Jordan Carpenter


2017

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Personality Driven Differences in Paraphrase Preference
Daniel Preoţiuc-Pietro | Jordan Carpenter | Lyle Ungar
Proceedings of the Second Workshop on NLP and Computational Social Science

Personality plays a decisive role in how people behave in different scenarios, including online social media. Researchers have used such data to study how personality can be predicted from language use. In this paper, we study phrase choice as a particular stylistic linguistic difference, as opposed to the mostly topical differences identified previously. Building on previous work on demographic preferences, we quantify differences in paraphrase choice from a massive Facebook data set with posts from over 115,000 users. We quantify the predictive power of phrase choice in user profiling and use phrase choice to study psycholinguistic hypotheses. This work is relevant to future applications that aim to personalize text generation to specific personality types.

2016

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Analyzing Biases in Human Perception of User Age and Gender from Text
Lucie Flekova | Jordan Carpenter | Salvatore Giorgi | Lyle Ungar | Daniel Preoţiuc-Pietro
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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An Empirical Exploration of Moral Foundations Theory in Partisan News Sources
Dean Fulgoni | Jordan Carpenter | Lyle Ungar | Daniel Preoţiuc-Pietro
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

News sources frame issues in different ways in order to appeal or control the perception of their readers. We present a large scale study of news articles from partisan sources in the US across a variety of different issues. We first highlight that differences between sides exist by predicting the political leaning of articles of unseen political bias. Framing can be driven by different types of morality that each group values. We emphasize differences in framing of different news building on the moral foundations theory quantified using hand crafted lexicons. Our results show that partisan sources frame political issues differently both in terms of words usage and through the moral foundations they relate to.