Gabriel Doyle


2023

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Not Just Iconic: Emoji Interpretation is Shaped by Use
Brianna O’Boyle | Gabriel Doyle
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

Where do the meaning of emoji come from? Though it is generally assumed that emoji are fully iconic, with meanings derived from their visual forms, we argue that this is only one component of their meaning. We surveyed users and non-users of the Chinese social media platform WeChat for their interpretations of emoji specific to WeChat. We find that some emoji show significant differences in their interpretations between users and non-users, and based on how familiar a person is with the specific emoji. We argue that this reflects a more complex process for building the meaning of emoji on a platform than pure iconicity.

2018

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Alignment, Acceptance, and Rejection of Group Identities in Online Political Discourse
Hagyeong Shin | Gabriel Doyle
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Conversation is a joint social process, with participants cooperating to exchange information. This process is helped along through linguistic alignment: participants’ adoption of each other’s word use. This alignment is robust, appearing many settings, and is nearly always positive. We create an alignment model for examining alignment in Twitter conversations across antagonistic groups. This model finds that some word categories, specifically pronouns used to establish group identity and common ground, are negatively aligned. This negative alignment is observed despite other categories, which are less related to the group dynamics, showing the standard positive alignment. This suggests that alignment is strongly biased toward cooperative alignment, but that different linguistic features can show substantially different behaviors.

2017

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Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations
Gabriel Doyle | Amir Goldberg | Sameer Srivastava | Michael Frank
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person’s word use on another’s and distinguishes between two enculturation mechanisms: internalization and self-regulation. We use this measure to trace employees’ enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals’ downstream outcomes, especially involuntary exit. Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.

2016

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Investigating the Sources of Linguistic Alignment in Conversation
Gabriel Doyle | Michael C. Frank
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Data-driven learning of symbolic constraints for a log-linear model in a phonological setting
Gabriel Doyle | Roger Levy
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.

2015

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Shared common ground influences information density in microblog texts
Gabriel Doyle | Michael Frank
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Audience size and contextual effects on information density in Twitter conversations
Gabriel Doyle | Michael Frank
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

2014

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Mapping Dialectal Variation by Querying Social Media
Gabriel Doyle
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Nonparametric Learning of Phonological Constraints in Optimality Theory
Gabriel Doyle | Klinton Bicknell | Roger Levy
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Combining multiple information types in Bayesian word segmentation
Gabriel Doyle | Roger Levy
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies