The past decade in the US has been one of the most politically polarizing in recent memory. Ordinary Democrats and Republicans fundamentally dislike and distrust each other, even when they agree on policy issues. This increase in hostility towards opposing party supporters, commonly called affective polarization, has important ramifications that threaten democracy. Political science research suggests that at least part of this polarization stems from Democrats' misperceptions about Republicans' political views and vice-versa. Therefore, in this work, drawing on insights from political science and game studies research, we designed an online casual game that combines the relaxed, playful nonpartisan norms of casual games with corrective information about party supporters' political views that are often misperceived. Through an experiment, we found that playing the game significantly reduces negative feelings toward outparty supporters among Democrats, but not Republicans. It was also effective in improving willingness to talk politics with outparty supporters. Further, we identified psychological reactance as a potential mechanism that affects the effectiveness of depolarization interventions. Finally, our analyses suggest that the game versions with political content were rated to be just as fun to play as a game version without any political content suggesting that, contrary to popular belief, people do like to mix politics and play.

NeuroML is an extensible markup language for describing complex mathematical models of neurons and neuronal networks. NeuroML is unique in its modular, multi-scale structure -- not only can entire NeuroML models be exchanged, but subcomponents of these models that correspond to neuroscience objects, like channels or synapses, also can be shared and reimplemented in a different model. This paper presents the design, implementation, and evaluation of an ontology-assisted search for NeuroML models. Specifically, the paper describes the design of the system, including the database that stores the modular NeuroML models and the architecture of the Web-based search (neuroml-db.org). The implementation takes advantage of the nested structure of NeuroML models and the NeuroLex ontology for neuroscience to provide additional semantic information to enhance the search. In addition to NeuroLex terms that may exist in model metadata, this initial implementation takes advantage of several semantic relationships provided by the NeuroLex ontology: Is_part_of, Located_in, and Neurotransmitter. An evaluation of the system illustrates its effectiveness both for functionality and performance, covering various types of searches broken down by keyword searches over the database and ontology searches using the semantic relationships.

Sarcasm is a nuanced form of language in which individuals state the opposite of what is implied. With this intentional ambiguity, sarcasm detection has always been a challenging task, even for humans. Current approaches to automatic sarcasm detection rely primarily on lexical and linguistic cues. This paper aims to address the difficult task of sarcasm detection on Twitter by leveraging behavioral traits intrinsic to users expressing sarcasm. We identify such traits using the user's past tweets. We employ theories from behavioral and psychological studies to construct a behavioral modeling framework tuned for detecting sarcasm. We evaluate our framework and demonstrate its efficiency in identifying sarcastic tweets.