then learns attention weights among subjective ground comments to predict the redditor's moral judgments. through learned attention weights, we present human-readable subjective ground and how they contribute to the model's prediction about the judgments. from empirical results, we suggest that our proposed model provides explanations for the redditors' subjective judgments on diverse social situations and contributes to downstream task performance in a statistically significant way. we additionally compute the consistency score of attention weights with respect to the model's final prediction, and show that the model efficiently uses attended subjective ground. from qualitative analysis results, we further claim that our model not only learns an individual's subjective preference on real world situations (e.g. reporting my best friend for cheating), but also infers an individual's perspective on more abstract concepts of competing moral values (e.g. fairness is more important than a friendship). ## data preparation we analyze daily situations and individuals' subjective judgments on them posted on a reddit community, r/amitheasshole. users submit posts describing their situations and ask whether or not their behaviors are acceptable. one of the advantages of using this data domain is that most of the situations are generic (e.g. getting annoyed at my roommate) rather than related to specific world events (e.g. new climate change policies in u.s.), thus the models benefit from implicit knowledge in language model parameters to better understand the situation. ## model in this work, we develop neural models with two main components: subjective ground training module and value attention training module. this is followed by a classifier to predict the redditor's moral judgments. figure 2 illustrates the model ## experiments we implement models varying in attention structures and subjective ground representations. macro f1 is used as an evaluation metric for accuracy since the label distribution is unbalanced and the two classes are equally important. implementation details are described in appendix a.2. codes are released for future reference. foot_2 ## conclusion this paper proposes a neural model, subjective ground attention, that represents an individual's subjective preference with their previous activities and explains the reasoning behind their moral judgments on diverse social situations by spotlighting the most relevant subjective ground. we explore situations posted on a reddit community, r/amitheasshole, and analyze active redditors' judgments on these situations indicating whether or not the author's behavior is acceptable. upon attending subjective comments and moral rules-ofthumb, experimental results show that the model provides reasonable explanations without sacrificing prediction accuracy. although attention weights on moral rules-of-thumb show suboptimal consistency with the model's prediction, we illustrate the model's consistency in attention weights on subjective ground comments. we further claim that our model better captures one's perspectives on abstract moral concepts.