Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole

This project explores how affective polarization, hostility towards people’s political adversaries, manifests on social media. Whereas prior attempts have relied on sentiment analysis and bag-of-word approaches, we use supervised machine learning to capture the nuances of affective polarization in text on social media. Specifically, we fine-tune BERT to build a classifier that identifies expressions of affective polarization in posts shared on Facebook or Twitter during the first six months of the COVID-19 pandemic (n = 8,603,695). Focusing on this context allows us to study how affective polarization evolved on social media as the COVID-19 issue went from unfamiliar to highly political. We explore the temporal dynamics of affective polarization on Facebook and Twitter using ARIMA models and an outlier analysis of the first few months of the pandemic. Further, we examine the interplay between affective polarization and virality across the two platforms. The findings have important implications for those seeking to (1) capture affective polarization in text, and (2) understand how affective polarization manifests on social media. These implications are discussed.

Section: Supervised Machine Learningmentioning

confidence: 97%

The Manifestation of Affective Polarization on Social Media: A Cross-Platform Supervised Machine Learning Approach

Overgaard,

Lukito,

Soorholtz

2024

“…Botzer, Gu, and Weninger (2022) investigated how users provide moral judgments of others, finding that users prefer posts with a positive moral valence. Similar work using r/AmITheAsshole attempts to automatically classify "reasonability" of people's actions based on a retelling of events from a story using a number of social and linguistic features (e.g., up/down votes and sentiment; Haworth et al 2021). Efstathiadis, Paulino-Passos, and Toni (2021) built BERT-based classifiers for submissions and comments and attempts to predict both the final label and the comment labels.…”

Section: Related Workmentioning

confidence: 99%

Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

Giorgi

Zhao

Feng

et al. 2023

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). These first person narratives are, in general, a unique storytelling domain where the author is not only the narrator (the person telling the story) but is also a character (the person living the story) and, thus, the author has two distinct voices presented in the story. In this study, we identify linguistic and narrative features associated with the author as the character or as a narrator. We use these features to answer the following questions: (1) what makes an asshole character and (2) what makes an asshole narrator? We extract both Author-as-Character features (e.g., demographics, narrative event chain, and emotional arc) and Author-as-Narrator features (i.e., the style and emotion of the story as a whole) in order to identify which aspects of the narrative are correlated with the final moral judgment. Our work shows that "assholes" as Characters frame themselves as lacking agency with a more positive personal arc, while "assholes" as Narrators will tell emotional and opinionated stories.

“…Zhou, Smith, and Lee (2021) profiled linguistic patterns in relation to moral judgments, showing that the use of the first-person passive voice in a post correlates with receiving a not-at-fault judgment. Haworth et al (2021) called the judgment on a post 'reasonability' and built machine learning classifiers to predict the judgments using linguistic and behavioral features of a post. Other works focus on automated prediction of moral judgements.…”

Section: Topic Modeling In Textmentioning

confidence: 99%

Mapping Topics in 100,000 Real-Life Moral Dilemmas

Nguyen

Lyall

Tran

et al. 2022

Moral dilemmas play an important role in theorizing both about ethical norms and moral psychology. Yet thought experiments borrowed from the philosophical literature often lack the nuances and complexity of real life. We leverage 100,000 threads—the largest collection to date—from Reddit’s r/AmItheAsshole to examine the features of everyday moral dilemmas. Combining topic modeling with evaluation from both expert and crowd-sourced workers, we discover 47 fine-grained, meaningful topics and group them into five meta-categories. We show that most dilemmas combine at least two topics, such as family and money. We also observe that the pattern of topic co-occurrence carries interesting information about the structure of everyday moral concerns: for example, the generation of moral dilemmas from nominally neutral topics, and interaction effects in which final verdicts do not line up with the moral concerns in the original stories in any simple way. Our analysis demonstrates the utility of a fine-grained data-driven approach to online moral dilemmas, and provides a valuable resource for researchers aiming to explore the intersection of practical and theoretical ethics.