Best practices for high conflict conversations like counseling or customer support almost always include recommendations to paraphrase the previous speaker.Although paraphrase classification has received widespread attention in NLP, paraphrases are usually considered independent from context, and common models and datasets are not applicable to dialog settings.In this work, we investigate paraphrases across turns in dialog (e.g., Speaker 1: "That book is mine."becomes Speaker 2: "That book is yours.").We provide an operationalization of context-dependent paraphrases, and develop a training for crowd-workers to classify paraphrases in dialog.We introduce ContextDeP, a dataset with utterance pairs from NPR and CNN news interviews annotated for contextdependent paraphrases.To enable analysis on label variation, the dataset contains 5,581 annotations on 600 utterance pairs.We present promising results with in-context learning and with token classification models for automatic paraphrase detection in dialog.What? Shortened Examples Clear Contextual Equivalence ⊆

We consider the task of automatically identifying participants' motivations in the public health campaign Movember and investigate the impact of the different motivations on the amount of campaign donations raised.Our classification scheme is based on the Social Identity Model of Collective Action (van Zomeren et al., 2008).We find that automatic classification based on Movember profiles is fairly accurate, while automatic classification based on tweets is challenging.Using our classifier, we find a strong relation between types of motivations and donations.Our study is a first step towards scaling-up collective action research methods.