This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification

Fanfiction presents an opportunity as a data source for research in NLP, education, and social science. However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction. We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters. The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters. Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes. For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories. This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution

In this work we investigate how role-based behavior profiles of a Wikipedia editor, considered against the backdrop of roles taken up by other editors in discussions, predict the success of the editor at achieving an impact on the associated article. We first contribute a new public dataset including a task predicting the success of Wikipedia editors involved in discussion, measured by an operationalization of the lasting impact of their edits in the article. We then propose a probabilistic graphical model that advances earlier work inducing latent discussion roles using the light supervision of success in the negotiation task. We evaluate the performance of the model and interpret findings of roles and group configurations that lead to certain outcomes on Wikipedia.

Code-switching has been found to have social motivations in addition to syntactic constraints. In this work, we explore the social effect of code-switching in an online community. We present a task from the Arabic Wikipedia to capture language choice, in this case code-switching between Arabic and other languages, as a predictor of social influence in collaborative editing. We find that code-switching is positively associated with Wikipedia editor success, particularly borrowing technical language on pages with topics less directly related to Arabic-speaking regions.

We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.