Julia Mendelsohn


2021

pdf
Detecting Community Sensitive Norm Violations in Online Conversations
Chan Young Park | Julia Mendelsohn | Karthik Radhakrishnan | Kinjal Jain | Tushar Kanakagiri | David Jurgens | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EMNLP 2021

Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur. However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context- and community-sensitive norm violation detection, showing that these changes give high performance.

pdf
Modeling Framing in Immigration Discourse on Social Media
Julia Mendelsohn | Ceren Budak | David Jurgens
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The framing of political issues can influence policy and public opinion. Even though the public plays a key role in creating and spreading frames, little is known about how ordinary people on social media frame political issues. By creating a new dataset of immigration-related tweets labeled for multiple framing typologies from political communication theory, we develop supervised models to detect frames. We demonstrate how users’ ideology and region impact framing choices, and how a message’s framing influences audience responses. We find that the more commonly-used issue-generic frames obscure important ideological and regional patterns that are only revealed by immigration-specific frames. Furthermore, frames oriented towards human interests, culture, and politics are associated with higher user engagement. This large-scale analysis of a complex social and linguistic phenomenon contributes to both NLP and social science research.

2019

pdf
Using Sentiment Induction to Understand Variation in Gendered Online Communities
Li Lucy | Julia Mendelsohn
Proceedings of the Society for Computation in Linguistics (SCiL) 2019